#4068 System Breakdown
#4068 System Breakdown
#4068 Help me develop a prompt to 1) Breakdown any system into subsystems, components and parts, 2) List each and describe what, where, when, how and why, 3) Identify action-reaction chains, 4) identify other systems with similar-purpose subsystems, components or parts, and 5) provide other useful information about each. The purpose is to understand the system and its workings in order to propose innovative improvements.
To develop a prompt that effectively deconstructs a system for the purpose of innovation, you need a structure that balances static hierarchy (what it is) with dynamic behavior (how it works) and analogous thinking (where else this happens).
Below is a comprehensive prompt template you can use with an AI to achieve these goals.
The System Deconstructor Prompