#4869 Self-Reinforcing Innovation Pipeline
#4869 Self-Reinforcing Innovation Pipeline
See also #4848 https://learningfromchatgpt.blogspot.com/2026/10/4848-innovation-workflow.html
#4869 Once I have implemented a successful AI-assisted pipeline """1) identify opportunities in industry and user groups where innovative solutions are needed or would be beneficial, 2) analyze each opportunity for understanding, 3) recommend N improvements for each opportunity, 4) describe implementation of each recommended improvement""", how can I track the final results, rank them for level of success, then use that information to concentrate on the right areas of industry and user groups (step 1) to get more and more successes?
Closing the loop from Step 4 back to Step 1 transforms an open-ended generative pipeline into a self-reinforcing, adaptive discovery engine. To make this operational, you need three structural elements: a unified trace schema that tags every initiative, an empirical success metric with decay functions, and a reinforcement / bandit-style allocation loop that updates Step 1 sampling probabilities.
1. Data Model & Lineage Tracking
Every opportunity passing through the 4-step pipeline must be logged as an immutable node in a relational or graph structure with end-to-end lineage:
[Industry / Domain / User Group] │ (1. Identifies) ▼ [Opportunity] ── (2. Analyzes) ──> [Root Cause / System Need] │ │ (3. Generates) ▼ [Improvement Concept (1..N)] │ │ (4. Implements) ▼ [Implementation Deployment] ── (Logs) ──> [Outcome Observation]
Core Schema Fields
Taxonomy Tags:
industry_sector,sub_domain,target_persona,system_type(e.g., cyber-physical, enterprise workflow, compliance).Generation Metadata: Hypothesis confidence score, baseline TRIZ/SCAMPER mechanism applied, projected ROI, LLM checkpoint, run timestamp.
Execution Telemetry:
Stage Gate:
idea_stage-->prototyped-->piloted-->deployed-->abandoned.Resource Cost: Engineering hours, capital spent, token inference costs, latency to prototype.
Downstream Ground Truth: Actual adoption rate, cost reduction, error/defect reduction, customer satisfaction/NPS shift, revenue lift.
2. Multi-Horizon Success Scoring (S)
Because innovation outcomes take time to mature, ranking cannot rely purely on immediate post-deployment metrics. Use a tiered composite score evaluated across three horizons:
S = w_1 M_early + w_2 M_intermediate + w_3 M_mature - lambda C
| Horizon | Window | Key Indicators | Weight (w) |
| Early (M_early) | 0–30 Days | Feasibility validation, stakeholder approval, developer velocity, initial prototype pass rate. | 0.20 |
| Intermediate (M_intermediate) | 30–90 Days | Pilot adoption, active daily engagement, initial defect deflection, cycle time compression. | 0.40 |
| Mature (M_mature) | 90–360 Days | Sustained ROI, hard cost savings, system-level safety/reliability improvements, user retention. | 0.40 |
| Cost Penalty (lambda C) | Continuous | Normalized budget overruns, operational friction, regulatory/compliance overhead. | Subtracted |
If an opportunity is abandoned early due to unviable technical barriers discovered during Step 4, assign it a structured negative/failure reward categorized by root cause (e.g., economic infeasibility, regulatory blockade, phantom user demand).
3. Feedback Loop: Conditioning Step 1 on Historical Returns
To concentrate Step 1 on the highest-yielding industries and user groups without falling into local optima, treat opportunity discovery as a hierarchical multi-armed bandit problem.
┌────────────────────────────────────────────────────────┐
│ │
▼ │
┌──────────────┐ ┌──────────────┐ ┌─────────────────┐ │
│ Step 1 │───>│ Steps 2–4 │───>│ Outcome Logging │─────┘
│ Opportunity │ │ Analysis & │ │ & Score Engine │ (Updates Domain
│ Sampling │ │ Execution │ │ (Calc S) │ Weights & Embeddings)
└──────────────┘ └──────────────┘ └─────────────────┘
Mechanism A: Hierarchical Thompson Sampling (Exploration vs. Exploitation)
Assign a Beta distribution Beta(alpha_i, beta_i) or Gaussian prior to each industry sector and user group segment:
alpha_i = 1 + sum( Successful deployments in Domain i )
beta_i = 1 + sum( Stalled or low-scoring initiatives in Domain i )
When Step 1 prompts trigger, sample the expected value from the distribution. High-performing sectors (e.g., Mission Assurance tooling, Robotic chassis sensor retrofits) are sampled frequently (exploitation), while high-uncertainty or nascent sectors maintain non-zero probability to prevent blindness to emerging opportunities (exploration).
Mechanism B: Embedding-Space Vector Conditioning
Convert the top 10% highest-scoring opportunities (S >= tau) into dense embeddings using an embedding model.
Cluster these successes into a "High-Value Vector Space."
In Step 1 prompt generation, inject these high-return centroids as dynamic few-shot exemplars:
"Here are structural attributes of historically top-ranked solutions in related domains: [Vector Matches]. Identify adjacent, unaddressed friction points sharing these structural dynamics in [Target Domain]."
Apply negative exemplar filtering: pass low-scoring centroids to Step 1 as explicit negative constraints (e.g., "Avoid generating opportunities constrained by [Common Failure Pattern X]").
Mechanism C: Knowledge Graph Traversal
In a system graph where nodes represent entities (Domain, Subsystem, ProblemArchetype, SolutionPattern), annotate edges with dynamic weights:
Weight(D --> P) = {sum S_domain, problem} / {N_attempts}
Step 1 queries then run graph traversals biased toward paths of high historical yield, or follow structural analogies (isomorphisms) where a problem archetype that succeeded in Industry A has an unaddressed equivalent in Industry B.
4. Implementation Blueprint: Automated Loop Execution
from pydantic import BaseModel, Field
from typing import List, Optional
import numpy as np
class DomainProfile(BaseModel):
industry: str
user_group: str
alpha: float = 1.0 # Success pseudo-counts
beta: float = 1.0 # Failure/stagnation pseudo-counts
avg_score: float = 0.0
def sample_priority(self) -> float:
# Thompson Sampling draw
return float(np.random.beta(self.alpha, self.beta))
class PipelineFeedbackEngine:
def __init__(self, domains: List[DomainProfile]):
self.domain_registry = {f"{d.industry}:{d.user_group}": d for d in domains}
def allocate_step_1_focus(self, top_k: int = 3) -> List[DomainProfile]:
"""Rank domains dynamically to determine where Step 1 generates prompts."""
ranked = sorted(
self.domain_registry.values(),
key=lambda d: d.sample_priority(),
reverse=True
)
return ranked[:top_k]
def log_trial_result(self, industry: str, user_group: str, score: float, threshold: float = 0.65):
"""Update domain belief distribution based on measured outcome."""
key = f"{industry}:{user_group}"
if key not in self.domain_registry:
self.domain_registry[key] = DomainProfile(industry=industry, user_group=user_group)
domain = self.domain_registry[key]
if score >= threshold:
domain.alpha += score
else:
domain.beta += (1.0 - score)
# Exponential moving average update
domain.avg_score = (0.85 * domain.avg_score) + (0.15 * score)
Immediate Setup Checklist
Deploy the Lineage Table: Add an append-only SQLite or Postgres table storing
opportunity_id,domain_vector,stage_status, andoutcome_metrics.Standardize the Success Rubric: Define objective measurement windows at Day 30 and Day 90 with numeric cutoffs for feasibility, cost reduction, and user adoption.
Automate Step 1 Context Injection: Update the Step 1 prompt generator to dynamically pull the top 3 highest-yield domain profiles and high-scoring exemplars directly from the outcome ledger.
Provide the complete SQLite schema, score calculation queries, and indexing strategy for tracking opportunities and outcomes across industry domains.
An end-to-end SQLite architecture to track innovation opportunities, calculate multi-horizon success scores, and update domain-level bandit distributions for feedback into Step 1 requires a relational model that enforces data integrity, stage gating, and query performance.
1. SQLite Relational Schema (schema.sql)
The schema enforces strict foreign key relationships across the 4-step pipeline: Domain Target --> Opportunity --> Improvement Concepts --> Deployments --> Milestone Evaluations.
PRAGMA foreign_keys = ON;
-- ---------------------------------------------------------------------
-- 1. DOMAIN & USER GROUP REGISTRY (Step 1 Sampling Space)
-- ---------------------------------------------------------------------
CREATE TABLE domains (
domain_id TEXT PRIMARY KEY, -- e.g., 'aerospace_defense:safety_assurance'
industry TEXT NOT NULL, -- e.g., 'Aerospace & Defense'
user_group TEXT NOT NULL, -- e.g., 'Safety & Mission Assurance Engineers'
alpha_prior REAL NOT NULL DEFAULT 1.0, -- Beta prior (success pseudo-counts)
beta_prior REAL NOT NULL DEFAULT 1.0, -- Beta prior (failure/stagnation pseudo-counts)
total_trials INTEGER NOT NULL DEFAULT 0,
rolling_success_avg REAL NOT NULL DEFAULT 0.0,
created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
last_sampled_at TEXT,
UNIQUE(industry, user_group)
);
-- ---------------------------------------------------------------------
-- 2. OPPORTUNITY LOG (Step 1 -> Step 2)
-- ---------------------------------------------------------------------
CREATE TABLE opportunities (
opportunity_id TEXT PRIMARY KEY, -- e.g., 'opp_20261022_001'
domain_id TEXT NOT NULL,
title TEXT NOT NULL,
need_statement TEXT NOT NULL,
root_cause_summary TEXT, -- Step 2 synthesis
source_model TEXT NOT NULL, -- e.g., 'gemini-2.5-pro'
generation_metadata TEXT, -- JSON payload: {triz_operator, confidence, tokens}
pipeline_stage TEXT NOT NULL CHECK(pipeline_stage IN (
'IDENTIFIED', 'ANALYZED', 'IMPROVED',
'PILOTING', 'DEPLOYED', 'ABANDONED'
)) DEFAULT 'IDENTIFIED',
abandon_reason TEXT CHECK(abandon_reason IN (
'TECHNICAL_BLOCKER', 'ECONOMIC_UNVIABLE',
'REGULATORY_COMPLIANCE', 'NO_USER_NEED', NULL
)),
created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (domain_id) REFERENCES domains(domain_id) ON DELETE CASCADE
);
-- ---------------------------------------------------------------------
-- 3. IMPROVEMENT CONCEPTS (Step 3: 1..N per Opportunity)
-- ---------------------------------------------------------------------
CREATE TABLE improvement_concepts (
concept_id TEXT PRIMARY KEY, -- e.g., 'cpt_20261022_001_A'
opportunity_id TEXT NOT NULL,
concept_order INTEGER NOT NULL, -- 1 to N
headline TEXT NOT NULL,
mechanism_type TEXT NOT NULL, -- e.g., 'Automation', 'Elimination', 'Graph-RAG'
projected_roi_ratio REAL, -- Expected Value / Cost
technical_risk_tier INTEGER CHECK(technical_risk_tier BETWEEN 1 AND 5),
selected_for_impl INTEGER NOT NULL DEFAULT 0 CHECK(selected_for_impl IN (0, 1)),
created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (opportunity_id) REFERENCES opportunities(opportunity_id) ON DELETE CASCADE
);
-- ---------------------------------------------------------------------
-- 4. IMPLEMENTATION DEPLOYMENTS (Step 4 Execution Records)
-- ---------------------------------------------------------------------
CREATE TABLE deployments (
deployment_id TEXT PRIMARY KEY, -- e.g., 'dep_20261022_001_A1'
concept_id TEXT NOT NULL,
deployed_version TEXT NOT NULL,
deployment_status TEXT NOT NULL CHECK(deployment_status IN (
'STAGED', 'ACTIVE_PILOT', 'PRODUCTION', 'DECOMMISSIONED'
)) DEFAULT 'STAGED',
cost_budget_usd REAL NOT NULL DEFAULT 0.0,
cost_actual_usd REAL NOT NULL DEFAULT 0.0,
compliance_friction REAL NOT NULL DEFAULT 0.0 CHECK(compliance_friction BETWEEN 0.0 AND 1.0),
launched_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (concept_id) REFERENCES improvement_concepts(concept_id) ON DELETE CASCADE
);
-- ---------------------------------------------------------------------
-- 5. HORIZON OUTCOME OBSERVATIONS (Tracking Ground Truth)
-- ---------------------------------------------------------------------
CREATE TABLE milestone_evaluations (
evaluation_id TEXT PRIMARY KEY,
deployment_id TEXT NOT NULL,
horizon_tier TEXT NOT NULL CHECK(horizon_tier IN ('EARLY_30D', 'INTERMEDIATE_90D', 'MATURE_360D')),
evaluation_date TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
-- Normalized Metric Scores [0.0 - 1.0]
feasibility_score REAL CHECK(feasibility_score BETWEEN 0.0 AND 1.0),
adoption_rate REAL CHECK(adoption_rate BETWEEN 0.0 AND 1.0),
defect_reduction REAL CHECK(defect_reduction BETWEEN 0.0 AND 1.0),
cost_savings_factor REAL CHECK(cost_savings_factor BETWEEN 0.0 AND 1.0),
-- Sub-composite score for this horizon
horizon_subtotal REAL NOT NULL,
FOREIGN KEY (deployment_id) REFERENCES deployments(deployment_id) ON DELETE CASCADE,
UNIQUE(deployment_id, horizon_tier)
);
2. Indexing Strategy
Indexing targets stage-gate filtering, Thompson sampling joins, and lineage rollups.
-- 1. Optimize Pipeline Stage Gating and Status Audits
CREATE INDEX idx_opportunities_stage ON opportunities(pipeline_stage, domain_id);
-- 2. Optimize Domain Joins for Feedback Loop Aggregations
CREATE INDEX idx_opportunities_domain ON opportunities(domain_id);
-- 3. Fast Retrieval of Active Deployments per Concept
CREATE INDEX idx_concepts_opportunity ON improvement_concepts(opportunity_id, selected_for_impl);
CREATE INDEX idx_deployments_concept ON deployments(concept_id, deployment_status);
-- 4. Fast Milestone Lookup for Scoring Engine Aggregation
CREATE INDEX idx_evaluations_deployment_tier ON milestone_evaluations(deployment_id, horizon_tier);
-- 5. Full Pipeline Traversal Index (Covering Index for Multi-Horizon Rollups)
CREATE INDEX idx_evaluations_covering
ON milestone_evaluations(deployment_id, horizon_tier, horizon_subtotal);
3. Score Calculation Views & Queries
The scoring model reflects the multi-horizon equation:
S = 0.20 cdot M_early + 0.40 cdot M_intermediate + 0.40 cdot M_mature - lambda cdot C_penalty
View: Normalized Deployment Composite Scores
CREATE VIEW v_deployment_scores AS
WITH horizon_aggregates AS (
SELECT
deployment_id,
MAX(CASE WHEN horizon_tier = 'EARLY_30D' THEN horizon_subtotal ELSE NULL END) AS m_early,
MAX(CASE WHEN horizon_tier = 'INTERMEDIATE_90D' THEN horizon_subtotal ELSE NULL END) AS m_inter,
MAX(CASE WHEN horizon_tier = 'MATURE_360D' THEN horizon_subtotal ELSE NULL END) AS m_mature
FROM milestone_evaluations
GROUP BY deployment_id
),
cost_penalties AS (
SELECT
deployment_id,
-- Normalized cost overrun penalty + regulatory compliance friction
CASE
WHEN cost_budget_usd > 0 THEN
MAX(0.0, (cost_actual_usd - cost_budget_usd) / cost_budget_usd)
ELSE 0.0
END * 0.15 + (compliance_friction * 0.10) AS penalty
FROM deployments
)
SELECT
d.deployment_id,
d.concept_id,
c.opportunity_id,
o.domain_id,
COALESCE(h.m_early, 0.0) AS score_30d,
COALESCE(h.m_inter, 0.0) AS score_90d,
COALESCE(h.m_mature, 0.0) AS score_360d,
p.penalty,
ROUND(
-- Adaptive weighting if later horizons have not matured yet
CASE
-- All horizons present
WHEN h.m_mature IS NOT NULL AND h.m_inter IS NOT NULL AND h.m_early IS NOT NULL THEN
(0.20 * h.m_early) + (0.40 * h.m_inter) + (0.40 * h.m_mature) - p.penalty
-- Early + Intermediate present
WHEN h.m_inter IS NOT NULL AND h.m_early IS NOT NULL THEN
(0.35 * h.m_early) + (0.65 * h.m_inter) - p.penalty
-- Only Early present
WHEN h.m_early IS NOT NULL THEN
h.m_early - p.penalty
ELSE 0.0
END,
4
) AS final_success_score
FROM deployments d
JOIN improvement_concepts c ON d.concept_id = c.concept_id
JOIN opportunities o ON c.opportunity_id = o.opportunity_id
LEFT JOIN horizon_aggregates h ON d.deployment_id = h.deployment_id
LEFT JOIN cost_penalties p ON d.deployment_id = p.deployment_id;
Query: Rank Highest Yielding Domains
SELECT
d.domain_id,
d.industry,
d.user_group,
COUNT(DISTINCT v.opportunity_id) AS opportunities_tested,
COUNT(v.deployment_id) AS total_deployments,
ROUND(AVG(v.final_success_score), 3) AS mean_composite_score,
ROUND(MAX(v.final_success_score), 3) AS peak_success_score
FROM domains d
JOIN v_deployment_scores v ON d.domain_id = v.domain_id
GROUP BY d.domain_id
ORDER BY mean_composite_score DESC;
4. Bayesian Feedback Loop Update (Thompson Sampling)
When an evaluation settles or an opportunity is marked ABANDONED, execute this transactional update to modify the Beta distribution parameters (alpha, beta) for Step 1 prompt selection:
-- Step 4 -> Step 1 Feedback Transaction
-- Updates Alpha (success) and Beta (failure) counts per domain
BEGIN TRANSACTION;
WITH recent_domain_signals AS (
SELECT
domain_id,
-- Score >= 0.70 counts towards Alpha; Score < 0.70 contributes to Beta
SUM(CASE WHEN final_success_score >= 0.70 THEN final_success_score ELSE 0.0 END) AS delta_alpha,
SUM(CASE WHEN final_success_score < 0.70 THEN (1.0 - final_success_score) ELSE 0.0 END) AS delta_beta,
AVG(final_success_score) AS current_avg
FROM v_deployment_scores
GROUP BY domain_id
)
UPDATE domains
SET
alpha_prior = alpha_prior + COALESCE((SELECT delta_alpha FROM recent_domain_signals WHERE recent_domain_signals.domain_id = domains.domain_id), 0.0),
beta_prior = beta_prior + COALESCE((SELECT delta_beta FROM recent_domain_signals WHERE recent_domain_signals.domain_id = domains.domain_id), 0.0),
total_trials = (SELECT COUNT(*) FROM opportunities WHERE opportunities.domain_id = domains.domain_id),
rolling_success_avg = COALESCE((SELECT current_avg FROM recent_domain_signals WHERE recent_domain_signals.domain_id = domains.domain_id), rolling_success_avg),
last_sampled_at = CURRENT_TIMESTAMP
WHERE domain_id IN (SELECT domain_id FROM recent_domain_signals);
-- Apply fixed Beta penalty for directly abandoned opportunities without deployments
UPDATE domains
SET beta_prior = beta_prior + 1.0
WHERE domain_id IN (
SELECT domain_id FROM opportunities
WHERE pipeline_stage = 'ABANDONED'
AND opportunity_id NOT IN (SELECT opportunity_id FROM v_deployment_scores)
);
COMMIT;
5. Step 1 Sampling Query (Python Driver Hook)
To prioritize target industries in Step 1, compute the Thompson draw using Python's scipy.stats.beta or approximate the Expected Value (mu) with upper exploration bounds (95th percentile):
-- Deterministic approximation of upper confidence bound (UCB1) directly in SQLite:
SELECT
domain_id,
industry,
user_group,
alpha_prior,
beta_prior,
-- Mean of Beta distribution: a / (a + b)
ROUND(alpha_prior / (alpha_prior + beta_prior), 4) AS expected_mean,
-- Exploration variance bonus: sqrt(2 * ln(total_trials) / n)
ROUND(
(alpha_prior / (alpha_prior + beta_prior)) +
SQRT(2.0 * 2.302585 / (alpha_prior + beta_prior + 1.0)),
4
) AS priority_sample_rank
FROM domains
ORDER BY priority_sample_rank DESC
LIMIT 5;Write a self-contained Python script using sqlite3 and Pydantic that initializes this schema, inserts mock pipeline runs, and executes the Thompson Sampling feedback update.
The script below integrates the complete relational schema with typed Pydantic ingestion models, dynamically computes multi-horizon composite deployment success scores (factoring in cost overruns and compliance penalties), and closes the loop with a Bayesian Thompson Sampling update to dynamically rank target domains for Step 1.
"""
closed_loop_feedback_pipeline.py
Self-contained innovation pipeline closed-loop SQLite feedback engine.
Enforces Pydantic models for data ingestion, computes multi-horizon deployment scores,
and runs a Bayesian Thompson Sampling loop to update domain sampling allocations.
"""
import json
import random
import sqlite3
from typing import List, Literal, Optional
from pydantic import BaseModel, Field
# ==============================================================================
# 1. Pydantic Domain Transfer Schemas
# ==============================================================================
class DomainModel(BaseModel):
domain_id: str
industry: str
user_group: str
alpha_prior: float = 1.0
beta_prior: float = 1.0
class OpportunityModel(BaseModel):
opportunity_id: str
domain_id: str
title: str
need_statement: str
root_cause_summary: Optional[str] = None
source_model: str = "gemini-2.5-pro"
generation_metadata: dict = Field(default_factory=dict)
pipeline_stage: Literal[
'IDENTIFIED', 'ANALYZED', 'IMPROVED',
'PILOTING', 'DEPLOYED', 'ABANDONED'
] = 'IDENTIFIED'
abandon_reason: Optional[
Literal['TECHNICAL_BLOCKER', 'ECONOMIC_UNVIABLE', 'REGULATORY_COMPLIANCE', 'NO_USER_NEED']
] = None
class ConceptModel(BaseModel):
concept_id: str
opportunity_id: str
concept_order: int
headline: str
mechanism_type: str
projected_roi_ratio: float
technical_risk_tier: int = Field(ge=1, le=5)
selected_for_impl: bool = False
class DeploymentModel(BaseModel):
deployment_id: str
concept_id: str
deployed_version: str = "1.0.0"
deployment_status: Literal['STAGED', 'ACTIVE_PILOT', 'PRODUCTION', 'DECOMMISSIONED'] = 'STAGED'
cost_budget_usd: float = 0.0
cost_actual_usd: float = 0.0
compliance_friction: float = Field(ge=0.0, le=1.0, default=0.0)
class EvaluationModel(BaseModel):
evaluation_id: str
deployment_id: str
horizon_tier: Literal['EARLY_30D', 'INTERMEDIATE_90D', 'MATURE_360D']
feasibility_score: float = Field(ge=0.0, le=1.0)
adoption_rate: float = Field(ge=0.0, le=1.0)
defect_reduction: float = Field(ge=0.0, le=1.0)
cost_savings_factor: float = Field(ge=0.0, le=1.0)
horizon_subtotal: float = Field(ge=0.0, le=1.0)
# ==============================================================================
# 2. Pipeline Manager & Feedback Engine
# ==============================================================================
class InnovationPipelineDB:
def __init__(self, db_path: str = ":memory:"):
self.conn = sqlite3.connect(db_path)
self.conn.row_factory = sqlite3.Row
self.conn.execute("PRAGMA foreign_keys = ON;")
self._init_tables()
def _init_tables(self):
schema = """
CREATE TABLE IF NOT EXISTS domains (
domain_id TEXT PRIMARY KEY,
industry TEXT NOT NULL,
user_group TEXT NOT NULL,
alpha_prior REAL NOT NULL DEFAULT 1.0,
beta_prior REAL NOT NULL DEFAULT 1.0,
total_trials INTEGER NOT NULL DEFAULT 0,
rolling_success_avg REAL NOT NULL DEFAULT 0.0,
created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
last_sampled_at TEXT,
UNIQUE(industry, user_group)
);
CREATE TABLE IF NOT EXISTS opportunities (
opportunity_id TEXT PRIMARY KEY,
domain_id TEXT NOT NULL,
title TEXT NOT NULL,
need_statement TEXT NOT NULL,
root_cause_summary TEXT,
source_model TEXT NOT NULL,
generation_metadata TEXT,
pipeline_stage TEXT NOT NULL CHECK(pipeline_stage IN (
'IDENTIFIED', 'ANALYZED', 'IMPROVED',
'PILOTING', 'DEPLOYED', 'ABANDONED'
)) DEFAULT 'IDENTIFIED',
abandon_reason TEXT CHECK(abandon_reason IN (
'TECHNICAL_BLOCKER', 'ECONOMIC_UNVIABLE',
'REGULATORY_COMPLIANCE', 'NO_USER_NEED', NULL
)),
created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (domain_id) REFERENCES domains(domain_id) ON DELETE CASCADE
);
CREATE TABLE IF NOT EXISTS improvement_concepts (
concept_id TEXT PRIMARY KEY,
opportunity_id TEXT NOT NULL,
concept_order INTEGER NOT NULL,
headline TEXT NOT NULL,
mechanism_type TEXT NOT NULL,
projected_roi_ratio REAL,
technical_risk_tier INTEGER CHECK(technical_risk_tier BETWEEN 1 AND 5),
selected_for_impl INTEGER NOT NULL DEFAULT 0 CHECK(selected_for_impl IN (0, 1)),
created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (opportunity_id) REFERENCES opportunities(opportunity_id) ON DELETE CASCADE
);
CREATE TABLE IF NOT EXISTS deployments (
deployment_id TEXT PRIMARY KEY,
concept_id TEXT NOT NULL,
deployed_version TEXT NOT NULL,
deployment_status TEXT NOT NULL CHECK(deployment_status IN (
'STAGED', 'ACTIVE_PILOT', 'PRODUCTION', 'DECOMMISSIONED'
)) DEFAULT 'STAGED',
cost_budget_usd REAL NOT NULL DEFAULT 0.0,
cost_actual_usd REAL NOT NULL DEFAULT 0.0,
compliance_friction REAL NOT NULL DEFAULT 0.0 CHECK(compliance_friction BETWEEN 0.0 AND 1.0),
launched_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (concept_id) REFERENCES improvement_concepts(concept_id) ON DELETE CASCADE
);
CREATE TABLE IF NOT EXISTS milestone_evaluations (
evaluation_id TEXT PRIMARY KEY,
deployment_id TEXT NOT NULL,
horizon_tier TEXT NOT NULL CHECK(horizon_tier IN ('EARLY_30D', 'INTERMEDIATE_90D', 'MATURE_360D')),
evaluation_date TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
feasibility_score REAL CHECK(feasibility_score BETWEEN 0.0 AND 1.0),
adoption_rate REAL CHECK(adoption_rate BETWEEN 0.0 AND 1.0),
defect_reduction REAL CHECK(defect_reduction BETWEEN 0.0 AND 1.0),
cost_savings_factor REAL CHECK(cost_savings_factor BETWEEN 0.0 AND 1.0),
horizon_subtotal REAL NOT NULL,
FOREIGN KEY (deployment_id) REFERENCES deployments(deployment_id) ON DELETE CASCADE,
UNIQUE(deployment_id, horizon_tier)
);
DROP VIEW IF EXISTS v_deployment_scores;
CREATE VIEW v_deployment_scores AS
WITH horizon_aggregates AS (
SELECT
deployment_id,
MAX(CASE WHEN horizon_tier = 'EARLY_30D' THEN horizon_subtotal ELSE NULL END) AS m_early,
MAX(CASE WHEN horizon_tier = 'INTERMEDIATE_90D' THEN horizon_subtotal ELSE NULL END) AS m_inter,
MAX(CASE WHEN horizon_tier = 'MATURE_360D' THEN horizon_subtotal ELSE NULL END) AS m_mature
FROM milestone_evaluations
GROUP BY deployment_id
),
cost_penalties AS (
SELECT
deployment_id,
CASE
WHEN cost_budget_usd > 0 THEN
MAX(0.0, (cost_actual_usd - cost_budget_usd) / cost_budget_usd)
ELSE 0.0
END * 0.15 + (compliance_friction * 0.10) AS penalty
FROM deployments
)
SELECT
d.deployment_id,
d.concept_id,
c.opportunity_id,
o.domain_id,
COALESCE(h.m_early, 0.0) AS score_30d,
COALESCE(h.m_inter, 0.0) AS score_90d,
COALESCE(h.m_mature, 0.0) AS score_360d,
p.penalty,
ROUND(
CASE
WHEN h.m_mature IS NOT NULL AND h.m_inter IS NOT NULL AND h.m_early IS NOT NULL THEN
(0.20 * h.m_early) + (0.40 * h.m_inter) + (0.40 * h.m_mature) - p.penalty
WHEN h.m_inter IS NOT NULL AND h.m_early IS NOT NULL THEN
(0.35 * h.m_early) + (0.65 * h.m_inter) - p.penalty
WHEN h.m_early IS NOT NULL THEN
h.m_early - p.penalty
ELSE 0.0
END,
4
) AS final_success_score
FROM deployments d
JOIN improvement_concepts c ON d.concept_id = c.concept_id
JOIN opportunities o ON c.opportunity_id = o.opportunity_id
LEFT JOIN horizon_aggregates h ON d.deployment_id = h.deployment_id
LEFT JOIN cost_penalties p ON d.deployment_id = p.deployment_id;
"""
self.conn.executescript(schema)
self.conn.commit()
def register_domain(self, domain: DomainModel):
sql = """
INSERT INTO domains (domain_id, industry, user_group, alpha_prior, beta_prior)
VALUES (?, ?, ?, ?, ?)
ON CONFLICT(domain_id) DO UPDATE SET
industry = excluded.industry,
user_group = excluded.user_group;
"""
self.conn.execute(sql, (domain.domain_id, domain.industry, domain.user_group, domain.alpha_prior, domain.beta_prior))
self.conn.commit()
def record_opportunity(self, opp: OpportunityModel):
sql = """
INSERT INTO opportunities (
opportunity_id, domain_id, title, need_statement,
root_cause_summary, source_model, generation_metadata,
pipeline_stage, abandon_reason
) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?);
"""
self.conn.execute(sql, (
opp.opportunity_id, opp.domain_id, opp.title, opp.need_statement,
opp.root_cause_summary, opp.source_model, json.dumps(opp.generation_metadata),
opp.pipeline_stage, opp.abandon_reason
))
self.conn.commit()
def record_concept(self, cpt: ConceptModel):
sql = """
INSERT INTO improvement_concepts (
concept_id, opportunity_id, concept_order, headline,
mechanism_type, projected_roi_ratio, technical_risk_tier, selected_for_impl
) VALUES (?, ?, ?, ?, ?, ?, ?, ?);
"""
self.conn.execute(sql, (
cpt.concept_id, cpt.opportunity_id, cpt.concept_order, cpt.headline,
cpt.mechanism_type, cpt.projected_roi_ratio, cpt.technical_risk_tier,
1 if cpt.selected_for_impl else 0
))
self.conn.commit()
def record_deployment(self, dep: DeploymentModel):
sql = """
INSERT INTO deployments (
deployment_id, concept_id, deployed_version, deployment_status,
cost_budget_usd, cost_actual_usd, compliance_friction
) VALUES (?, ?, ?, ?, ?, ?, ?);
"""
self.conn.execute(sql, (
dep.deployment_id, dep.concept_id, dep.deployed_version, dep.deployment_status,
dep.cost_budget_usd, dep.cost_actual_usd, dep.compliance_friction
))
self.conn.commit()
def record_milestone_evaluation(self, evl: EvaluationModel):
sql = """
INSERT INTO milestone_evaluations (
evaluation_id, deployment_id, horizon_tier, feasibility_score,
adoption_rate, defect_reduction, cost_savings_factor, horizon_subtotal
) VALUES (?, ?, ?, ?, ?, ?, ?, ?);
"""
self.conn.execute(sql, (
evl.evaluation_id, evl.deployment_id, evl.horizon_tier, evl.feasibility_score,
evl.adoption_rate, evl.defect_reduction, evl.cost_savings_factor, evl.horizon_subtotal
))
self.conn.commit()
def run_feedback_update(self, success_threshold: float = 0.70):
"""
Executes Bayesian Thompson Sampling update:
Rolls up final deployment success scores and updates alpha/beta counts per domain.
"""
cur = self.conn.cursor()
# 1. Update alpha/beta from completed deployments
score_rows = cur.execute("""
SELECT
domain_id,
SUM(CASE WHEN final_success_score >= ? THEN final_success_score ELSE 0.0 END) AS delta_alpha,
SUM(CASE WHEN final_success_score < ? THEN (1.0 - final_success_score) ELSE 0.0 END) AS delta_beta,
AVG(final_success_score) AS current_avg
FROM v_deployment_scores
GROUP BY domain_id;
""", (success_threshold, success_threshold)).fetchall()
for row in score_rows:
d_id = row['domain_id']
d_alpha = row['delta_alpha'] or 0.0
d_beta = row['delta_beta'] or 0.0
cur_avg = row['current_avg'] or 0.0
cur.execute("""
UPDATE domains
SET alpha_prior = alpha_prior + ?,
beta_prior = beta_prior + ?,
rolling_success_avg = ?,
total_trials = (SELECT COUNT(*) FROM opportunities WHERE domain_id = ?)
WHERE domain_id = ?;
""", (d_alpha, d_beta, cur_avg, d_id, d_id))
# 2. Penalize domains for opportunities directly abandoned before deployment
abandoned_rows = cur.execute("""
SELECT domain_id, COUNT(*) AS count_abandoned
FROM opportunities
WHERE pipeline_stage = 'ABANDONED'
AND opportunity_id NOT IN (SELECT opportunity_id FROM v_deployment_scores)
GROUP BY domain_id;
""").fetchall()
for row in abandoned_rows:
cur.execute("""
UPDATE domains
SET beta_prior = beta_prior + ?,
total_trials = (SELECT COUNT(*) FROM opportunities WHERE domain_id = ?)
WHERE domain_id = ?;
""", (float(row['count_abandoned']), row['domain_id'], row['domain_id']))
self.conn.commit()
def sample_domains_for_step1(self, top_k: int = 3) -> List[dict]:
"""
Executes Thompson Sampling draw from Beta(alpha, beta) for each registered domain.
Returns top_k domains prioritized for Step 1 opportunity identification.
"""
rows = self.conn.execute(
"SELECT domain_id, industry, user_group, alpha_prior, beta_prior, rolling_success_avg FROM domains;"
).fetchall()
sampled_results = []
for r in rows:
sample = random.betavariate(r['alpha_prior'], r['beta_prior'])
sampled_results.append({
"domain_id": r['domain_id'],
"industry": r['industry'],
"user_group": r['user_group'],
"alpha": r['alpha_prior'],
"beta": r['beta_prior'],
"thompson_sample": round(sample, 4),
"rolling_avg": round(r['rolling_success_avg'], 3)
})
sampled_results.sort(key=lambda x: x["thompson_sample"], reverse=True)
return sampled_results[:top_k]
# ==============================================================================
# 3. Execution Verification
# ==============================================================================
if __name__ == "__main__":
db = InnovationPipelineDB(":memory:")
# Register candidate target domains
domains = [
DomainModel(domain_id="aero:mission_assurance", industry="Aerospace", user_group="Safety & Mission Assurance"),
DomainModel(domain_id="robotics:field_agri", industry="Autonomous Robotics", user_group="Field Hardware Engineers"),
DomainModel(domain_id="fintech:reconciliation", industry="Financial Tech", user_group="Compliance Auditors"),
]
for d in domains:
db.register_domain(d)
# Run 1: High-success project in Aerospace Mission Assurance
db.record_opportunity(OpportunityModel(
opportunity_id="opp_aero_01",
domain_id="aero:mission_assurance",
title="Automated Hazard Report Parsing",
need_statement="Manual review of contractor hazard reports causes verification delays.",
root_cause_summary="Unstructured PDF hazard reports require multi-doc cross-referencing.",
pipeline_stage="DEPLOYED"
))
db.record_concept(ConceptModel(
concept_id="cpt_aero_01_a",
opportunity_id="opp_aero_01",
concept_order=1,
headline="Semantic Graph RAG Pipeline for NASA NPR Standards Cross-Verification",
mechanism_type="Graph-RAG",
projected_roi_ratio=4.5,
technical_risk_tier=2,
selected_for_impl=True
))
db.record_deployment(DeploymentModel(
deployment_id="dep_aero_01_a1",
concept_id="cpt_aero_01_a",
deployment_status="PRODUCTION",
cost_budget_usd=15000.0,
cost_actual_usd=14200.0,
compliance_friction=0.05
))
db.record_milestone_evaluation(EvaluationModel(
evaluation_id="evl_aero_30d",
deployment_id="dep_aero_01_a1",
horizon_tier="EARLY_30D",
feasibility_score=0.95,
adoption_rate=0.88,
defect_reduction=0.75,
cost_savings_factor=0.80,
horizon_subtotal=0.845
))
db.record_milestone_evaluation(EvaluationModel(
evaluation_id="evl_aero_90d",
deployment_id="dep_aero_01_a1",
horizon_tier="INTERMEDIATE_90D",
feasibility_score=0.92,
adoption_rate=0.94,
defect_reduction=0.88,
cost_savings_factor=0.89,
horizon_subtotal=0.9075
))
# Run 2: Early Regulatory Abandonment in Fintech
db.record_opportunity(OpportunityModel(
opportunity_id="opp_fin_01",
domain_id="fintech:reconciliation",
title="Autonomous Ledger Anomaly Remediation",
need_statement="Auto-correct discrepancy ledgers without human auditor signoff.",
pipeline_stage="ABANDONED",
abandon_reason="REGULATORY_COMPLIANCE"
))
# Run 3: Deployment with high cost overrun in Robotics
db.record_opportunity(OpportunityModel(
opportunity_id="opp_rob_01",
domain_id="robotics:field_agri",
title="Computer Vision Blade Clogging Detector",
need_statement="Detect debris build-up on chassis undercarriages.",
pipeline_stage="DEPLOYED"
))
db.record_concept(ConceptModel(
concept_id="cpt_rob_01_a",
opportunity_id="opp_rob_01",
concept_order=1,
headline="Ultrasonic Transducer Array Retrofit",
mechanism_type="Acoustic Sensing",
projected_roi_ratio=1.8,
technical_risk_tier=4,
selected_for_impl=True
))
db.record_deployment(DeploymentModel(
deployment_id="dep_rob_01_a1",
concept_id="cpt_rob_01_a",
deployment_status="ACTIVE_PILOT",
cost_budget_usd=8000.0,
cost_actual_usd=12500.0, # 56% budget overrun penalty
compliance_friction=0.20
))
db.record_milestone_evaluation(EvaluationModel(
evaluation_id="evl_rob_30d",
deployment_id="dep_rob_01_a1",
horizon_tier="EARLY_30D",
feasibility_score=0.60,
adoption_rate=0.50,
defect_reduction=0.45,
cost_savings_factor=0.40,
horizon_subtotal=0.4875
))
# Execute scoring audit & Thompson Sampling update
db.run_feedback_update(success_threshold=0.70)
allocations = db.sample_domains_for_step1(top_k=3)
for rank, d in enumerate(allocations, 1):
print(f"#{rank} {d['domain_id']} (Alpha: {d['alpha']:.2f}, Beta: {d['beta']:.2f}) -> Draw: {d['thompson_sample']}")
Key Execution Highlights
SQL View Horizon Blending: The
v_deployment_scoresview dynamically checks which evaluations are present (30D,90D,360D) and adapts the weighting formula accordingly while deducting penalties calculated from budget variances and compliance friction.Dual-Path Bayesian Feedback:
Deployed initiatives: Scores >= 0.70 increment alpha (reward), while scores < 0.70 increment beta (regret).
Abandoned initiatives: Opportunities killed during stages 2 or 3 (e.g., regulatory or technical infeasibility) apply a direct +1.0 penalty to beta.
Thompson Sampling Selection:
sample_domains_for_step1()pulls beta variates for each registered domain profile, naturally shifting sampling probability toward high-yield sectors while maintaining exploration probability for underserved domains.
To wire your Thompson-sampled domain directly into Step 1, the script must query the SQLite database for the top-priority domain, retrieve the highest-scoring historical deployments as few-shot exemplars, assemble a structured prompt, and request a Pydantic-validated list of opportunities via the Google GenAI SDK.
Python Pipeline Integration (step1_prompt_generator.py)
import os
import json
import sqlite3
from typing import List, Optional
from pydantic import BaseModel, Field
from google import genai
from google.genai import types
# ---------------------------------------------------------------------
# 1. Pydantic Models for Structured Output
# ---------------------------------------------------------------------
class IdentifiedOpportunity(BaseModel):
title: str = Field(description="Actionable title for the friction point or innovative opening.")
need_statement: str = Field(description="Precise statement of the operational, safety, or functional deficit.")
root_cause_hypothesis: str = Field(description="First-principles or structural cause of the friction.")
triz_direction: Optional[str] = Field(description="Applicable TRIZ inventive principle or contradiction dynamic.")
estimated_impact_tier: int = Field(ge=1, le=5, description="1 (minor efficiency) to 5 (mission-critical / high ROI).")
class Step1OpportunityManifest(BaseModel):
target_industry: str
target_user_group: str
sampled_domain_id: str
opportunities: List[IdentifiedOpportunity]
# ---------------------------------------------------------------------
# 2. Context Extraction & LLM Pipeline
# ---------------------------------------------------------------------
class Step1DiscoveryAgent:
def __init__(self, db_conn: sqlite3.Connection, model_name: str = "gemini-2.5-pro"):
self.conn = db_conn
self.model_name = model_name
self.client = genai.Client()
def get_top_sampled_domain(self) -> dict:
"""Fetch the current highest Thompson-draw priority domain from SQLite."""
query = """
SELECT domain_id, industry, user_group, alpha_prior, beta_prior, rolling_success_avg
FROM domains
ORDER BY (alpha_prior / (alpha_prior + beta_prior)) DESC
LIMIT 1;
"""
row = self.conn.execute(query).fetchone()
if not row:
raise ValueError("No domains registered in the database.")
return dict(row)
def get_historical_exemplars(self, domain_id: str, limit: int = 2) -> List[dict]:
"""
Extract high-scoring deployments and their root opportunities.
Prefers current domain, falling back to top overall if sparse.
"""
query = """
SELECT
o.title AS opp_title,
o.need_statement,
c.headline AS concept_headline,
c.mechanism_type,
v.final_success_score,
d.industry
FROM v_deployment_scores v
JOIN improvement_concepts c ON v.concept_id = c.concept_id
JOIN opportunities o ON c.opportunity_id = o.opportunity_id
JOIN domains d ON v.domain_id = d.domain_id
WHERE v.final_success_score >= 0.70
ORDER BY (v.domain_id = ?) DESC, v.final_success_score DESC
LIMIT ?;
"""
rows = self.conn.execute(query, (domain_id, limit)).fetchall()
return [dict(r) for r in rows]
def format_exemplars_block(self, exemplars: List[dict]) -> str:
if not exemplars:
return "No previous deployments meet the success threshold for few-shot guidance."
lines = []
for i, ex in enumerate(exemplars, 1):
lines.append(
f"Exemplar {i} (Success Score: {ex['final_success_score']:.2f} | Industry: {ex['industry']}):\n"
f" - Need: {ex['need_statement']}\n"
f" - Proven Mechanism: {ex['mechanism_type']} -> {ex['concept_headline']}"
)
return "\n\n".join(lines)
def run_step_1(self, top_k_opportunities: int = 3) -> Step1OpportunityManifest:
# 1. Thompson Sampling focus retrieval
target_domain = self.get_top_sampled_domain()
domain_id = target_domain["domain_id"]
industry = target_domain["industry"]
user_group = target_domain["user_group"]
# 2. Extract grounded exemplars
exemplars = self.get_historical_exemplars(domain_id, limit=2)
exemplar_context = self.format_exemplars_block(exemplars)
# 3. Construct the prompt
system_instruction = (
"You are a principal systems engineering innovation analyst. Your task is Step 1 of the "
"innovation pipeline: identify concrete, high-leverage opportunities where innovative "
"breakthroughs are needed. Avoid generic productivity or collaboration suggestions; focus on "
"structural friction, physical constraints, compliance bottlenecks, or safety margins."
)
user_prompt = f"""
TARGET DOMAIN:
- Domain ID: {domain_id}
- Industry: {industry}
- Target User Group: {user_group}
- Historical Pipeline Yield: {target_domain['rolling_success_avg']:.2f} avg score
HISTORICAL HIGH-YIELD PATTERNS (EXEMPLARS):
{exemplar_context}
TASK:
Identify exactly {top_k_opportunities} distinct, non-trivial operational or architectural opportunities for the specified user group.
Each opportunity must isolate the underlying root cause and suggest an engineering/inventive principle (e.g., TRIZ, automation, decoupling) to explore.
"""
# 4. Invoke Gemini API with strict structured schema
response = self.client.models.generate_content(
model=self.model_name,
contents=user_prompt,
config=types.GenerateContentConfig(
system_instruction=system_instruction,
response_mime_type="application/json",
response_schema=Step1OpportunityManifest,
temperature=0.4,
),
)
manifest = Step1OpportunityManifest.model_validate_json(response.text)
return manifest
def persist_opportunities(self, manifest: Step1OpportunityManifest, run_prefix: str = "opp_gen"):
"""Save newly identified opportunities directly back into the SQLite database."""
for i, item in enumerate(manifest.opportunities, 1):
opp_id = f"{run_prefix}_{int(self.conn.execute('SELECT COUNT(*) FROM opportunities').fetchone()[0]) + i:04d}"
metadata = {
"triz_direction": item.triz_direction,
"estimated_impact_tier": item.estimated_impact_tier,
"generated_from_domain": manifest.sampled_domain_id
}
self.conn.execute("""
INSERT INTO opportunities (
opportunity_id, domain_id, title, need_statement,
root_cause_summary, source_model, generation_metadata, pipeline_stage
) VALUES (?, ?, ?, ?, ?, ?, ?, 'IDENTIFIED');
""", (
opp_id,
manifest.sampled_domain_id,
item.title,
item.need_statement,
item.root_cause_hypothesis,
self.model_name,
json.dumps(metadata)
))
self.conn.commit()
# ---------------------------------------------------------------------
# 3. Usage Demonstration
# ---------------------------------------------------------------------
if __name__ == "__main__":
# Assumes 'pipeline.db' exists from previous schema execution
conn = sqlite3.connect("pipeline.db")
conn.row_factory = sqlite3.Row
agent = Step1DiscoveryAgent(db_conn=conn, model_name="gemini-2.5-pro")
print("Executing Step 1 Discovery against top-performing domain...")
manifest = agent.run_step_1(top_k_opportunities=2)
print(f"\nDiscovered Opportunities for {manifest.target_industry} ({manifest.target_user_group}):")
for opp in manifest.opportunities:
print(f"\n- Title: {opp.title}")
print(f" Need: {opp.need_statement}")
print(f" Root Cause: {opp.root_cause_hypothesis}")
print(f" Inventive Direction: {opp.triz_direction} (Tier {opp.estimated_impact_tier})")
# Commit discovered opportunities back into SQLite for Step 2 ingestion
agent.persist_opportunities(manifest)
print("\nSuccessfully committed new opportunities into the database with stage 'IDENTIFIED'.")
Step-by-Step Execution Verification
Deterministic Selection:
get_top_sampled_domain()targets the high-yield domain using the Beta posterior mean ({alpha} / {alpha + beta}).Context Conditioning:
get_historical_exemplars()filtersv_deployment_scoreswhere Score >= 0.70. It preferentially loads successes from the same domain first, falling back to top cross-domain solutions to inject structural problem-solving patterns.Structured Ingestion: Setting
response_schema=Step1OpportunityManifestandresponse_mime_type="application/json"guarantees that the output maps directly to SQLite-insertable fields without requiring regex parsing.State Transition:
persist_opportunities()appends the generated batch into theopportunitiestable with stateIDENTIFIED, making them available for Step 2 root-cause decomposition.
Show how to write the Step 2 agent that takes these IDENTIFIED opportunities, runs root-cause decomposition, and advances their stage to ANALYZED.
Your Python script is ready
step2_decomposition_agent
PY
"""
Step 2 Root-Cause Decomposition Agent:
Polls unanalyzed ('IDENTIFIED') opportunities from SQLite, decomposes the underlying
functional bottlenecks and technical/physical contradictions via the Google GenAI SDK,
persists the structured root-cause analysis, and promotes the record stage to 'ANALYZED'
(or 'ABANDONED' if fundamentally unviable).
"""
import json
import sqlite3
from typing import List, Optional, Literal
from pydantic import BaseModel, Field
from google import genai
from google.genai import types
# ---------------------------------------------------------------------
# 1. Pydantic Schemas for Root-Cause Decomposition
# ---------------------------------------------------------------------
class FunctionalDeficit(BaseModel):
component_or_process: str = Field(
description="The specific subsystem, operational step, or software component experiencing the bottleneck."
)
failure_mode: str = Field(
description="The direct failure mode or degraded functional behavior (e.g., latency spike, semantic hallucination, fatigue wear)."
)
causal_mechanism: str = Field(
description="The physical, logical, or architectural mechanism triggering the failure mode."
)
class RootCauseAnalysisResult(BaseModel):
opportunity_id: str = Field(description="Foreign key matching the opportunity being analyzed.")
underlying_system_constraint: str = Field(
description="The foundational structural, physical, mathematical, or regulatory boundary condition causing the problem."
)
functional_deficits: List[FunctionalDeficit] = Field(
description="Breakdown of specific component-level deficits contributing to the root cause."
)
technical_contradiction: str = Field(
description="The core engineering tradeoff (e.g., 'Improving X degrades Y')."
)
physical_contradiction: Optional[str] = Field(
default=None,
description="Underlying parameter that must simultaneously be state A and state NOT-A (e.g., 'Filter must be dense to capture particles but open to avoid flow restriction')."
)
synthesis_summary: str = Field(
description="High-density engineering summary suitable for Step 3 concept generation (populates root_cause_summary)."
)
recommended_stage: Literal['ANALYZED', 'ABANDONED'] = Field(
description="Advance to 'ANALYZED' if viable; flag 'ABANDONED' if blocked by unresolvable physics, law, or economics."
)
abandon_reason: Optional[Literal[
'TECHNICAL_BLOCKER', 'ECONOMIC_UNVIABLE', 'REGULATORY_COMPLIANCE', 'NO_USER_NEED'
]] = Field(
default=None,
description="Mandatory if recommended_stage is 'ABANDONED'."
)
# ---------------------------------------------------------------------
# 2. Step 2 Root-Cause Decomposition Agent
# ---------------------------------------------------------------------
class Step2DecompositionAgent:
def __init__(self, db_conn: sqlite3.Connection, model_name: str = "gemini-2.5-pro"):
self.conn = db_conn
self.model_name = model_name
self.client = genai.Client()
def fetch_unprocessed_opportunities(self, limit: int = 5) -> List[dict]:
"""Query opportunities currently in 'IDENTIFIED' stage."""
query = """
SELECT
o.opportunity_id,
o.domain_id,
o.title,
o.need_statement,
o.generation_metadata,
d.industry,
d.user_group
FROM opportunities o
JOIN domains d ON o.domain_id = d.domain_id
WHERE o.pipeline_stage = 'IDENTIFIED'
ORDER BY o.created_at ASC
LIMIT ?;
"""
rows = self.conn.execute(query, (limit,)).fetchall()
return [dict(r) for r in rows]
def decompose_opportunity(self, opp: dict) -> RootCauseAnalysisResult:
"""Analyze root causes and contradictions using structured generation."""
meta = json.loads(opp.get("generation_metadata") or "{}")
system_instruction = (
"You are a Principal Systems Architect and Root Cause Analysis Lead using first-principles physics, "
"TRIZ contradiction modeling, and functional failure mode decomposition. Your task is Step 2 of the "
"innovation pipeline: analyze the submitted opportunity to isolate the fundamental physical, logical, "
"or architectural root cause. Do NOT propose solutions yet (Step 3). Determine whether this opportunity "
"is technically viable to advance to 'ANALYZED' or if it violates insurmountable laws/regulations ('ABANDONED')."
)
user_prompt = f"""
OPPORTUNITY UNDER INVESTIGATION:
- Opportunity ID: {opp['opportunity_id']}
- Target Industry: {opp['industry']}
- Target User Group: {opp['user_group']}
- Title: {opp['title']}
- Need Statement: {opp['need_statement']}
- Initial Inventive Clues: {json.dumps(meta)}
EXECUTE STEP 2 ANALYSIS:
1. Identify the fundamental system constraint causing this friction.
2. Break down the failure mechanisms into component-level functional deficits.
3. Formulate the core Technical Contradiction (improving parameter vs. worsening parameter).
4. Formulate the core Physical Contradiction (the opposite physical states demanded of the same parameter).
5. Produce a concise, rigorous synthesis summary for the engineering team.
6. Make a stage-gate determination: 'ANALYZED' (viable) or 'ABANDONED' (impossible).
"""
response = self.client.models.generate_content(
model=self.model_name,
contents=user_prompt,
config=types.GenerateContentConfig(
system_instruction=system_instruction,
response_mime_type="application/json",
response_schema=RootCauseAnalysisResult,
temperature=0.2, # Low temperature for deterministic causal analysis
),
)
return RootCauseAnalysisResult.model_validate_json(response.text)
def commit_decomposition(self, result: RootCauseAnalysisResult):
"""Persist structured analysis to SQLite and advance pipeline stage."""
metadata_patch = {
"root_cause_decomposition": {
"underlying_system_constraint": result.underlying_system_constraint,
"functional_deficits": [f.model_dump() for f in result.functional_deficits],
"technical_contradiction": result.technical_contradiction,
"physical_contradiction": result.physical_contradiction
}
}
query = """
UPDATE opportunities
SET
root_cause_summary = ?,
pipeline_stage = ?,
abandon_reason = ?,
generation_metadata = json_patch(COALESCE(generation_metadata, '{}'), ?)
WHERE opportunity_id = ?;
"""
self.conn.execute(query, (
result.synthesis_summary,
result.recommended_stage,
result.abandon_reason,
json.dumps(metadata_patch),
result.opportunity_id
))
self.conn.commit()
def run_batch(self, batch_size: int = 5) -> int:
"""Process a batch of unanalyzed opportunities."""
unprocessed = self.fetch_unprocessed_opportunities(limit=batch_size)
if not unprocessed:
print("No pending opportunities in 'IDENTIFIED' stage.")
return 0
print(f"Processing {len(unprocessed)} identified opportunities in Step 2...")
for opp in unprocessed:
print(f" -> Decomposing: [{opp['opportunity_id']}] {opp['title']}")
analysis = self.decompose_opportunity(opp)
self.commit_decomposition(analysis)
print(f" Status updated: {analysis.recommended_stage} (Abandon Reason: {analysis.abandon_reason})")
return len(unprocessed)
# ---------------------------------------------------------------------
# 3. Usage Demonstration
# ---------------------------------------------------------------------
if __name__ == "__main__":
conn = sqlite3.connect("pipeline.db")
conn.row_factory = sqlite3.Row
agent = Step2DecompositionAgent(db_conn=conn, model_name="gemini-2.5-pro")
processed_count = agent.run_batch(batch_size=5)
print(f"Step 2 batch complete. Total opportunities processed: {processed_count}")
Mechanical Workflow
Transactional Stage Progression: The agent extracts rows marked
IDENTIFIED, queries Gemini with a low temperature (0.2), and transitions them toANALYZED.First-Class Contradiction Modeling: The decomposition isolates the TRIZ technical contradiction (P_improve <----> P_worsen) and physical contradiction (X <----> -X), which directly primes Step 3 to select inventive operators (e.g., Segmentation, Equipotentiality, Self-Service).
Automated Triage / Kill Switch: If an opportunity violates regulatory constraints or fundamental physics, the agent marks
recommended_stage='ABANDONED'with an explicitabandon_reason. This immediately feeds back into the SQLite Thompson Sampling loop to increase the domain's beta penalty.JSON Patch State Merging: Uses SQLite's native
json_patch()to append decomposition details intogeneration_metadatawithout overwriting the Step 1 telemetry.
Show how to write the Step 3 agent that reads ANALYZED opportunities, applies TRIZ contradiction matrix operators, and produces N ranked ImprovementConcept records in SQLite.
Your Python script is ready
step3_concept_generator
PY
"""
Step 3 Concept Generation Agent:
Reads opportunities in the 'ANALYZED' stage from SQLite, extracts the decomposed root causes
and TRIZ contradictions, prompts Google GenAI to synthesize N distinct improvement concepts
using specific TRIZ inventive operators, ranks them by projected ROI ratio and feasibility,
and commits them into the 'improvement_concepts' table while advancing the opportunity to 'IMPROVED'.
"""
import json
import sqlite3
from typing import List, Literal, Optional
from pydantic import BaseModel, Field
from google import genai
from google.genai import types
# ---------------------------------------------------------------------
# 1. Pydantic Schemas for Concept Generation
# ---------------------------------------------------------------------
class ImprovementConceptCandidate(BaseModel):
concept_order: int = Field(
description="Ranking order of the concept (1 to N, where 1 is highest priority/best ROI)."
)
headline: str = Field(
description="Concise, high-impact architectural or mechanism title."
)
triz_operator_applied: str = Field(
description="Specific TRIZ principle applied (e.g., 'Principle 1: Segmentation', 'Principle 10: Prior Action', 'Principle 24: Intermediary')."
)
mechanism_type: str = Field(
description="Core technical classification (e.g., 'Deterministic Graph-RAG', 'Static Pre-computation', 'Hardware Decoupling')."
)
mechanism_description: str = Field(
description="Technical breakdown of how the concept resolves the technical/physical contradiction without worsening the tradeoff."
)
projected_roi_ratio: float = Field(
ge=0.1,
description="Projected Benefit / Cost ratio (e.g., 4.2 represents a 420% return on engineering investment)."
)
technical_risk_tier: int = Field(
ge=1, le=5,
description="Technical risk rating from 1 (low risk, proven patterns) to 5 (high R&D complexity)."
)
implementation_complexity: Literal['LOW', 'MEDIUM', 'HIGH'] = Field(
description="Estimated engineering effort and deployment friction."
)
selected_for_impl: bool = Field(
default=False,
description="True for the top-priority concept (rank 1) recommended for immediate Stage 4 prototyping."
)
class Step3ConceptManifest(BaseModel):
opportunity_id: str = Field(description="Foreign key matching the analyzed opportunity.")
concepts: List[ImprovementConceptCandidate] = Field(
description="List of exactly N distinct improvement concepts ranked by feasibility and projected ROI."
)
# ---------------------------------------------------------------------
# 2. Step 3 Concept Generation Agent
# ---------------------------------------------------------------------
class Step3ConceptGenerationAgent:
def __init__(self, db_conn: sqlite3.Connection, model_name: str = "gemini-2.5-pro"):
self.conn = db_conn
self.model_name = model_name
self.client = genai.Client()
def fetch_analyzed_opportunities(self, limit: int = 5) -> List[dict]:
"""Fetch opportunities in 'ANALYZED' stage ready for concept generation."""
query = """
SELECT
o.opportunity_id,
o.domain_id,
o.title,
o.need_statement,
o.root_cause_summary,
o.generation_metadata,
d.industry,
d.user_group
FROM opportunities o
JOIN domains d ON o.domain_id = d.domain_id
WHERE o.pipeline_stage = 'ANALYZED'
ORDER BY o.created_at ASC
LIMIT ?;
"""
rows = self.conn.execute(query, (limit,)).fetchall()
return [dict(r) for r in rows]
def generate_concepts(self, opp: dict, n_concepts: int = 3) -> Step3ConceptManifest:
"""Prompt Gemini to synthesize N improvement concepts using TRIZ contradiction operators."""
meta = json.loads(opp.get("generation_metadata") or "{}")
decomp = meta.get("root_cause_decomposition", {})
system_instruction = (
"You are a Principal Innovation Architect and TRIZ Master Engineer. Your role in Step 3 of the "
"innovation pipeline is to synthesize N distinct, non-trivial engineering improvement concepts that "
"systematically resolve the formulated technical and physical contradictions. Do NOT propose generic "
"process band-aids; apply specific TRIZ inventive principles (e.g., Segmentation, Prior Action, "
"Asymmetry, Dynamicity, Intermediary, Self-Service, Parameter Changes) to decouple the conflict. "
"Rank the generated concepts descending by feasibility and projected ROI."
)
user_prompt = f"""
ANALYZED OPPORTUNITY FOR CONCEPT SYNTHESIS:
- Opportunity ID: {opp['opportunity_id']}
- Target Industry: {opp['industry']}
- Target User Group: {opp['user_group']}
- Title: {opp['title']}
- Need Statement: {opp['need_statement']}
- Root Cause Summary: {opp.get('root_cause_summary') or 'N/A'}
DECOMPOSED CONTRADICTION CONTEXT:
- Underlying Constraint: {decomp.get('underlying_system_constraint', 'N/A')}
- Technical Contradiction: {decomp.get('technical_contradiction', 'N/A')}
- Physical Contradiction: {decomp.get('physical_contradiction', 'N/A')}
TASK:
1. Generate exactly {n_concepts} distinct, structurally diverse improvement concepts resolving these contradictions.
2. For each concept, specify the primary TRIZ principle utilized, the mechanism type, and a detailed engineering breakdown.
3. Quantify the projected ROI ratio, technical risk tier (1-5), and implementation complexity (LOW, MEDIUM, HIGH).
4. Rank the concepts strictly from 1 to {n_concepts}. Mark concept_order = 1 with selected_for_impl = True.
"""
response = self.client.models.generate_content(
model=self.model_name,
contents=user_prompt,
config=types.GenerateContentConfig(
system_instruction=system_instruction,
response_mime_type="application/json",
response_schema=Step3ConceptManifest,
temperature=0.35, # Moderate temperature for creative architectural divergence within rigorous bounds
),
)
return Step3ConceptManifest.model_validate_json(response.text)
def commit_concepts(self, manifest: Step3ConceptManifest):
"""Persist generated concepts to SQLite and advance the opportunity stage to 'IMPROVED'."""
cur = self.conn.cursor()
# Insert each concept record
for cpt in manifest.concepts:
concept_pk = f"cpt_{manifest.opportunity_id}_{cpt.concept_order}"
cur.execute("""
INSERT INTO improvement_concepts (
concept_id, opportunity_id, concept_order, headline,
mechanism_type, projected_roi_ratio, technical_risk_tier, selected_for_impl
) VALUES (?, ?, ?, ?, ?, ?, ?, ?)
ON CONFLICT(concept_id) DO UPDATE SET
headline = excluded.headline,
mechanism_type = excluded.mechanism_type,
projected_roi_ratio = excluded.projected_roi_ratio,
technical_risk_tier = excluded.technical_risk_tier,
selected_for_impl = excluded.selected_for_impl;
""", (
concept_pk,
manifest.opportunity_id,
cpt.concept_order,
cpt.headline,
cpt.mechanism_type,
cpt.projected_roi_ratio,
cpt.technical_risk_tier,
1 if cpt.selected_for_impl else 0
))
# Update metadata with the full concept descriptions and advance stage
concepts_dump = [c.model_dump() for c in manifest.concepts]
metadata_patch = {"step_3_generated_concepts": concepts_dump}
cur.execute("""
UPDATE opportunities
SET pipeline_stage = 'IMPROVED',
generation_metadata = json_patch(COALESCE(generation_metadata, '{}'), ?)
WHERE opportunity_id = ?;
""", (json.dumps(metadata_patch), manifest.opportunity_id))
self.conn.commit()
def run_batch(self, batch_size: int = 5, n_concepts_per_opp: int = 3) -> int:
"""Process a batch of analyzed opportunities and generate N improvements for each."""
unprocessed = self.fetch_analyzed_opportunities(limit=batch_size)
if not unprocessed:
print("No opportunities currently in 'ANALYZED' stage.")
return 0
print(f"Synthesizing improvements for {len(unprocessed)} opportunities...")
for opp in unprocessed:
print(f" -> Generating {n_concepts_per_opp} concepts for: [{opp['opportunity_id']}] {opp['title']}")
manifest = self.generate_concepts(opp, n_concepts=n_concepts_per_opp)
self.commit_concepts(manifest)
print(f" Committed {len(manifest.concepts)} concepts. Stage advanced to 'IMPROVED'.")
return len(unprocessed)
# ---------------------------------------------------------------------
# 3. Usage Demonstration
# ---------------------------------------------------------------------
if __name__ == "__main__":
conn = sqlite3.connect("pipeline.db")
conn.row_factory = sqlite3.Row
agent = Step3ConceptGenerationAgent(db_conn=conn, model_name="gemini-2.5-pro")
processed_count = agent.run_batch(batch_size=5, n_concepts_per_opp=3)
print(f"Step 3 batch completed. Total opportunities promoted to 'IMPROVED': {processed_count}")
Pipeline Mechanics & TRIZ Integration
Direct Contradiction Mapping: The agent pulls
underlying_system_constraint,technical_contradiction, andphysical_contradictionstraight fromgeneration_metadata['root_cause_decomposition'](populated by Step 2), ensuring the model solves the structural tradeoff rather than proposing superficial process fixes. Ranked Concept Allocation:
concept_order(1 to N) strictly orders the concepts. The top concept automatically receivesselected_for_impl = True, which feeds directly into Stage 4 deployment staging. Relational Normalization: Each concept is persisted as a distinct record in
improvement_concepts(with primary keycpt_{opportunity_id}_{order}) to enable subsequent tracking inv_deployment_scores. Atomic Stage Promotion: Upon committing the N concepts, the opportunity state is updated from
ANALYZEDtoIMPROVED, making it available for the Step 4 deployment planning module.
Would you like to complete the four-step pipeline by building the Step 4 deployment specification agent that converts the selected concepts into executable prototyping sprints and deployment records?
Provide a unified pipeline runner script that executes Step 1 through Step 4 sequentially with a single command, then triggers the Thompson Sampling update.
Your Python script is ready
run_pipeline
PY
This unified master runner (run_pipeline.py) coordinates the entire lifecycle from end to end:
Step 1: Discover Opportunity ──> Step 2: Root Cause & Contradictions (TRIZ) ▲ │ │ ▼ Thompson Sampling Feedback <── Step 4: Prototyping <── Step 3: N-Concept (Updates Beta Priors) Deployment & Metrics Improvement Synthesis
Master Orchestrator Code (run_pipeline.py)
"""
Unified Innovation Pipeline Orchestrator (`run_pipeline.py`):
Executes Steps 1 through 4 sequentially, logs outcomes, and closes the loop via Thompson Sampling:
- Step 1: Identifies high-leverage opportunities in top Thompson-sampled domain.
- Step 2: Decomposes root causes, functional deficits, and TRIZ contradictions (IDENTIFIED -> ANALYZED).
- Step 3: Generates N ranked improvement concepts utilizing TRIZ operators (ANALYZED -> IMPROVED).
- Step 4: Compiles a concrete deployment specification for selected concepts (IMPROVED -> DEPLOYED).
- Feedback Loop: Evaluates initial telemetry and updates the Beta distribution priors in SQLite.
"""
import os
import sys
import json
import sqlite3
import random
import argparse
from typing import List, Optional, Literal
from pydantic import BaseModel, Field
from google import genai
from google.genai import types
# =====================================================================
# 1. PYDANTIC CONTRACTS ACROSS ALL 4 STEPS
# =====================================================================
# --- Step 1 Models ---
class IdentifiedOpportunity(BaseModel):
title: str = Field(description="Actionable title for the friction point or innovative opening.")
need_statement: str = Field(description="Precise statement of the operational, safety, or functional deficit.")
root_cause_hypothesis: str = Field(description="First-principles or structural cause of the friction.")
triz_direction: Optional[str] = Field(description="Applicable TRIZ inventive principle or contradiction dynamic.")
estimated_impact_tier: int = Field(ge=1, le=5, description="1 (minor efficiency) to 5 (mission-critical).")
class Step1OpportunityManifest(BaseModel):
target_industry: str
target_user_group: str
sampled_domain_id: str
opportunities: List[IdentifiedOpportunity]
# --- Step 2 Models ---
class FunctionalDeficit(BaseModel):
component_or_process: str
failure_mode: str
causal_mechanism: str
class RootCauseAnalysisResult(BaseModel):
opportunity_id: str
underlying_system_constraint: str
functional_deficits: List[FunctionalDeficit]
technical_contradiction: str
physical_contradiction: Optional[str] = None
synthesis_summary: str
recommended_stage: Literal['ANALYZED', 'ABANDONED']
abandon_reason: Optional[Literal['TECHNICAL_BLOCKER', 'ECONOMIC_UNVIABLE', 'REGULATORY_COMPLIANCE', 'NO_USER_NEED']] = None
# --- Step 3 Models ---
class ImprovementConceptCandidate(BaseModel):
concept_order: int
headline: str
triz_operator_applied: str
mechanism_type: str
mechanism_description: str
projected_roi_ratio: float
technical_risk_tier: int = Field(ge=1, le=5)
implementation_complexity: Literal['LOW', 'MEDIUM', 'HIGH']
selected_for_impl: bool = False
class Step3ConceptManifest(BaseModel):
opportunity_id: str
concepts: List[ImprovementConceptCandidate]
# --- Step 4 Models ---
class DeploymentTask(BaseModel):
phase: Literal['PHASE_1_ENVIRONMENT_VALIDATION', 'PHASE_2_CORE_IMPLEMENTATION', 'PHASE_3_TELEMETRY_GATING']
description: str
verification_command_or_metric: str
completion_criteria: str
class DeploymentSpecification(BaseModel):
concept_id: str
deployed_version: str
cost_budget_usd: float
estimated_actual_usd: float
compliance_friction_score: float = Field(ge=0.0, le=1.0)
prototype_tasks: List[DeploymentTask]
expected_30d_adoption_rate: float = Field(ge=0.0, le=1.0)
expected_30d_defect_reduction: float = Field(ge=0.0, le=1.0)
# =====================================================================
# 2. MASTER PIPELINE ORCHESTRATOR
# =====================================================================
class InnovationPipelineOrchestrator:
def __init__(self, db_path: str = "pipeline.db", model_name: str = "gemini-2.5-pro"):
self.db_path = db_path
self.model_name = model_name
self.conn = sqlite3.connect(self.db_path)
self.conn.row_factory = sqlite3.Row
self.conn.execute("PRAGMA foreign_keys = ON;")
self.client = genai.Client()
self._bootstrap_schema()
def _bootstrap_schema(self):
"""Ensure all required tables, views, and baseline domains exist."""
cursor = self.conn.cursor()
cursor.executescript("""
CREATE TABLE IF NOT EXISTS domains (
domain_id TEXT PRIMARY KEY,
industry TEXT NOT NULL,
user_group TEXT NOT NULL,
alpha_prior REAL NOT NULL DEFAULT 1.0,
beta_prior REAL NOT NULL DEFAULT 1.0,
total_trials INTEGER NOT NULL DEFAULT 0,
rolling_success_avg REAL NOT NULL DEFAULT 0.0,
created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
last_sampled_at TEXT,
UNIQUE(industry, user_group)
);
CREATE TABLE IF NOT EXISTS opportunities (
opportunity_id TEXT PRIMARY KEY,
domain_id TEXT NOT NULL,
title TEXT NOT NULL,
need_statement TEXT NOT NULL,
root_cause_summary TEXT,
source_model TEXT NOT NULL,
generation_metadata TEXT,
pipeline_stage TEXT NOT NULL CHECK(pipeline_stage IN (
'IDENTIFIED', 'ANALYZED', 'IMPROVED',
'PILOTING', 'DEPLOYED', 'ABANDONED'
)) DEFAULT 'IDENTIFIED',
abandon_reason TEXT CHECK(abandon_reason IN (
'TECHNICAL_BLOCKER', 'ECONOMIC_UNVIABLE',
'REGULATORY_COMPLIANCE', 'NO_USER_NEED', NULL
)),
created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (domain_id) REFERENCES domains(domain_id) ON DELETE CASCADE
);
CREATE TABLE IF NOT EXISTS improvement_concepts (
concept_id TEXT PRIMARY KEY,
opportunity_id TEXT NOT NULL,
concept_order INTEGER NOT NULL,
headline TEXT NOT NULL,
mechanism_type TEXT NOT NULL,
projected_roi_ratio REAL,
technical_risk_tier INTEGER CHECK(technical_risk_tier BETWEEN 1 AND 5),
selected_for_impl INTEGER NOT NULL DEFAULT 0 CHECK(selected_for_impl IN (0, 1)),
created_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (opportunity_id) REFERENCES opportunities(opportunity_id) ON DELETE CASCADE
);
CREATE TABLE IF NOT EXISTS deployments (
deployment_id TEXT PRIMARY KEY,
concept_id TEXT NOT NULL,
deployed_version TEXT NOT NULL,
deployment_status TEXT NOT NULL CHECK(deployment_status IN (
'STAGED', 'ACTIVE_PILOT', 'PRODUCTION', 'DECOMMISSIONED'
)) DEFAULT 'STAGED',
cost_budget_usd REAL NOT NULL DEFAULT 0.0,
cost_actual_usd REAL NOT NULL DEFAULT 0.0,
compliance_friction REAL NOT NULL DEFAULT 0.0 CHECK(compliance_friction BETWEEN 0.0 AND 1.0),
launched_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (concept_id) REFERENCES improvement_concepts(concept_id) ON DELETE CASCADE
);
CREATE TABLE IF NOT EXISTS milestone_evaluations (
evaluation_id TEXT PRIMARY KEY,
deployment_id TEXT NOT NULL,
horizon_tier TEXT NOT NULL CHECK(horizon_tier IN ('EARLY_30D', 'INTERMEDIATE_90D', 'MATURE_360D')),
evaluation_date TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
feasibility_score REAL CHECK(feasibility_score BETWEEN 0.0 AND 1.0),
adoption_rate REAL CHECK(adoption_rate BETWEEN 0.0 AND 1.0),
defect_reduction REAL CHECK(defect_reduction BETWEEN 0.0 AND 1.0),
cost_savings_factor REAL CHECK(cost_savings_factor BETWEEN 0.0 AND 1.0),
horizon_subtotal REAL NOT NULL,
FOREIGN KEY (deployment_id) REFERENCES deployments(deployment_id) ON DELETE CASCADE,
UNIQUE(deployment_id, horizon_tier)
);
CREATE INDEX IF NOT EXISTS idx_opps_stage ON opportunities(pipeline_stage, domain_id);
CREATE INDEX IF NOT EXISTS idx_concepts_selected ON improvement_concepts(opportunity_id, selected_for_impl);
CREATE INDEX IF NOT EXISTS idx_deployments_concept ON deployments(concept_id);
DROP VIEW IF EXISTS v_deployment_scores;
CREATE VIEW v_deployment_scores AS
WITH horizon_aggregates AS (
SELECT
deployment_id,
MAX(CASE WHEN horizon_tier = 'EARLY_30D' THEN horizon_subtotal ELSE NULL END) AS m_early,
MAX(CASE WHEN horizon_tier = 'INTERMEDIATE_90D' THEN horizon_subtotal ELSE NULL END) AS m_inter,
MAX(CASE WHEN horizon_tier = 'MATURE_360D' THEN horizon_subtotal ELSE NULL END) AS m_mature
FROM milestone_evaluations
GROUP BY deployment_id
),
cost_penalties AS (
SELECT
deployment_id,
CASE
WHEN cost_budget_usd > 0 THEN
MAX(0.0, (cost_actual_usd - cost_budget_usd) / cost_budget_usd)
ELSE 0.0
END * 0.15 + (compliance_friction * 0.10) AS penalty
FROM deployments
)
SELECT
d.deployment_id,
d.concept_id,
c.opportunity_id,
o.domain_id,
COALESCE(h.m_early, 0.0) AS score_30d,
COALESCE(h.m_inter, 0.0) AS score_90d,
COALESCE(h.m_mature, 0.0) AS score_360d,
p.penalty,
ROUND(
CASE
WHEN h.m_mature IS NOT NULL AND h.m_inter IS NOT NULL AND h.m_early IS NOT NULL THEN
(0.20 * h.m_early) + (0.40 * h.m_inter) + (0.40 * h.m_mature) - p.penalty
WHEN h.m_inter IS NOT NULL AND h.m_early IS NOT NULL THEN
(0.35 * h.m_early) + (0.65 * h.m_inter) - p.penalty
WHEN h.m_early IS NOT NULL THEN
h.m_early - p.penalty
ELSE 0.0
END,
4
) AS final_success_score
FROM deployments d
JOIN improvement_concepts c ON d.concept_id = c.concept_id
JOIN opportunities o ON c.opportunity_id = o.opportunity_id
LEFT JOIN horizon_aggregates h ON d.deployment_id = h.deployment_id
LEFT JOIN cost_penalties p ON d.deployment_id = p.deployment_id;
""")
# Seed initial domain space if empty
cursor.execute("SELECT COUNT(*) FROM domains;")
if cursor.fetchone()[0] == 0:
cursor.executescript("""
INSERT INTO domains (domain_id, industry, user_group, alpha_prior, beta_prior) VALUES
('aero:mission_assurance', 'Aerospace & Defense', 'Safety & Mission Assurance Engineers', 2.0, 1.0),
('energy:grid_operations', 'Electric Utilities', 'Substation Relay Operators', 1.0, 1.0),
('robotics:field_agri', 'Field Robotics', 'Autonomous Chassis Engineers', 1.0, 1.0);
""")
self.conn.commit()
# --- Step 1: Opportunity Discovery ---
def execute_step_1(self, top_k_opps: int = 1) -> List[str]:
print("\n================== STEP 1: OPPORTUNITY DISCOVERY ==================")
# Thompson sampling selection
rows = self.conn.execute("SELECT domain_id, industry, user_group, alpha_prior, beta_prior FROM domains;").fetchall()
sampled = []
for r in rows:
draw = random.betavariate(r['alpha_prior'], r['beta_prior'])
sampled.append((draw, dict(r)))
sampled.sort(key=lambda x: x[0], reverse=True)
top_domain = sampled[0][1]
domain_id = top_domain['domain_id']
print(f"[*] Top Thompson Draw Target: {top_domain['industry']} -> {top_domain['user_group']} ({domain_id})")
# Load exemplars
exemplars = self.conn.execute("""
SELECT o.need_statement, c.headline, c.mechanism_type, v.final_success_score
FROM v_deployment_scores v
JOIN improvement_concepts c ON v.concept_id = c.concept_id
JOIN opportunities o ON c.opportunity_id = o.opportunity_id
WHERE v.final_success_score >= 0.70
ORDER BY (v.domain_id = ?) DESC, v.final_success_score DESC
LIMIT 2;
""", (domain_id,)).fetchall()
exemplar_str = "No prior high-scoring exemplars."
if exemplars:
exemplar_str = "\n".join([f"- Need: {e['need_statement']} | Mechanism: {e['mechanism_type']} ({e['headline']})" for e in exemplars])
prompt = f"""
TARGET DOMAIN:
- Domain: {domain_id}
- Industry: {top_domain['industry']}
- User Group: {top_domain['user_group']}
HISTORICAL HIGH-YIELD PATTERNS:
{exemplar_str}
Identify exactly {top_k_opps} high-leverage friction point/opportunity.
"""
response = self.client.models.generate_content(
model=self.model_name,
contents=prompt,
config=types.GenerateContentConfig(
system_instruction="You are a Principal Innovation Analyst. Identify concrete structural or operational friction points.",
response_mime_type="application/json",
response_schema=Step1OpportunityManifest,
temperature=0.35,
)
)
manifest = Step1OpportunityManifest.model_validate_json(response.text)
created_ids = []
for i, item in enumerate(manifest.opportunities, 1):
opp_count = self.conn.execute("SELECT COUNT(*) FROM opportunities;").fetchone()[0]
opp_id = f"opp_{domain_id.replace(':', '_')}_{opp_count + i:04d}"
metadata = {
"triz_direction": item.triz_direction,
"estimated_impact_tier": item.estimated_impact_tier,
"domain_id": domain_id
}
self.conn.execute("""
INSERT INTO opportunities (opportunity_id, domain_id, title, need_statement, root_cause_summary, source_model, generation_metadata, pipeline_stage)
VALUES (?, ?, ?, ?, ?, ?, ?, 'IDENTIFIED');
""", (opp_id, domain_id, item.title, item.need_statement, item.root_cause_hypothesis, self.model_name, json.dumps(metadata)))
created_ids.append(opp_id)
print(f"[+] Identified Opportunity: [{opp_id}] {item.title}")
self.conn.commit()
return created_ids
# --- Step 2: Root-Cause Decomposition ---
def execute_step_2(self, opportunity_ids: List[str]) -> List[str]:
print("\n================== STEP 2: ROOT-CAUSE DECOMPOSITION ==================")
analyzed_ids = []
for opp_id in opportunity_ids:
row = self.conn.execute("SELECT * FROM opportunities WHERE opportunity_id = ?;", (opp_id,)).fetchone()
if not row:
continue
prompt = f"""
OPPORTUNITY:
- ID: {row['opportunity_id']}
- Title: {row['title']}
- Need: {row['need_statement']}
- Initial Clue: {row['root_cause_summary']}
Decompose the failure mechanism, formulate the Technical Contradiction and Physical Contradiction, and determine if viable.
"""
response = self.client.models.generate_content(
model=self.model_name,
contents=prompt,
config=types.GenerateContentConfig(
system_instruction="You are a Principal Systems Root Cause Architect. Isolate root causes and contradictions via TRIZ.",
response_mime_type="application/json",
response_schema=RootCauseAnalysisResult,
temperature=0.2,
)
)
res = RootCauseAnalysisResult.model_validate_json(response.text)
meta_patch = {
"root_cause_decomposition": {
"underlying_system_constraint": res.underlying_system_constraint,
"technical_contradiction": res.technical_contradiction,
"physical_contradiction": res.physical_contradiction,
"deficits": [d.model_dump() for d in res.functional_deficits]
}
}
self.conn.execute("""
UPDATE opportunities
SET root_cause_summary = ?,
pipeline_stage = ?,
abandon_reason = ?,
generation_metadata = json_patch(COALESCE(generation_metadata, '{}'), ?)
WHERE opportunity_id = ?;
""", (res.synthesis_summary, res.recommended_stage, res.abandon_reason, json.dumps(meta_patch), opp_id))
self.conn.commit()
print(f"[+] Decomposed [{opp_id}]: Stage -> {res.recommended_stage}")
if res.recommended_stage == 'ANALYZED':
analyzed_ids.append(opp_id)
return analyzed_ids
# --- Step 3: N-Concept Improvement Synthesis ---
def execute_step_3(self, opportunity_ids: List[str], n_concepts: int = 3) -> List[str]:
print("\n================== STEP 3: CONCEPT SYNTHESIS ==================")
improved_concept_ids = []
for opp_id in opportunity_ids:
row = self.conn.execute("SELECT * FROM opportunities WHERE opportunity_id = ?;", (opp_id,)).fetchone()
meta = json.loads(row['generation_metadata'] or "{}")
decomp = meta.get("root_cause_decomposition", {})
prompt = f"""
ANALYZED CONTRADICTIONS:
- Opportunity: {row['title']}
- Constraint: {decomp.get('underlying_system_constraint')}
- Technical Contradiction: {decomp.get('technical_contradiction')}
- Physical Contradiction: {decomp.get('physical_contradiction')}
Synthesize exactly {n_concepts} ranked improvement concepts using TRIZ principles.
"""
response = self.client.models.generate_content(
model=self.model_name,
contents=prompt,
config=types.GenerateContentConfig(
system_instruction="You are a TRIZ Master Engineer. Generate distinct, non-trivial engineering improvement concepts.",
response_mime_type="application/json",
response_schema=Step3ConceptManifest,
temperature=0.35,
)
)
manifest = Step3ConceptManifest.model_validate_json(response.text)
for cpt in manifest.concepts:
cpt_pk = f"cpt_{opp_id}_{cpt.concept_order}"
self.conn.execute("""
INSERT INTO improvement_concepts (
concept_id, opportunity_id, concept_order, headline,
mechanism_type, projected_roi_ratio, technical_risk_tier, selected_for_impl
) VALUES (?, ?, ?, ?, ?, ?, ?, ?);
""", (cpt_pk, opp_id, cpt.concept_order, cpt.headline, cpt.mechanism_type, cpt.projected_roi_ratio, cpt.technical_risk_tier, 1 if cpt.selected_for_impl else 0))
if cpt.selected_for_impl:
improved_concept_ids.append(cpt_pk)
print(f"[+] Concept [{cpt_pk}]: {cpt.headline} (ROI: {cpt.projected_roi_ratio}x, Selected: {cpt.selected_for_impl})")
self.conn.execute("UPDATE opportunities SET pipeline_stage = 'IMPROVED' WHERE opportunity_id = ?;", (opp_id,))
self.conn.commit()
return improved_concept_ids
# --- Step 4: Prototyping Deployment & Telemetry Ingestion ---
def execute_step_4(self, concept_ids: List[str]) -> List[str]:
print("\n================== STEP 4: IMPLEMENTATION DEPLOYMENT ==================")
deployment_ids = []
for cpt_id in concept_ids:
row = self.conn.execute("""
SELECT c.*, o.title AS opp_title, o.domain_id
FROM improvement_concepts c
JOIN opportunities o ON c.opportunity_id = o.opportunity_id
WHERE c.concept_id = ?;
""", (cpt_id,)).fetchone()
prompt = f"""
CONCEPT TO DEPLOY:
- Concept ID: {cpt_id}
- Headline: {row['headline']}
- Mechanism: {row['mechanism_type']}
- Opportunity: {row['opp_title']}
Generate a 72-hour prototyping deployment plan with telemetry targets.
"""
response = self.client.models.generate_content(
model=self.model_name,
contents=prompt,
config=types.GenerateContentConfig(
system_instruction="You are a Technical Lead and DevOps Architect. Detail executable sprint steps and telemetry expectations.",
response_mime_type="application/json",
response_schema=DeploymentSpecification,
temperature=0.25,
)
)
spec = DeploymentSpecification.model_validate_json(response.text)
dep_id = f"dep_{cpt_id.replace('cpt_', '')}"
self.conn.execute("""
INSERT INTO deployments (
deployment_id, concept_id, deployed_version, deployment_status,
cost_budget_usd, cost_actual_usd, compliance_friction
) VALUES (?, ?, ?, 'PRODUCTION', ?, ?, ?);
""", (dep_id, cpt_id, spec.deployed_version, spec.cost_budget_usd, spec.estimated_actual_usd, spec.compliance_friction_score))
# Simulate initial 30-day early evaluation telemetry
subtotal = (spec.expected_30d_adoption_rate + spec.expected_30d_defect_reduction + 0.90 + 0.85) / 4.0
self.conn.execute("""
INSERT INTO milestone_evaluations (
evaluation_id, deployment_id, horizon_tier,
feasibility_score, adoption_rate, defect_reduction, cost_savings_factor, horizon_subtotal
) VALUES (?, ?, 'EARLY_30D', 0.90, ?, ?, 0.85, ?);
""", (f"eval_{dep_id}_30d", dep_id, spec.expected_30d_adoption_rate, spec.expected_30d_defect_reduction, subtotal))
self.conn.execute("UPDATE opportunities SET pipeline_stage = 'DEPLOYED' WHERE opportunity_id = ?;", (row['opportunity_id'],))
self.conn.commit()
deployment_ids.append(dep_id)
print(f"[+] Deployment Activated: [{dep_id}] Version: {spec.deployed_version} (Score: {subtotal:.3f})")
return deployment_ids
# --- Thompson Sampling Feedback Update ---
def execute_feedback_update(self, success_threshold: float = 0.70):
print("\n================== THOMPSON SAMPLING FEEDBACK UPDATE ==================")
cur = self.conn.cursor()
# Update from deployments
cur.execute("""
WITH recent_domain_signals AS (
SELECT
domain_id,
SUM(CASE WHEN final_success_score >= ? THEN final_success_score ELSE 0.0 END) AS delta_alpha,
SUM(CASE WHEN final_success_score < ? THEN (1.0 - final_success_score) ELSE 0.0 END) AS delta_beta,
AVG(final_success_score) AS current_avg
FROM v_deployment_scores
GROUP BY domain_id
)
UPDATE domains
SET
alpha_prior = alpha_prior + COALESCE((SELECT delta_alpha FROM recent_domain_signals WHERE recent_domain_signals.domain_id = domains.domain_id), 0.0),
beta_prior = beta_prior + COALESCE((SELECT delta_beta FROM recent_domain_signals WHERE recent_domain_signals.domain_id = domains.domain_id), 0.0),
total_trials = (SELECT COUNT(*) FROM opportunities WHERE opportunities.domain_id = domains.domain_id),
rolling_success_avg = COALESCE((SELECT current_avg FROM recent_domain_signals WHERE recent_domain_signals.domain_id = domains.domain_id), rolling_success_avg),
last_sampled_at = CURRENT_TIMESTAMP
WHERE domain_id IN (SELECT domain_id FROM recent_domain_signals);
""", (success_threshold, success_threshold))
# Update penalties from abandoned pipelines
cur.execute("""
UPDATE domains
SET
beta_prior = beta_prior + 1.0,
total_trials = (SELECT COUNT(*) FROM opportunities WHERE opportunities.domain_id = domains.domain_id),
last_sampled_at = CURRENT_TIMESTAMP
WHERE domain_id IN (
SELECT domain_id FROM opportunities
WHERE pipeline_stage = 'ABANDONED'
AND opportunity_id NOT IN (SELECT opportunity_id FROM v_deployment_scores)
);
""")
self.conn.commit()
# Print current Bayesian distribution
domains = self.conn.execute("SELECT domain_id, alpha_prior, beta_prior, rolling_success_avg, total_trials FROM domains;").fetchall()
print("\nUpdated Domain Bandit Allocations:")
for d in domains:
mean = d['alpha_prior'] / (d['alpha_prior'] + d['beta_prior'])
print(f" - {d['domain_id']:<26} | Alpha: {d['alpha_prior']:<5.2f} | Beta: {d['beta_prior']:<5.2f} | Trials: {d['total_trials']} | Prior Mean: {mean:.3f}")
def run_cycle(self):
"""Execute full 1 -> 2 -> 3 -> 4 -> Feedback loop."""
opp_ids = self.execute_step_1(top_k_opps=1)
if not opp_ids:
print("Step 1 yielded no opportunities.")
return
analyzed_ids = self.execute_step_2(opp_ids)
if not analyzed_ids:
print("All opportunities were abandoned in Step 2. Triggering feedback update.")
self.execute_feedback_update()
return
concept_ids = self.execute_step_3(analyzed_ids, n_concepts=3)
if not concept_ids:
print("No concepts selected for implementation.")
return
dep_ids = self.execute_step_4(concept_ids)
self.execute_feedback_update()
print(f"\n[✓] Pipeline execution cycle successfully completed for deployments: {dep_ids}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Master Innovation Pipeline Runner")
parser.add_argument("--db", type=str, default="pipeline.db", help="Path to SQLite database file")
parser.add_argument("--model", type=str, default="gemini-2.5-pro", help="Gemini LLM model name")
args = parser.parse_args()
orchestrator = InnovationPipelineOrchestrator(db_path=args.db, model_name=args.model)
orchestrator.run_cycle()
Execution Instructions
Prerequisites: Ensure the Google GenAI SDK and Pydantic are installed:
pip install google-genai pydanticAuthentication: Export your Gemini API key in your shell:
export GEMINI_API_KEY="your-api-key"Run a Single End-to-End Cycle:
python run_pipeline.py --db pipeline.db --model gemini-2.5-pro
Running this command executes the full pipeline end-to-end: it selects the top domain via Thompson Sampling, identifies and stores the opportunity in Step 1, decomposes its TRIZ contradiction in Step 2, generates 3 ranked concepts in Step 3, constructs the prototyping plan in Step 4, logs the resulting metrics, and updates the Bayesian distribution for the next iteration.
Would you like to wrap this runner in a scheduled autonomous loop (e.g., using cron or an asynchronous queue) that runs M continuous cycles until a convergence or discovery threshold is reached?
Show how to wrap this orchestrator in a multi-cycle loop with early-stopping and convergence criteria based on domain reward stabilization.
An autonomous multi-cycle wrapper requires a mathematical stabilization harness rather than a simple counter. Because our Step 1 prioritizer models each domain as a Beta distribution $\text{Beta}(\alpha, \beta)$
Convergence Mathematics & Stopping Criteria
To prevent premature termination during early exploration while avoiding wasted API calls once returns plateau, the monitor checks three conditions across iterations $t$:
Warmup Exploration Guard: Prohibits halting before completing a mandatory minimum exploration budget ($t < t_{\text{min}}$) so all initial domains receive trial runs
. Mean Posterior Shift ($\Delta \mu$): Computes the maximum change in expected value across all registered domains:
$$\Delta \mu_t = \max_{d \in D} \left\vert{} \mathbb{E}[\theta_{d, t}] - \mathbb{E}[\theta_{d, t-1}] \right\vert{} = \max_{d \in D} \left\vert{} \frac{\alpha_{d, t}}{\alpha_{d, t} + \beta_{d, t}} - \frac{\alpha_{d, t-1}}{\alpha_{d, t-1} + \beta_{d, t-1}} \right\vert{}$$Condition: $\Delta \mu_t \le \epsilon_{\text{shift}}$ (e.g., $\le 0.015$).
Posterior Variance Ceiling ($\sigma^2_{\text{top}}$): Verifies that the leading domain's uncertainty has dropped sufficiently to confirm exploitation dominance:
$$\operatorname{Var}(\theta_{\text{top}}) = \frac{\alpha \beta}{(\alpha + \beta)^2(\alpha + \beta + 1)} \le \delta_{\text{var}} \quad (\text{e.g., } \le 0.020)$$Patience Window ($P$): Both $\Delta \mu_t \le \epsilon_{\text{shift}}$ and $\sigma^2_{\text{top}} \le \delta_{\text{var}}$ must hold for $P$ consecutive cycles (e.g., $P = 3$) before early stopping triggers.
Autonomous Runner Script (run_autonomous_loop.py)
"""
Autonomous Multi-Cycle Pipeline Orchestrator with Convergence-Based Early Stopping (`run_autonomous_loop.py`):
Wraps the 4-step innovation pipeline in a self-governing loop that monitors the posterior Beta distribution
parameters (alpha, beta, expected mean, variance) across all domains, halting execution when domain rewards
stabilize or when maximum cycle budgets are reached.
"""
import sys
import json
import sqlite3
import argparse
from typing import Dict, List, Tuple
from pydantic import BaseModel, Field
# Import pipeline orchestrator primitives from run_pipeline
from run_pipeline import InnovationPipelineOrchestrator
class DomainRewardSnapshot(BaseModel):
domain_id: str
alpha: float
beta: float
expected_mean: float
variance: float
total_trials: int
class ConvergenceMonitor:
"""
Evaluates convergence and stability across multi-armed bandit domain allocations.
Convergence Criteria:
1. Minimum Warmup: Guarantees a minimum exploration period (min_cycles).
2. Max Expected Shift: Checks if max(|E[theta_t] - E[theta_{t-1}]|) < mean_shift_tolerance.
3. Uncertainty Horizon: Checks if the leading domain's posterior variance falls below variance_threshold.
4. Consecutive Patience: Requires criteria to hold for 'patience' successive cycles.
"""
def __init__(
self,
mean_shift_tolerance: float = 0.015,
variance_threshold: float = 0.025,
patience: int = 3,
min_cycles: int = 4
):
self.mean_shift_tolerance = mean_shift_tolerance
self.variance_threshold = variance_threshold
self.patience = patience
self.min_cycles = min_cycles
self.patience_counter = 0
self.history: List[Dict[str, DomainRewardSnapshot]] = []
def get_snapshot(self, conn: sqlite3.Connection) -> Dict[str, DomainRewardSnapshot]:
rows = conn.execute("""
SELECT domain_id, alpha_prior, beta_prior, total_trials
FROM domains;
""").fetchall()
snapshot = {}
for r in rows:
a, b = float(r["alpha_prior"]), float(r["beta_prior"])
mean = a / (a + b)
var = (a * b) / (((a + b) ** 2) * (a + b + 1.0))
snapshot[r["domain_id"]] = DomainRewardSnapshot(
domain_id=r["domain_id"],
alpha=a,
beta=b,
expected_mean=round(mean, 5),
variance=round(var, 6),
total_trials=int(r["total_trials"])
)
return snapshot
def check_convergence(self, conn: sqlite3.Connection, current_cycle: int) -> Tuple[bool, str, Dict]:
current_snapshot = self.get_snapshot(conn)
if current_cycle < self.min_cycles:
self.history.append(current_snapshot)
return False, f"Warmup Phase: Cycle {current_cycle}/{self.min_cycles} completed.", {}
prev_snapshot = self.history[-1]
self.history.append(current_snapshot)
# 1. Compute delta in expected means across common domains
deltas = {}
for d_id, cur in current_snapshot.items():
if d_id in prev_snapshot:
deltas[d_id] = abs(cur.expected_mean - prev_snapshot[d_id].expected_mean)
max_mean_delta = max(deltas.values()) if deltas else 0.0
# 2. Check top domain uncertainty
top_domain = max(current_snapshot.values(), key=lambda d: d.expected_mean)
top_variance = top_domain.variance
telemetry = {
"max_mean_delta": round(max_mean_delta, 5),
"top_domain": top_domain.domain_id,
"top_domain_mean": top_domain.expected_mean,
"top_domain_variance": top_variance,
"patience": f"{self.patience_counter}/{self.patience}"
}
# Check conditions
mean_stabilized = max_mean_delta <= self.mean_shift_tolerance
variance_settled = top_variance <= self.variance_threshold
if mean_stabilized and variance_settled:
self.patience_counter += 1
if self.patience_counter >= self.patience:
reason = (
f"CONVERGED: Max mean shift ({max_mean_delta:.4f} <= {self.mean_shift_tolerance}) "
f"and top domain variance ({top_variance:.5f} <= {self.variance_threshold}) "
f"sustained for {self.patience} consecutive cycles."
)
return True, reason, telemetry
else:
status = f"STABILIZING: Criteria met for {self.patience_counter}/{self.patience} cycles."
return False, status, telemetry
else:
self.patience_counter = 0
reasons = []
if not mean_stabilized:
reasons.append(f"mean delta ({max_mean_delta:.4f} > {self.mean_shift_tolerance})")
if not variance_settled:
reasons.append(f"variance ({top_variance:.5f} > {self.variance_threshold})")
return False, f"EVOLVING: Active exploration driven by {', '.join(reasons)}.", telemetry
class AutonomousPipelineLoop:
def __init__(
self,
db_path: str = "pipeline.db",
model_name: str = "gemini-2.5-pro",
max_cycles: int = 15,
min_cycles: int = 4,
tolerance: float = 0.015,
variance_cutoff: float = 0.025,
patience: int = 3
):
self.orchestrator = InnovationPipelineOrchestrator(db_path=db_path, model_name=model_name)
self.max_cycles = max_cycles
self.monitor = ConvergenceMonitor(
mean_shift_tolerance=tolerance,
variance_threshold=variance_cutoff,
patience=patience,
min_cycles=min_cycles
)
def run(self):
print("=" * 70)
print("STARTING AUTONOMOUS INNOVATION PIPELINE LOOP")
print(f"Max Cycles: {self.max_cycles} | Min Warmup: {self.monitor.min_cycles} | Patience: {self.monitor.patience}")
print(f"Mean Tolerance: {self.monitor.mean_shift_tolerance} | Variance Cutoff: {self.monitor.variance_threshold}")
print("=" * 70)
for cycle in range(1, self.max_cycles + 1):
print(f"\n>>> COMMENCING PIPELINE CYCLE {cycle}/{self.max_cycles}")
try:
self.orchestrator.run_cycle()
except Exception as e:
print(f"[ERROR] Cycle {cycle} failed during execution: {str(e)}")
break
# Evaluate mathematical convergence criteria
converged, status_msg, telemetry = self.monitor.check_convergence(
self.orchestrator.conn,
current_cycle=cycle
)
print(f"\n[Convergence Monitor - Cycle {cycle}]")
print(f" Status : {status_msg}")
if telemetry:
print(f" Telemetry: Max Delta={telemetry['max_mean_delta']} | Top={telemetry['top_domain']} "
f"(Mean={telemetry['top_domain_mean']}, Var={telemetry['top_domain_variance']}) | "
f"Patience={telemetry['patience']}")
if converged:
print("\n" + "=" * 70)
print(f"EARLY STOPPING TRIGGERED AT CYCLE {cycle}")
print(status_msg)
print("=" * 70)
self._print_terminal_summary()
return
print("\n" + "=" * 70)
print(f"REACHED MAXIMUM CYCLE BUDGET ({self.max_cycles}). TERMINATING LOOP.")
print("=" * 70)
self._print_terminal_summary()
def _print_terminal_summary(self):
cur = self.orchestrator.conn.cursor()
print("\nFINAL DOMAIN BANDIT POSTERIOR DISTRIBUTION:")
rows = cur.execute("""
SELECT domain_id, industry, user_group, alpha_prior, beta_prior, total_trials, rolling_success_avg
FROM domains
ORDER BY (alpha_prior / (alpha_prior + beta_prior)) DESC;
""").fetchall()
for r in rows:
a, b = float(r["alpha_prior"]), float(r["beta_prior"])
mean = a / (a + b)
var = (a * b) / (((a + b) ** 2) * (a + b + 1.0))
print(f" * {r['domain_id']:<28} | Mean: {mean:.4f} | Var: {var:.5f} | Trials: {r['total_trials']:<2} | "
f"Alpha: {a:<5.2f} | Beta: {b:<5.2f} | Sector: {r['industry']}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Autonomous Multi-Cycle Pipeline Loop with Early Stopping")
parser.add_argument("--db", type=str, default="pipeline.db", help="Path to SQLite database file")
parser.add_argument("--model", type=str, default="gemini-2.5-pro", help="Gemini LLM model name")
parser.add_argument("--max-cycles", type=int, default=12, help="Maximum allowed iterations")
parser.add_argument("--min-cycles", type=int, default=4, help="Minimum warm-up exploration cycles")
parser.add_argument("--tolerance", type=float, default=0.015, help="Maximum allowable mean shift across domains")
parser.add_argument("--variance-cutoff", type=float, default=0.020, help="Variance upper bound for top domain")
parser.add_argument("--patience", type=int, default=3, help="Consecutive cycles criteria must hold")
args = parser.parse_args()
loop = AutonomousPipelineLoop(
db_path=args.db,
model_name=args.model,
max_cycles=args.max_cycles,
min_cycles=args.min_cycles,
tolerance=args.tolerance,
variance_cutoff=args.variance_cutoff,
patience=args.patience
)
loop.run()
Command-Line Usage
To execute with custom stopping thresholds:
python run_autonomous_loop.py \
--db pipeline.db \
--model gemini-2.5-pro \
--max-cycles 15 \
--min-cycles 4 \
--tolerance 0.015 \
--variance-cutoff 0.020 \
--patience 3
During execution, each cycle prints telemetry tracking max_mean_delta across domains alongside the leading domain's posterior variance. As soon as the algorithm concentrates sufficient successful runs in the highest-yielding domain and stops oscillating across alternatives, early stopping safely halts the execution and displays the final ranking.
Would you like to build an automated HTML dashboard or Markdown summary generator that exports the complete opportunity portfolio and Pareto frontier upon loop termination?
Show how to automatically discover and insert new domains into the domains table if all current domains settle below a minimum viable reward threshold.
Your Python file is ready
domain_expansion_manager
PY
To dynamically expand the candidate domain space when all current sectors stagnate, you need an automated meta-exploration circuit. When every adequately tested domain falls below your minimum viable return threshold ($\tau_{\min}$), the pipeline queries previously abandoned failure patterns from SQLite, prompts Gemini to hypothesize novel frontier sectors orthogonal to those failures, and inserts them into the domains table with optimistic initialization priors to immediately bootstrap Thompson Sampling.
Mathematical Stagnation Condition
A domain pool $D$ is flagged as stagnant if all domains meeting a minimum trial threshold ($N_{\text{trials}} \ge N_{\min}$) exhibit an expected posterior mean reward below $\tau_{\min}$
$$\forall d \in D_{\text{active}}, \quad \mathbb{E}[\theta_d] = \frac{\alpha_d}{\alpha_d + \beta_d} < \tau_{\min} \quad (\text{e.g., } \tau_{\min} = 0.45)$$
When this triggers, novel domains are inserted with optimistic priors ($\alpha_0 = 1.5, \beta_0 = 1.0 \implies \mathbb{E}[\theta_0] = 0.60$)
Dynamic Expansion Script (domain_expansion_manager.py)
"""
Dynamic Domain Expansion Module:
Monitors current bandit reward distributions in SQLite. When all explored domains
drop below a minimum viable reward threshold (or exhaust viable opportunities),
it prompts Gemini to discover novel, structurally adjacent industrial domains and
user groups, inserting them into SQLite with optimistic priors.
"""
import json
import sqlite3
from typing import List, Optional, Tuple
from pydantic import BaseModel, Field
from google import genai
from google.genai import types
# ---------------------------------------------------------------------
# 1. Pydantic Schemas for Domain Discovery
# ---------------------------------------------------------------------
class DiscoveredDomain(BaseModel):
domain_id: str = Field(
description="Globally unique snake_case identifier formatted as 'industry_slug:user_group_slug'."
)
industry: str = Field(
description="High-level industry or sector name (e.g., 'Precision Agriculture', 'Subsea Robotics')."
)
user_group: str = Field(
description="Specific target role or user cohort experiencing operational friction."
)
structural_rationale: str = Field(
description="Architectural reason why this domain represents a high-potential frontier distinct from exhausted domains."
)
class DomainExpansionManifest(BaseModel):
expansion_trigger_reason: str
discovered_domains: List[DiscoveredDomain] = Field(
description="List of novel domain candidates to register."
)
# ---------------------------------------------------------------------
# 2. Dynamic Domain Expansion Engine
# ---------------------------------------------------------------------
class DomainExpansionManager:
def __init__(
self,
db_conn: sqlite3.Connection,
model_name: str = "gemini-2.5-pro",
min_reward_threshold: float = 0.45,
min_exploration_trials: int = 2
):
self.conn = db_conn
self.model_name = model_name
self.min_reward_threshold = min_reward_threshold
self.min_exploration_trials = min_exploration_trials
self.client = genai.Client()
def check_stagnation(self) -> Tuple[bool, List[dict]]:
"""
Evaluates whether the portfolio is stagnant:
Returns True if ALL domains with >= min_exploration_trials have
an expected mean reward < min_reward_threshold.
"""
rows = self.conn.execute("""
SELECT domain_id, industry, user_group, alpha_prior, beta_prior, total_trials, rolling_success_avg
FROM domains;
""").fetchall()
if not rows:
return True, []
domain_stats = []
exhausted_count = 0
active_trials_count = 0
for r in rows:
a, b = float(r["alpha_prior"]), float(r["beta_prior"])
mean = a / (a + b)
trials = int(r["total_trials"])
d_dict = dict(r)
d_dict["mean_reward"] = round(mean, 4)
domain_stats.append(d_dict)
if trials >= self.min_exploration_trials:
active_trials_count += 1
if mean < self.min_reward_threshold:
exhausted_count += 1
is_stagnant = (active_trials_count > 0) and (exhausted_count == active_trials_count)
return is_stagnant, domain_stats
def discover_frontier_domains(
self,
exhausted_domains: List[dict],
num_candidates: int = 3
) -> DomainExpansionManifest:
"""Prompt Gemini to hypothesize novel domains orthogonal to exhausted ones."""
cur = self.conn.cursor()
# Pull previous abandoned failure modes to provide negative constraints
failures = cur.execute("""
SELECT o.title, o.abandon_reason, o.root_cause_summary, d.industry
FROM opportunities o
JOIN domains d ON o.domain_id = d.domain_id
WHERE o.pipeline_stage = 'ABANDONED'
LIMIT 5;
""").fetchall()
failure_context = "\n".join([
f"- [{f['industry']}] {f['title']}: {f['abandon_reason']} ({f['root_cause_summary']})"
for f in failures
]) if failures else "No previous abandonment records."
exhausted_context = "\n".join([
f"- {d['domain_id']} ({d['industry']} / {d['user_group']}): Mean Reward = {d['mean_reward']:.3f}, Trials = {d['total_trials']}"
for d in exhausted_domains
])
system_instruction = (
"You are a Strategic Innovation Director and Systems Horizon Analyst. "
"All current active domains in the innovation pipeline have exhausted their high-yield potential or "
"settled below the minimum viable reward threshold. Your task is to discover structurally adjacent, "
"untapped industry sectors and technical user groups where unaddressed high-value friction exists. "
"Avoid sectors with failure modes similar to previously abandoned initiatives."
)
user_prompt = f"""
EXHAUSTED / SUB-THRESHOLD DOMAINS:
{exhausted_context}
HISTORICAL FAILURE/ABANDONMENT MODES (NEGATIVE CONSTRAINTS):
{failure_context}
TASK:
1. Synthesize exactly {num_candidates} novel, high-potential domains (industry + specific user group).
2. Ensure domain IDs are unique, valid snake_case (`industry_slug:user_group_slug`).
3. Detail an architectural rationale explaining why each domain bypasses previous bottlenecks.
"""
response = self.client.models.generate_content(
model=self.model_name,
contents=user_prompt,
config=types.GenerateContentConfig(
system_instruction=system_instruction,
response_mime_type="application/json",
response_schema=DomainExpansionManifest,
temperature=0.45,
),
)
return DomainExpansionManifest.model_validate_json(response.text)
def register_new_domains(
self,
manifest: DomainExpansionManifest,
optimistic_alpha: float = 1.5,
optimistic_beta: float = 1.0
) -> List[str]:
"""
Inserts discovered domains with optimistic priors (E[theta] = 0.60)
to trigger immediate exploration via Thompson Sampling.
"""
inserted_ids = []
for d in manifest.discovered_domains:
self.conn.execute("""
INSERT INTO domains (
domain_id, industry, user_group, alpha_prior, beta_prior, total_trials, rolling_success_avg
) VALUES (?, ?, ?, ?, ?, 0, 0.0)
ON CONFLICT(domain_id) DO NOTHING;
""", (d.domain_id, d.industry, d.user_group, optimistic_alpha, optimistic_beta))
inserted_ids.append(d.domain_id)
self.conn.commit()
return inserted_ids
def evaluate_and_expand(self, num_candidates: int = 3) -> Optional[List[str]]:
"""Orchestrates detection and expansion. Returns new domain IDs if triggered."""
is_stagnant, domain_stats = self.check_stagnation()
if not is_stagnant:
return None
print("\n[!] Stagnation Detected: All active domains have settled below threshold.")
manifest = self.discover_frontier_domains(domain_stats, num_candidates=num_candidates)
new_ids = self.register_new_domains(manifest)
print(f"[+] Expanded search space with {len(new_ids)} novel domains: {new_ids}")
return new_ids
Wiring into the Autonomous Loop
To integrate this check into your pipeline runner, invoke evaluate_and_expand() at the beginning of each cycle before Step 1 samples the target domain
Python
# Inside run_cycle() or run_autonomous_loop.py before execute_step_1():
from domain_expansion_manager import DomainExpansionManager
expansion_mgr = DomainExpansionManager(
db_conn=self.conn,
model_name=self.model_name,
min_reward_threshold=0.45,
min_exploration_trials=2
)
# Proactively check and inject new domains if stagnant
new_domains = expansion_mgr.evaluate_and_expand(num_candidates=3)
if new_domains:
print(f"Injected new frontier domains for Step 1 sampling: {new_domains}")
# Continue with normal Thompson Sampling draw
opp_ids = self.execute_step_1(top_k_opps=1)
Key Behaviors
Failure Mode Inoculation: The prompt builder pulls prior opportunities marked
ABANDONEDalong with theirabandon_reasonandroot_cause_summaryfrom SQLite, feeding them to Gemini as explicit negative constraints. Optimistic Initial Sampling: By assigning newly registered domains alpha_0 = 1.5 and beta_0 = 1.0, their initial expected return starts at 0.60. This guarantees they beat exhausted domains (which have settled below 0.45 due to accumulated failure penalties
) in upcoming Thompson draws while remaining subject to immediate downgrade if Step 2 or 4 fails.
Would you like to build an automated HTML dashboard or Markdown summary generator that exports the complete opportunity portfolio and Pareto frontier upon loop termination?
How can I structure a Neo4j knowledge graph to detect cross-domain isomorphisms so high-scoring solutions in one industry automatically seed Step 1 in adjacent industries?
To detect structural isomorphisms across domains, a knowledge graph cannot merely store entities by their surface vocabulary (e.g., "turbine blade" vs. "software hazard matrix"). Cross-domain transfer requires separating domain-specific physical/operational entities from domain-agnostic functional abstractions (systemic dynamics, contradictions, and causal topologies).
When an improvement concept scores >= 0.70 in Industry A
DOMAIN A (Source: High Score) DOMAIN B (Target: Unaddressed)
┌──────────────────────────────┐ ┌──────────────────────────────┐
│ Concrete Entity / System │ │ Concrete Entity / System │
└──────────────┬───────────────┘ └──────────────┬───────────────┘
│ (instantiates) │ (instantiates)
▼ ▼
┌──────────────────────────────┐ ISOMORPHIC ┌──────────────────────────────┐
│ Abstract Functional Deficit │◄══════════════►│ Abstract Functional Deficit │
└──────────────┬───────────────┘ TOPOLOGY └──────────────┬───────────────┘
│ (governed by) │ (governed by)
▼ ▼
┌──────────────────────────────┐ ┌──────────────────────────────┐
│ Contradiction / Conflict │ │ Contradiction / Conflict │
└──────────────┬───────────────┘ └──────────────┬───────────────┘
│ (resolved by) │ (projected to)
▼ ▼
┌──────────────────────────────┐ ┌──────────────────────────────┐
│ Proven Solution Mechanism │═══════════════>│ Seed Step 1 Discovery │
│ (Score >= 0.70) │ Transfer │ (Industry B) │
└──────────────────────────────┘ └──────────────────────────────┘
1. Neo4j Multi-Layer Property Graph Schema
The schema separates data into three distinct ontological layers:
Domain Context Layer: Concrete industries, subsystems, and physical components.
Functional Abstraction Layer: System-level functional verbs, causal mechanisms, and failure dynamics.
Contradiction & Mechanism Layer: TRIZ contradiction pairs, inventive operators, and deployment outcomes
.
// =====================================================================
// CONSTRAINTS & INDEXES
// =====================================================================
CREATE CONSTRAINT unique_domain_id IF NOT EXISTS
FOR (d:Domain) REQUIRE d.id IS UNIQUE;
CREATE CONSTRAINT unique_opportunity_id IF NOT EXISTS
FOR (o:Opportunity) REQUIRE o.id IS UNIQUE;
CREATE CONSTRAINT unique_concept_id IF NOT EXISTS
FOR (c:ImprovementConcept) REQUIRE c.id IS UNIQUE;
CREATE CONSTRAINT unique_functional_node IF NOT EXISTS
FOR (f:FunctionalDeficit) REQUIRE f.id IS UNIQUE;
CREATE CONSTRAINT unique_contradiction_node IF NOT EXISTS
FOR (tc:TechnicalContradiction) REQUIRE tc.id IS UNIQUE;
CREATE INDEX idx_eval_score IF NOT EXISTS
FOR (c:ImprovementConcept) ON (c.final_score);
Node Labels & Properties
(:Domain {id, industry, user_group, alpha_prior, beta_prior})(:Opportunity {id, title, need_statement, pipeline_stage})(:Subsystem {name, physical_type, operational_context})(:FunctionalDeficit {id, verb, target_property, failure_mode, causal_topology})Example:
verb: "trace",target_property: "hierarchical_dependency",failure_mode: "context_fragmentation",causal_topology: "n_to_m_lossy_mapping".
(:TechnicalContradiction {id, improving_param, worsening_param, physical_conflict})Parameters: Standardized via engineering parameters (e.g., Information Accuracy vs. Complexity/Context Latency).
(:InventiveOperator {triz_id, name, principle_number})(:ImprovementConcept {id, headline, mechanism_type, final_score, deployment_status})
Relationship Topology
(:Domain)-[:OWNS_OPPORTUNITY]->(:Opportunity)
(:Opportunity)-[:MANIFESTS_IN]->(:Subsystem)
(:Subsystem)-[:EXHIBITS_DEFICIT]->(:FunctionalDeficit)
(:FunctionalDeficit)-[:GOVERNED_BY]->(:TechnicalContradiction)
(:TechnicalContradiction)-[:RESOLVED_BY]->(:InventiveOperator)
(:ImprovementConcept)-[:APPLIES_OPERATOR]->(:InventiveOperator)
(:ImprovementConcept)-[:VALIDATED_BY]->(:Opportunity)
2. Isomorphism Detection via Graph Cypher Pattern Matching
An isomorphism occurs when Opportunity O_A in Domain A and Opportunity O_B in Domain B connect to topologically identical abstraction paths, even though their concrete entities are completely different.
Cypher Query: Cross-Domain Isomorphic Candidate Retrieval
This query identifies verified, high-scoring solutions in Domain A (score >= 0.70)
MATCH (source_domain:Domain)-[:OWNS_OPPORTUNITY]->(source_opp:Opportunity)
-[:MANIFESTS_IN]->(:Subsystem)-[:EXHIBITS_DEFICIT]->(f_src:FunctionalDeficit)
-[:GOVERNED_BY]->(tc:TechnicalContradiction)<-[:GOVERNED_BY]-(f_tgt:FunctionalDeficit)
<-[:EXHIBITS_DEFICIT]-(:Subsystem)<-[:MANIFESTS_IN]-(target_opp:Opportunity)
<-[:OWNS_OPPORTUNITY]-(target_domain:Domain)
// Ensure cross-domain traversal
WHERE source_domain.id <> target_domain.id
// Trace validated, high-performing concepts from the source
MATCH (concept:ImprovementConcept)-[:VALIDATED_BY]->(source_opp)
MATCH (concept)-[:APPLIES_OPERATOR]->(op:InventiveOperator)
WHERE concept.final_score >= 0.70
// Optional: check functional verb or topology alignment
WHERE f_src.verb = f_tgt.verb
OR f_src.causal_topology = f_tgt.causal_topology
RETURN
source_domain.industry AS source_industry,
source_opp.title AS source_problem,
concept.headline AS proven_solution,
concept.mechanism_type AS proven_mechanism,
concept.final_score AS historical_score,
op.name AS transferrable_operator,
tc.improving_param AS resolved_tradeoff_improving,
tc.worsening_param AS resolved_tradeoff_worsening,
target_domain.id AS target_domain_id,
target_domain.industry AS target_industry,
target_domain.user_group AS target_user_group,
target_opp.title AS target_analogous_problem,
f_tgt.failure_mode AS target_friction_point
ORDER BY concept.final_score DESC
LIMIT 5;
3. Graph Data Science (GDS) Topological Proximity
When exact lexical matches on verbs or parameters do not exist, use graph topological representations (Node2Vec / FastRP embeddings) in Neo4j GDS to detect latent structural isomorphisms across problem subgraphs.
// 1. Project the functional bipartite subgraph into GDS
CALL gds.graph.project(
'functionalIsomorphismGraph',
['Subsystem', 'FunctionalDeficit', 'TechnicalContradiction', 'InventiveOperator'],
{
EXHIBITS_DEFICIT: {orientation: 'UNDIRECTED'},
GOVERNED_BY: {orientation: 'UNDIRECTED'},
RESOLVED_BY: {orientation: 'UNDIRECTED'}
}
);
// 2. Compute topological node embeddings
CALL gds.fastRP.mutate('functionalIsomorphismGraph', {
mutateProperty: 'structuralEmbedding',
embeddingDimension: 64,
iterationWeights: [0.0, 1.0, 0.7, 0.4]
});
// 3. Write embeddings back to Neo4j
CALL gds.graph.nodeProperty.stream('functionalIsomorphismGraph', 'structuralEmbedding')
YIELD nodeId, propertyValue
MATCH (n) WHERE id(n) = nodeId
SET n.structuralEmbedding = propertyValue;
Once computed, a cosine similarity between f_src.structuralEmbedding and f_tgt.structuralEmbedding quantitatively isolates analogical bridges regardless of industry nomenclature.
4. Direct Python Integration into Step 1 Discovery
This module queries Neo4j for structural isomorphisms and injects the proven source mechanism into the Gemini Step 1 prompt generator as an analogical seed:
import json
import sqlite3
from typing import List, Optional
from pydantic import BaseModel, Field
from neo4j import GraphDatabase
from google import genai
from google.genai import types
class CrossDomainSeed(BaseModel):
source_industry: str
proven_concept: str
proven_mechanism: str
transferrable_operator: str
tradeoff_resolved: str
target_analogy_rationale: str
class CrossDomainSeeder:
def __init__(self, neo4j_uri: str, neo4j_auth: tuple):
self.driver = GraphDatabase.driver(neo4j_uri, auth=neo4j_auth)
def fetch_isomorphic_seeds_for_domain(self, target_domain_id: str) -> List[dict]:
cypher = """
MATCH (target_domain:Domain {id: $domain_id})-[:OWNS_OPPORTUNITY]->(target_opp:Opportunity)
-[:MANIFESTS_IN]->(:Subsystem)-[:EXHIBITS_DEFICIT]->(f_tgt:FunctionalDeficit)
-[:GOVERNED_BY]->(tc:TechnicalContradiction)<-[:GOVERNED_BY]-(f_src:FunctionalDeficit)
<-[:EXHIBITS_DEFICIT]-(:Subsystem)<-[:MANIFESTS_IN]-(source_opp:Opportunity)
<-[:OWNS_OPPORTUNITY]-(source_domain:Domain)
WHERE source_domain.id <> target_domain.id
MATCH (concept:ImprovementConcept)-[:VALIDATED_BY]->(source_opp)
MATCH (concept)-[:APPLIES_OPERATOR]->(op:InventiveOperator)
WHERE concept.final_score >= 0.70
RETURN DISTINCT
source_domain.industry AS source_industry,
source_opp.title AS source_problem,
concept.headline AS proven_concept,
concept.mechanism_type AS proven_mechanism,
op.name AS transferrable_operator,
tc.improving_param + ' vs ' + tc.worsening_param AS tradeoff_resolved,
f_tgt.failure_mode AS target_friction
LIMIT 3;
"""
with self.driver.session() as session:
result = session.run(cypher, domain_id=target_domain_id)
return [record.data() for record in result]
def build_step1_isomorphic_prompt_block(self, target_domain_id: str) -> str:
seeds = self.fetch_isomorphic_seeds_for_domain(target_domain_id)
if not seeds:
return "No cross-domain topological isomorphisms detected. Proceed with baseline discovery."
lines = ["CROSS-DOMAIN STRUCTURAL ISOMORPHISMS (ANALOGICAL SEEDS):"]
for s in seeds:
lines.append(
f"- High-Scoring Precedent in [{s['source_industry']}]:\n"
f" * Mechanism: {s['proven_mechanism']} ({s['proven_concept']})\n"
f" * Operator Applied: {s['transferrable_operator']}\n"
f" * Conflict Resolved: {s['tradeoff_resolved']}\n"
f" * Structural Mapping: Transfer this mechanism to resolve '{s['target_friction']}' in the target domain."
)
return "\n".join(lines)
End-to-End Operational Trace
Local Success: An Aerospace S&MA team deploys a “Bidirectional Semantic Knowledge Graph for NASA STD Requirement Tracing”, scoring 0.91
. Graph Ingestion: Neo4j stores this solution under
InventiveOperator: "Intermediary / Segmentation"resolving the contradiction of “Verification Determinism vs. Context Window Scale”. Isomorphism Trigger: Autonomous exploration selects
Energy: Substation Relay Operations. The Cypher query traverses TechnicalContradictionand finds substation relay engineers have the exact same topological bottleneck: “Relay Configuration Determinism vs. Multi-Vendor Firmware Schema Heterogeneity”.Step 1 Transfer: Rather than initiating a blind search, Step 1 receives an analogical prompt seed:
"Aerospace successfully solved an isomorphic N-to-M hierarchical compliance deficit using deterministic graph-relational intermediates. Formulate an opportunity adapting this exact mechanism to substation relay firmware validation."
Execution Velocity: Step 1 directly generates high-leverage, pre-validated architectural candidates, bypassing initial low-yield exploration phases.
Would you like to write the ingestion pipeline that automatically syncs the SQLite opportunity and deployment records into this Neo4j graph schema after each cycle?
Write a Python script that monitors SQLite milestone evaluations and synchronizes completed runs into this multi-layer Neo4j graph schema.
Your Python file is ready
sync_sqlite_to_neo4j
PY
This synchronization daemon monitors evaluated milestones in SQLite, parses the telemetry and Step 2/3 metadata, and performs transactional UNWIND batch writes into the multi-layer Neo4j graph schema
Synchronization Daemon (sync_sqlite_to_neo4j.py)
"""
SQLite to Neo4j Real-Time Sync Daemon (`sync_sqlite_to_neo4j.py`):
Monitors milestone evaluations in SQLite, extracts verified deployment lineages,
deconstructs the Step 2 TRIZ decomposition context, and performs idempotent,
batched synchronization into the multi-layer Neo4j property graph schema.
"""
import os
import sys
import time
import json
import hashlib
import sqlite3
import argparse
from typing import List, Dict, Any, Optional
from pydantic import BaseModel, Field
try:
from neo4j import GraphDatabase, Driver
except ImportError:
GraphDatabase = None
Driver = Any
# ---------------------------------------------------------------------
# 1. Pydantic Payloads for Neo4j Batch Transaction
# ---------------------------------------------------------------------
class FunctionalDeficitRecord(BaseModel):
id: str
component_or_process: str
failure_mode: str
causal_mechanism: str
verb: str = "regulate"
causal_topology: str = "direct_coupling"
class SyncPayloadRecord(BaseModel):
deployment_id: str
concept_id: str
opportunity_id: str
domain_id: str
industry: str
user_group: str
opp_title: str
need_statement: str
root_cause_summary: Optional[str] = None
subsystem_name: str
functional_deficits: List[FunctionalDeficitRecord]
technical_contradiction_id: str
improving_param: str
worsening_param: str
physical_contradiction: Optional[str] = None
operator_name: str
concept_headline: str
mechanism_type: str
projected_roi_ratio: float
technical_risk_tier: int
final_score: float
deployment_status: str
# ---------------------------------------------------------------------
# 2. SQLite Extractor & State Management
# ---------------------------------------------------------------------
class SQLiteGraphSyncService:
def __init__(
self,
sqlite_path: str = "pipeline.db",
neo4j_uri: str = "bolt://localhost:7687",
neo4j_auth: tuple = ("neo4j", "password"),
batch_size: int = 50,
poll_interval_sec: float = 5.0
):
self.sqlite_path = sqlite_path
self.batch_size = batch_size
self.poll_interval_sec = poll_interval_sec
self.neo4j_uri = neo4j_uri
self.neo4j_auth = neo4j_auth
if GraphDatabase is None:
raise ImportError("neo4j package not found. Run 'pip install neo4j'.")
self.driver: Driver = GraphDatabase.driver(neo4j_uri, auth=neo4j_auth)
self._init_sqlite_watermark()
self._ensure_neo4j_schema()
def _init_sqlite_watermark(self):
"""Ensures a sync ledger table exists in SQLite to track synced evaluations."""
with sqlite3.connect(self.sqlite_path) as conn:
conn.execute("""
CREATE TABLE IF NOT EXISTS neo4j_sync_ledger (
deployment_id TEXT PRIMARY KEY,
synced_eval_count INTEGER NOT NULL,
last_final_score REAL NOT NULL,
synced_at TEXT NOT NULL DEFAULT CURRENT_TIMESTAMP,
FOREIGN KEY (deployment_id) REFERENCES deployments(deployment_id)
);
""")
conn.commit()
def _ensure_neo4j_schema(self):
"""Idempotently sets up Neo4j constraints and indexes for the multi-layer schema."""
constraints = [
"CREATE CONSTRAINT unique_domain_id IF NOT EXISTS FOR (d:Domain) REQUIRE d.id IS UNIQUE",
"CREATE CONSTRAINT unique_opportunity_id IF NOT EXISTS FOR (o:Opportunity) REQUIRE o.id IS UNIQUE",
"CREATE CONSTRAINT unique_concept_id IF NOT EXISTS FOR (c:ImprovementConcept) REQUIRE c.id IS UNIQUE",
"CREATE CONSTRAINT unique_functional_node IF NOT EXISTS FOR (f:FunctionalDeficit) REQUIRE f.id IS UNIQUE",
"CREATE CONSTRAINT unique_contradiction_node IF NOT EXISTS FOR (tc:TechnicalContradiction) REQUIRE tc.id IS UNIQUE",
"CREATE CONSTRAINT unique_operator_node IF NOT EXISTS FOR (op:InventiveOperator) REQUIRE op.name IS UNIQUE",
"CREATE INDEX idx_eval_score IF NOT EXISTS FOR (c:ImprovementConcept) ON (c.final_score)"
]
with self.driver.session() as session:
for q in constraints:
session.run(q)
def extract_pending_sync_records(self) -> List[SyncPayloadRecord]:
"""Queries completed deployments whose milestone state or score has evolved."""
with sqlite3.connect(self.sqlite_path) as conn:
conn.row_factory = sqlite3.Row
cursor = conn.cursor()
query = """
SELECT
d.deployment_id,
d.concept_id,
c.opportunity_id,
o.domain_id,
dom.industry,
dom.user_group,
o.title AS opp_title,
o.need_statement,
o.root_cause_summary,
o.generation_metadata,
c.concept_order,
c.headline AS concept_headline,
c.mechanism_type,
c.projected_roi_ratio,
c.technical_risk_tier,
v.final_success_score,
d.deployment_status,
(SELECT COUNT(*) FROM milestone_evaluations me WHERE me.deployment_id = d.deployment_id) AS current_eval_count,
COALESCE(l.synced_eval_count, 0) AS last_synced_eval_count,
COALESCE(l.last_final_score, -1.0) AS last_synced_score
FROM deployments d
JOIN improvement_concepts c ON d.concept_id = c.concept_id
JOIN opportunities o ON c.opportunity_id = o.opportunity_id
JOIN domains dom ON o.domain_id = dom.domain_id
JOIN v_deployment_scores v ON d.deployment_id = v.deployment_id
LEFT JOIN neo4j_sync_ledger l ON d.deployment_id = l.deployment_id
WHERE current_eval_count > 0
AND (current_eval_count > last_synced_eval_count OR ABS(v.final_success_score - last_synced_score) > 0.001)
LIMIT ?;
"""
rows = cursor.execute(query, (self.batch_size,)).fetchall()
records: List[SyncPayloadRecord] = []
for r in rows:
meta = json.loads(r["generation_metadata"] or "{}")
decomp = meta.get("root_cause_decomposition", {})
# Extract or generate functional deficits
raw_deficits = decomp.get("functional_deficits") or decomp.get("deficits") or []
parsed_deficits: List[FunctionalDeficitRecord] = []
subsystem_name = "Core Process"
for i, d in enumerate(raw_deficits):
comp = d.get("component_or_process", f"Subsystem_{i+1}")
subsystem_name = comp
f_hash_input = f"{r['domain_id']}_{comp}_{d.get('failure_mode')}".encode()
f_id = f"f_{hashlib.md5(f_hash_input).hexdigest()[:12]}"
parsed_deficits.append(FunctionalDeficitRecord(
id=f_id,
component_or_process=comp,
failure_mode=d.get("failure_mode", "Unspecified Failure"),
causal_mechanism=d.get("causal_mechanism", "Unspecified Mechanism"),
verb=comp.lower().split()[0] if comp else "process",
causal_topology="hierarchical_bottleneck"
))
if not parsed_deficits:
f_id = f"f_{hashlib.md5(r['opportunity_id'].encode()).hexdigest()[:12]}"
parsed_deficits.append(FunctionalDeficitRecord(
id=f_id,
component_or_process="Operational Workflow",
failure_mode="Execution Latency",
causal_mechanism=r["root_cause_summary"] or "Unstructured Process Friction"
))
# Parse contradiction details
tech_contradiction = decomp.get("technical_contradiction", "Improve Quality vs Increase Overhead")
if "vs" in tech_contradiction:
parts = tech_contradiction.split("vs")
improving_param = parts[0].replace("Improving", "").strip()
worsening_param = parts[1].replace("Degrades", "").strip()
else:
improving_param = "Throughput"
worsening_param = "Stability"
tc_hash_input = f"{improving_param}_{worsening_param}".encode()
tc_id = f"tc_{hashlib.md5(tc_hash_input).hexdigest()[:12]}"
# Parse TRIZ operator applied
concepts_list = meta.get("step_3_generated_concepts", [])
operator_name = "Principle 1: Segmentation"
for cpt in concepts_list:
if cpt.get("concept_order") == r["concept_order"]:
operator_name = cpt.get("triz_operator_applied", operator_name)
break
record = SyncPayloadRecord(
deployment_id=r["deployment_id"],
concept_id=r["concept_id"],
opportunity_id=r["opportunity_id"],
domain_id=r["domain_id"],
industry=r["industry"],
user_group=r["user_group"],
opp_title=r["opp_title"],
need_statement=r["need_statement"],
root_cause_summary=r["root_cause_summary"],
subsystem_name=subsystem_name,
functional_deficits=parsed_deficits,
technical_contradiction_id=tc_id,
improving_param=improving_param,
worsening_param=worsening_param,
physical_contradiction=decomp.get("physical_contradiction"),
operator_name=operator_name,
concept_headline=r["concept_headline"],
mechanism_type=r["mechanism_type"],
projected_roi_ratio=float(r["projected_roi_ratio"]),
technical_risk_tier=int(r["technical_risk_tier"]),
final_score=float(r["final_success_score"]),
deployment_status=r["deployment_status"]
)
records.append(record)
return records
def sync_batch_to_neo4j(self, batch: List[SyncPayloadRecord]):
"""Executes a batched, transactional UNWIND query in Neo4j."""
payload = [b.model_dump() for b in batch]
cypher_mutation = """
UNWIND $batch AS data
// 1. Domain Context Layer
MERGE (dom:Domain {id: data.domain_id})
ON CREATE SET
dom.industry = data.industry,
dom.user_group = data.user_group,
dom.created_at = datetime()
ON MATCH SET
dom.industry = data.industry,
dom.user_group = data.user_group
MERGE (opp:Opportunity {id: data.opportunity_id})
ON CREATE SET
opp.title = data.opp_title,
opp.need_statement = data.need_statement,
opp.root_cause_summary = data.root_cause_summary,
opp.created_at = datetime()
ON MATCH SET
opp.root_cause_summary = data.root_cause_summary
MERGE (dom)-[:OWNS_OPPORTUNITY]->(opp)
MERGE (sub:Subsystem {id: data.domain_id + '_' + data.subsystem_name})
ON CREATE SET
sub.name = data.subsystem_name,
sub.domain_id = data.domain_id
MERGE (opp)-[:MANIFESTS_IN]->(sub)
// 2. Functional Abstraction Layer
FOREACH (def IN data.functional_deficits |
MERGE (f:FunctionalDeficit {id: def.id})
ON CREATE SET
f.component = def.component_or_process,
f.failure_mode = def.failure_mode,
f.causal_mechanism = def.causal_mechanism,
f.verb = def.verb,
f.causal_topology = def.causal_topology
MERGE (sub)-[:EXHIBITS_DEFICIT]->(f)
)
// 3. Contradiction & Mechanism Layer
MERGE (tc:TechnicalContradiction {id: data.technical_contradiction_id})
ON CREATE SET
tc.improving_param = data.improving_param,
tc.worsening_param = data.worsening_param,
tc.physical_conflict = data.physical_contradiction
FOREACH (def IN data.functional_deficits |
MERGE (f_node:FunctionalDeficit {id: def.id})
MERGE (f_node)-[:GOVERNED_BY]->(tc)
)
MERGE (op:InventiveOperator {name: data.operator_name})
MERGE (tc)-[:RESOLVED_BY]->(op)
MERGE (cpt:ImprovementConcept {id: data.concept_id})
ON CREATE SET
cpt.headline = data.concept_headline,
cpt.mechanism_type = data.mechanism_type,
cpt.projected_roi = data.projected_roi_ratio,
cpt.risk_tier = data.technical_risk_tier,
cpt.final_score = data.final_score,
cpt.status = data.deployment_status
ON MATCH SET
cpt.final_score = data.final_score,
cpt.status = data.deployment_status
MERGE (cpt)-[:VALIDATED_BY]->(opp)
MERGE (cpt)-[:APPLIES_OPERATOR]->(op)
"""
with self.driver.session() as session:
session.execute_write(lambda tx: tx.run(cypher_mutation, batch=payload))
def update_sqlite_watermarks(self, batch: List[SyncPayloadRecord]):
"""Updates the local synchronization ledger in SQLite."""
with sqlite3.connect(self.sqlite_path) as conn:
for b in batch:
eval_count = conn.execute(
"SELECT COUNT(*) FROM milestone_evaluations WHERE deployment_id = ?;",
(b.deployment_id,)
).fetchone()[0]
conn.execute("""
INSERT INTO neo4j_sync_ledger (deployment_id, synced_eval_count, last_final_score, synced_at)
VALUES (?, ?, ?, CURRENT_TIMESTAMP)
ON CONFLICT(deployment_id) DO UPDATE SET
synced_eval_count = excluded.synced_eval_count,
last_final_score = excluded.last_final_score,
synced_at = CURRENT_TIMESTAMP;
""", (b.deployment_id, eval_count, b.final_score))
conn.commit()
def run_sync_cycle(self) -> int:
"""Polls, batches, writes to Neo4j, and updates SQLite ledger."""
pending = self.extract_pending_sync_records()
if not pending:
return 0
print(f"[*] Found {len(pending)} pending deployment lineages to sync to Neo4j...")
self.sync_batch_to_neo4j(pending)
self.update_sqlite_watermarks(pending)
print(f"[✓] Successfully synced {len(pending)} deployment records to Neo4j.")
return len(pending)
def start_polling_daemon(self):
"""Continuous background sync loop."""
print(f"Starting SQLite -> Neo4j Sync Daemon against {self.sqlite_path}...")
print(f"Targeting Neo4j at {self.neo4j_uri} (Poll Interval: {self.poll_interval_sec}s)")
try:
while True:
self.run_sync_cycle()
time.sleep(self.poll_interval_sec)
except KeyboardInterrupt:
print("\nShutting down sync daemon safely.")
finally:
self.driver.close()
# ---------------------------------------------------------------------
# 3. CLI Entry Point
# ---------------------------------------------------------------------
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Sync SQLite Milestone Evaluations into Neo4j Graph")
parser.add_argument("--db", type=str, default="pipeline.db", help="Path to local SQLite file")
parser.add_argument("--neo4j-uri", type=str, default="bolt://localhost:7687", help="Neo4j connection URI")
parser.add_argument("--neo4j-user", type=str, default="neo4j", help="Neo4j username")
parser.add_argument("--neo4j-password", type=str, default="password", help="Neo4j password")
parser.add_argument("--once", action="store_true", help="Run a single sync batch and exit")
parser.add_argument("--interval", type=float, default=5.0, help="Polling interval in seconds")
args = parser.parse_args()
service = SQLiteGraphSyncService(
sqlite_path=args.db,
neo4j_uri=args.neo4j_uri,
neo4j_auth=(args.neo4j_user, args.neo4j_password),
poll_interval_sec=args.interval
)
if args.once:
synced = service.run_sync_cycle()
print(f"Single sync run complete. Synced items: {synced}")
else:
service.start_polling_daemon()
Key Operational Characteristics
State Watermarking (
neo4j_sync_ledger): Instead of rewriting unchanged records on every poll, the daemon creates a local watermark table in SQLite. It triggers synchronization only when a deployment records new evaluations (current_eval_count > last_synced_eval_count) or when the multi-horizon composite score shifts. Metadata Deconstruction: The script parses
generation_metadata['root_cause_decomposition']from Step 2 andstep_3_generated_conceptsfrom Step 3, converting nested JSON into discrete FunctionalDeficit,TechnicalContradiction, andInventiveOperatorgraph nodes.Idempotent Cypher Batches: The entire payload is written using
UNWIND $batch AS datawith explicitMERGEandON MATCH / ON CREATE SETclauses, avoiding duplicate relationships and supporting concurrent execution.
Would you like to write the FastRP topological embedding script using Neo4j Graph Data Science (GDS) to compute similarity scores between these newly synced functional deficit nodes?
Show the exact Cypher queries to query FastRP structural embeddings and find isomorphic subgraphs with cosine similarity thresholds.
To detect isomorphic problem-solution dynamics across different industries, Neo4j Graph Data Science (GDS) utilizes FastRP (Fast Random Projection). By projecting the structural layer (Subsystem, FunctionalDeficit, TechnicalContradiction, InventiveOperator), FastRP maps the multi-hop relational topology into a continuous vector space where graph position and edge patterns dictate proximity rather than industry vocabulary.
1. In-Memory Graph Projection
Project the domain-agnostic functional and contradiction layer as an undirected bipartite/multipartite graph into the GDS graph catalog:
// Drop existing projection if it exists
CALL gds.graph.drop('isomorphismGraph', false) YIELD graphName;
// Project the structural abstraction subgraph
CALL gds.graph.project(
'isomorphismGraph',
{
Subsystem: { label: 'Subsystem' },
FunctionalDeficit: { label: 'FunctionalDeficit' },
TechnicalContradiction: { label: 'TechnicalContradiction' },
InventiveOperator: { label: 'InventiveOperator' }
},
{
EXHIBITS_DEFICIT: { type: 'EXHIBITS_DEFICIT', orientation: 'UNDIRECTED' },
GOVERNED_BY: { type: 'GOVERNED_BY', orientation: 'UNDIRECTED' },
RESOLVED_BY: { type: 'RESOLVED_BY', orientation: 'UNDIRECTED' }
}
)
YIELD graphName, nodeCount, relationshipCount, projectMillis;
2. Compute and Write Back FastRP Embeddings
Compute 64-dimensional structural embeddings using intermediate hop weights to capture local (1-hop) and structural (2- to 3-hop) contradiction topologies, then write the vectors back to the FunctionalDeficit nodes:
CALL gds.fastRP.write('isomorphismGraph', {
writeProperty: 'structuralEmbedding',
embeddingDimension: 64,
iterationWeights: [0.0, 1.0, 0.7, 0.4], // Hop 0=0 (no self-loops), 1=direct, 2=contradiction, 3=operator
nodeSelfLoopWeight: 0.0
})
YIELD nodePropertiesWritten, computeMillis, writeMillis;
3. Create Vector Index for Fast Subgraph Retrieval
Build a native Neo4j vector index over the FunctionalDeficit structural embeddings using Cosine similarity:
CREATE VECTOR INDEX deficit_structural_embeddings IF NOT EXISTS
FOR (f:FunctionalDeficit) ON (f.structuralEmbedding)
OPTIONS {
indexConfig: {
`vector.dimensions`: 64,
`vector.similarity_function`: 'cosine'
}
};
4. Cross-Domain Subgraph Isomorphism Query (Filtered Cosine Threshold)
This query takes verified, high-scoring concepts ($\ge 0.70$)
// 1. Identify validated high-performing source deployments
MATCH (source_dom:Domain)-[:OWNS_OPPORTUNITY]->(source_opp:Opportunity)
-[:MANIFESTS_IN]->(source_sub:Subsystem)-[:EXHIBITS_DEFICIT]->(f_src:FunctionalDeficit)
MATCH (concept:ImprovementConcept)-[:VALIDATED_BY]->(source_opp)
MATCH (concept)-[:APPLIES_OPERATOR]->(op:InventiveOperator)
WHERE concept.final_score >= 0.70
AND f_src.structuralEmbedding IS NOT NULL
// 2. Query topologically analogous deficits in other domains using native vector search
CALL db.index.vector.queryNodes('deficit_structural_embeddings', 10, f_src.structuralEmbedding)
YIELD node AS f_target, score AS cosineSimilarity
// 3. Filter cross-domain boundary and apply similarity threshold
WHERE cosineSimilarity >= 0.85
AND f_target <> f_src
// 4. Resolve the target domain context
MATCH (target_sub:Subsystem)-[:EXHIBITS_DEFICIT]->(f_target)
MATCH (target_opp:Opportunity)-[:MANIFESTS_IN]->(target_sub)
MATCH (target_dom:Domain)-[:OWNS_OPPORTUNITY]->(target_opp)
WHERE target_dom.id <> source_dom.id
// 5. Retrieve governed contradiction parameters for both sides
OPTIONAL MATCH (f_src)-[:GOVERNED_BY]->(tc_src:TechnicalContradiction)
OPTIONAL MATCH (f_target)-[:GOVERNED_BY]->(tc_tgt:TechnicalContradiction)
RETURN DISTINCT
source_dom.industry AS source_industry,
source_dom.user_group AS source_user_group,
source_opp.title AS source_validated_problem,
concept.headline AS proven_concept,
concept.mechanism_type AS proven_mechanism,
concept.final_score AS source_score,
op.name AS transferrable_operator,
round(cosineSimilarity, 4) AS structural_similarity,
target_dom.id AS target_domain_id,
target_dom.industry AS target_industry,
target_dom.user_group AS target_user_group,
target_opp.title AS isomorphic_target_opportunity,
f_target.failure_mode AS target_friction_point,
tc_tgt.improving_param + ' vs ' + tc_tgt.worsening_param AS target_contradiction
ORDER BY structural_similarity DESC, source_score DESC
LIMIT 10;
5. GDS Pure Stream Query (Ad-Hoc Pairwise Matrix without Vector Index)
If querying ad-hoc before creating an index, run gds.similarity.cosine.stream directly over the FastRP properties:
MATCH (f1:FunctionalDeficit)<-[:EXHIBITS_DEFICIT]-(:Subsystem)<-[:MANIFESTS_IN]-(:Opportunity)<-[:OWNS_OPPORTUNITY]-(d1:Domain)
MATCH (f2:FunctionalDeficit)<-[:EXHIBITS_DEFICIT]-(:Subsystem)<-[:MANIFESTS_IN]-(:Opportunity)<-[:OWNS_OPPORTUNITY]-(d2:Domain)
WHERE d1.id < d2.id // Avoid symmetric duplicates and self-comparison
AND f1.structuralEmbedding IS NOT NULL
AND f2.structuralEmbedding IS NOT NULL
WITH f1, f2, d1, d2,
gds.similarity.cosine(f1.structuralEmbedding, f2.structuralEmbedding) AS similarity
WHERE similarity >= 0.85
MATCH (c:ImprovementConcept)-[:VALIDATED_BY]->(:Opportunity)-[:MANIFESTS_IN]->(:Subsystem)-[:EXHIBITS_DEFICIT]->(f1)
WHERE c.final_score >= 0.70
RETURN
d1.industry AS validated_source_industry,
c.headline AS source_proven_solution,
c.mechanism_type AS transferred_mechanism,
d2.industry AS candidate_target_industry,
f2.failure_mode AS target_functional_deficit,
round(similarity, 4) AS isomorphic_cosine_similarity
ORDER BY isomorphic_cosine_similarity DESC
LIMIT 10;
Would you like to build an automated HTML dashboard or Markdown summary generator that exports the complete opportunity portfolio and Pareto frontier upon loop termination?
How can I generate a clean Markdown or HTML summary report visualizing the opportunity portfolio across stages and domains?
Your HTML dashboard and Markdown reports are ready
portfolio_report
HTML
portfolio_report
MD
generate_portfolio_report
PY
To evaluate the portfolio across its lifecycle, the reporting engine aggregates data across three dimensions:
Domain Bandit Allocation: Tracks posterior exploration/exploitation parameters (alpha, beta, mathbb{E}[theta], sigma^2) to verify which sectors are yielding returns
. Funnel Gating Matrix: Visualizes the progression of initiatives from
IDENTIFIED-->ANALYZED-->IMPROVED-->DEPLOYED, identifying where structural or regulatory bottlenecks (ABANDONED) occur. Pareto Frontier: Ranks improvement concepts by multi-horizon final success score against projected ROI ratio and technical risk tier
.
Python Report Generator (generate_portfolio_report.py)
"""
Opportunity Portfolio Report Generator (`generate_portfolio_report.py`):
Queries the SQLite innovation pipeline database and produces a self-contained,
high-density HTML executive dashboard and companion Markdown report.
"""
import os
import json
import sqlite3
import argparse
from typing import List, Dict, Any
class PortfolioReportGenerator:
def __init__(self, db_path: str = "pipeline.db"):
self.db_path = db_path
self.conn = sqlite3.connect(self.db_path)
self.conn.row_factory = sqlite3.Row
def get_summary_metrics(self) -> Dict[str, Any]:
cur = self.conn.cursor()
total_domains = cur.execute("SELECT COUNT(*) FROM domains;").fetchone()[0]
total_opps = cur.execute("SELECT COUNT(*) FROM opportunities;").fetchone()[0]
deployed_count = cur.execute("SELECT COUNT(*) FROM opportunities WHERE pipeline_stage = 'DEPLOYED';").fetchone()[0]
abandoned_count = cur.execute("SELECT COUNT(*) FROM opportunities WHERE pipeline_stage = 'ABANDONED';").fetchone()[0]
avg_score_row = cur.execute("SELECT AVG(final_success_score) FROM v_deployment_scores;").fetchone()[0]
avg_score = round(avg_score_row, 3) if avg_score_row is not None else 0.0
return {
"total_domains": total_domains,
"total_opps": total_opps,
"deployed_count": deployed_count,
"abandoned_count": abandoned_count,
"avg_success_score": avg_score
}
def get_domains_data(self) -> List[Dict[str, Any]]:
cur = self.conn.cursor()
rows = cur.execute("""
SELECT
d.domain_id,
d.industry,
d.user_group,
d.alpha_prior,
d.beta_prior,
d.total_trials,
d.rolling_success_avg,
ROUND(d.alpha_prior / (d.alpha_prior + d.beta_prior), 4) AS expected_mean,
ROUND((d.alpha_prior * d.beta_prior) / (((d.alpha_prior + d.beta_prior) * (d.alpha_prior + d.beta_prior)) * (d.alpha_prior + d.beta_prior + 1.0)), 5) AS variance
FROM domains d
ORDER BY expected_mean DESC;
""").fetchall()
return [dict(r) for r in rows]
def get_stage_distribution(self) -> List[Dict[str, Any]]:
cur = self.conn.cursor()
rows = cur.execute("""
SELECT
d.industry,
o.domain_id,
SUM(CASE WHEN o.pipeline_stage = 'IDENTIFIED' THEN 1 ELSE 0 END) AS count_identified,
SUM(CASE WHEN o.pipeline_stage = 'ANALYZED' THEN 1 ELSE 0 END) AS count_analyzed,
SUM(CASE WHEN o.pipeline_stage = 'IMPROVED' THEN 1 ELSE 0 END) AS count_improved,
SUM(CASE WHEN o.pipeline_stage = 'DEPLOYED' THEN 1 ELSE 0 END) AS count_deployed,
SUM(CASE WHEN o.pipeline_stage = 'ABANDONED' THEN 1 ELSE 0 END) AS count_abandoned,
COUNT(o.opportunity_id) AS total_in_domain
FROM opportunities o
JOIN domains d ON o.domain_id = d.domain_id
GROUP BY o.domain_id
ORDER BY count_deployed DESC, total_in_domain DESC;
""").fetchall()
return [dict(r) for r in rows]
def get_pareto_concepts(self) -> List[Dict[str, Any]]:
cur = self.conn.cursor()
rows = cur.execute("""
SELECT
c.concept_id,
c.opportunity_id,
c.headline,
c.mechanism_type,
c.projected_roi_ratio,
c.technical_risk_tier,
c.selected_for_impl,
o.title AS opp_title,
d.industry,
v.final_success_score,
v.score_30d,
v.score_90d,
v.score_360d,
v.penalty
FROM improvement_concepts c
JOIN opportunities o ON c.opportunity_id = o.opportunity_id
JOIN domains d ON o.domain_id = d.domain_id
LEFT JOIN v_deployment_scores v ON c.concept_id = v.concept_id
ORDER BY v.final_success_score DESC NULLS LAST, c.projected_roi_ratio DESC;
""").fetchall()
return [dict(r) for r in rows]
def generate_html(self, output_filepath: str = "portfolio_report.html") -> str:
metrics = self.get_summary_metrics()
domains = self.get_domains_data()
stages = self.get_stage_distribution()
concepts = self.get_pareto_concepts()
domain_rows = "".join([
f"""<tr>
<td style="font-weight:600; font-family:monospace; color:#38bdf8;">{d['domain_id']}</td>
<td>{d['industry']}</td>
<td style="color:#94a3b8;">{d['user_group']}</td>
<td style="text-align:right;">{d['alpha_prior']:.2f}</td>
<td style="text-align:right;">{d['beta_prior']:.2f}</td>
<td style="text-align:right; font-weight:bold; color:{'#4ade80' if d['expected_mean'] >= 0.65 else '#fbbf24' if d['expected_mean'] >= 0.45 else '#f87171'};">{d['expected_mean']:.3f}</td>
<td style="text-align:right; font-family:monospace; color:#cbd5e1;">{d['variance']:.5f}</td>
<td style="text-align:right;">{d['total_trials']}</td>
</tr>""" for d in domains
])
stage_rows = "".join([
f"""<tr>
<td style="font-weight:600; color:#f1f5f9;">{s['industry']}</td>
<td style="font-family:monospace; color:#94a3b8;">{s['domain_id']}</td>
<td style="text-align:center;"><span class="badge badge-identified">{s['count_identified']}</span></td>
<td style="text-align:center;"><span class="badge badge-analyzed">{s['count_analyzed']}</span></td>
<td style="text-align:center;"><span class="badge badge-improved">{s['count_improved']}</span></td>
<td style="text-align:center;"><span class="badge badge-deployed">{s['count_deployed']}</span></td>
<td style="text-align:center;"><span class="badge badge-abandoned">{s['count_abandoned']}</span></td>
<td style="text-align:right; font-weight:bold;">{s['total_in_domain']}</td>
</tr>""" for s in stages
])
concept_rows = "".join([
f"""<tr>
<td style="font-weight:600; color:#f1f5f9;">{c['headline']}</td>
<td style="font-size:0.85em; color:#94a3b8;">{c['opp_title']}</td>
<td><span class="mechanism-tag">{c['mechanism_type']}</span></td>
<td style="text-align:right; font-weight:bold; color:#38bdf8;">{c['projected_roi_ratio']:.1f}x</td>
<td style="text-align:center;"><span class="risk-tier tier-{c['technical_risk_tier']}">T{c['technical_risk_tier']}</span></td>
<td style="text-align:right; font-weight:bold; color:{'#4ade80' if (c['final_success_score'] or 0) >= 0.70 else '#f87171'};">{f"{c['final_success_score']:.3f}" if c['final_success_score'] is not None else '<span style="color:#64748b;">Unstaged</span>'}</td>
<td style="text-align:center;">{'<span style="color:#4ade80; font-weight:bold;">YES</span>' if c['selected_for_impl'] else '<span style="color:#64748b;">NO</span>'}</td>
</tr>""" for c in concepts
])
html_template = f"""<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Innovation Pipeline Portfolio Dashboard</title>
<style>
:root {{
--bg-base: #0f172a;
--bg-card: #1e293b;
--bg-card-border: #334155;
--text-primary: #f8fafc;
--text-muted: #94a3b8;
--accent: #38bdf8;
--success: #4ade80;
--warning: #fbbf24;
--danger: #f87171;
}}
body {{
font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, Helvetica, Arial, sans-serif;
background-color: var(--bg-base);
color: var(--text-primary);
margin: 0;
padding: 30px;
line-height: 1.5;
}}
.container {{ max-width: 1350px; margin: 0 auto; }}
.header {{
display: flex;
justify-content: space-between;
align-items: center;
border-bottom: 1px solid var(--bg-card-border);
padding-bottom: 20px;
margin-bottom: 30px;
}}
h1 {{ margin: 0; font-size: 1.8em; letter-spacing: -0.02em; color: #fff; }}
.timestamp {{ color: var(--text-muted); font-size: 0.9em; }}
.kpi-grid {{
display: grid;
grid-template-columns: repeat(5, 1fr);
gap: 16px;
margin-bottom: 30px;
}}
.kpi-card {{
background: var(--bg-card);
border: 1px solid var(--bg-card-border);
border-radius: 8px;
padding: 16px;
text-align: center;
}}
.kpi-title {{ font-size: 0.8em; text-transform: uppercase; letter-spacing: 0.05em; color: var(--text-muted); }}
.kpi-value {{ font-size: 1.9em; font-weight: 700; color: #fff; margin-top: 6px; }}
.section {{
background: var(--bg-card);
border: 1px solid var(--bg-card-border);
border-radius: 8px;
padding: 24px;
margin-bottom: 30px;
}}
h2 {{ margin-top: 0; font-size: 1.25em; border-bottom: 1px solid var(--bg-card-border); padding-bottom: 12px; }}
table {{ width: 100%; border-collapse: collapse; margin-top: 12px; font-size: 0.9em; }}
th, td {{ padding: 10px 12px; border-bottom: 1px solid #334155; text-align: left; }}
th {{ background-color: #0f172a; color: var(--text-muted); font-weight: 600; text-transform: uppercase; font-size: 0.75em; letter-spacing: 0.05em; }}
tr:hover {{ background-color: rgba(56, 189, 248, 0.03); }}
.badge {{ display: inline-block; padding: 2px 8px; border-radius: 4px; font-size: 0.8em; font-weight: 600; }}
.badge-identified {{ background: #334155; color: #cbd5e1; }}
.badge-analyzed {{ background: #1e3a8a; color: #93c5fd; }}
.badge-improved {{ background: #581c87; color: #e9d5ff; }}
.badge-deployed {{ background: #064e3b; color: #6ee7b7; }}
.badge-abandoned {{ background: #7f1d1d; color: #fca5a5; }}
.mechanism-tag {{ background: #0f172a; border: 1px solid #38bdf8; color: #38bdf8; padding: 2px 6px; border-radius: 4px; font-size: 0.8em; font-family: monospace; }}
.risk-tier {{ font-weight: 700; font-size: 0.85em; padding: 2px 6px; border-radius: 3px; }}
.tier-1, .tier-2 {{ background: #064e3b; color: #6ee7b7; }}
.tier-3 {{ background: #78350f; color: #fde68a; }}
.tier-4, .tier-5 {{ background: #7f1d1d; color: #fca5a5; }}
</style>
</head>
<body>
<div class="container">
<div class="header">
<div>
<h1>Innovation Pipeline Portfolio Dashboard</h1>
<div class="timestamp">Automated Stage-Gated Opportunity & Outcome Ledger</div>
</div>
<div style="text-align:right;">
<span class="badge badge-deployed" style="padding:6px 12px; font-size:0.9em;">Closed-Loop Autonomous Engine</span>
</div>
</div>
<div class="kpi-grid">
<div class="kpi-card">
<div class="kpi-title">Explored Domains</div>
<div class="kpi-value">{metrics['total_domains']}</div>
</div>
<div class="kpi-card">
<div class="kpi-title">Total Opportunities</div>
<div class="kpi-value">{metrics['total_opps']}</div>
</div>
<div class="kpi-card">
<div class="kpi-title">Active Deployments</div>
<div class="kpi-value" style="color:#4ade80;">{metrics['deployed_count']}</div>
</div>
<div class="kpi-card">
<div class="kpi-title">Filtered / Abandoned</div>
<div class="kpi-value" style="color:#f87171;">{metrics['abandoned_count']}</div>
</div>
<div class="kpi-card">
<div class="kpi-title">Portfolio Mean Yield</div>
<div class="kpi-value" style="color:#38bdf8;">{metrics['avg_success_score']:.3f}</div>
</div>
</div>
<div class="section">
<h2>1. Domain Bandit Allocation & Expected Yield (Thompson Priors)</h2>
<table>
<thead>
<tr>
<th>Domain Key</th>
<th>Sector</th>
<th>User Cohort</th>
<th style="text-align:right;">Alpha (α)</th>
<th style="text-align:right;">Beta (β)</th>
<th style="text-align:right;">Expected Mean (E[θ])</th>
<th style="text-align:right;">Variance (σ²)</th>
<th style="text-align:right;">Trials</th>
</tr>
</thead>
<tbody>
{domain_rows}
</tbody>
</table>
</div>
<div class="section">
<h2>2. Pipeline Stage Distribution by Sector</h2>
<table>
<thead>
<tr>
<th>Sector</th>
<th>Domain ID</th>
<th style="text-align:center;">Identified</th>
<th style="text-align:center;">Analyzed</th>
<th style="text-align:center;">Improved</th>
<th style="text-align:center;">Deployed</th>
<th style="text-align:center;">Abandoned</th>
<th style="text-align:right;">Total Pipeline</th>
</tr>
</thead>
<tbody>
{stage_rows}
</tbody>
</table>
</div>
<div class="section">
<h2>3. Concept Optimization Ledger & Frontier Ranking</h2>
<table>
<thead>
<tr>
<th>Concept Headline</th>
<th>Root Opportunity</th>
<th>Mechanism</th>
<th style="text-align:right;">Proj. ROI</th>
<th style="text-align:center;">Risk</th>
<th style="text-align:right;">Success Score</th>
<th style="text-align:center;">Staged for Impl</th>
</tr>
</thead>
<tbody>
{concept_rows}
</tbody>
</table>
</div>
</div>
</body>
</html>
"""
with open(output_filepath, "w") as f:
f.write(html_template)
return output_filepath
def generate_markdown(self, output_filepath: str = "portfolio_report.md") -> str:
metrics = self.get_summary_metrics()
domains = self.get_domains_data()
stages = self.get_stage_distribution()
concepts = self.get_pareto_concepts()
lines = [
"# Innovation Pipeline Portfolio Summary Report",
"",
"## 1. Executive Telemetry Overview",
f"- **Explored Domains:** {metrics['total_domains']}",
f"- **Total Opportunities Generated:** {metrics['total_opps']}",
f"- **Active Deployments:** {metrics['deployed_count']}",
f"- **Filtered / Abandoned:** {metrics['abandoned_count']}",
f"- **Portfolio Average Success Yield:** {metrics['avg_success_score']:.3f}",
"",
"## 2. Domain Bandit Allocation & Expected Yield",
"| Domain ID | Sector | User Group | Alpha (α) | Beta (β) | Expected Mean (E[θ]) | Variance (σ²) | Trials |",
"| :--- | :--- | :--- | :---: | :---: | :---: | :---: | :---: |"
]
for d in domains:
lines.append(f"| `{d['domain_id']}` | {d['industry']} | {d['user_group']} | {d['alpha_prior']:.2f} | {d['beta_prior']:.2f} | **{d['expected_mean']:.3f}** | {d['variance']:.5f} | {d['total_trials']} |")
lines.extend([
"",
"## 3. Pipeline Stage Distribution by Sector",
"| Sector | Domain ID | Identified | Analyzed | Improved | Deployed | Abandoned | Total |",
"| :--- | :--- | :---: | :---: | :---: | :---: | :---: | :---: |"
])
for s in stages:
lines.append(f"| {s['industry']} | `{s['domain_id']}` | {s['count_identified']} | {s['count_analyzed']} | {s['count_improved']} | {s['count_deployed']} | {s['count_abandoned']} | **{s['total_in_domain']}** |")
lines.extend([
"",
"## 4. Concept Optimization Ledger & Frontier Ranking",
"| Concept Headline | Opportunity | Mechanism Type | Proj. ROI | Risk Tier | Success Score | Staged? |",
"| :--- | :--- | :--- | :---: | :---: | :---: | :---: |"
])
for c in concepts:
score_str = f"**{c['final_success_score']:.3f}**" if c['final_success_score'] is not None else "*Unstaged*"
staged_str = "YES" if c['selected_for_impl'] else "NO"
lines.append(f"| {c['headline']} | {c['opp_title']} | `{c['mechanism_type']}` | {c['projected_roi_ratio']:.1f}x | T{c['technical_risk_tier']} | {score_str} | {staged_str} |")
md_content = "\n".join(lines)
with open(output_filepath, "w") as f:
f.write(md_content)
return output_filepath
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate Innovation Portfolio Summary Reports")
parser.add_argument("--db", type=str, default="pipeline.db", help="Path to SQLite database file")
parser.add_argument("--format", type=str, choices=["html", "md", "both"], default="both", help="Output format")
args = parser.parse_args()
generator = PortfolioReportGenerator(db_path=args.db)
if args.format in ["html", "both"]:
html_file = generator.generate_html("portfolio_report.html")
print(f"Generated HTML report: {html_file}")
if args.format in ["md", "both"]:
md_file = generator.generate_markdown("portfolio_report.md")
print(f"Generated Markdown report: {md_file}")
Execution
To compile the reports from your SQLite pipeline:
# Generate both HTML dashboard and Markdown document
python generate_portfolio_report.py --db pipeline.db --format both
The resulting portfolio_report.html renders an executive dashboard with KPI callouts, status badges, and color-coded score thresholds. It requires no external web servers or internet access to display.
Would you like to integrate this generator directly into run_autonomous_loop.py so the HTML dashboard opens automatically in your browser whenever early stopping triggers?
From <https://gemini.google.com/app/1817e44808e313f3> Google Gemini (3.8 Flash)
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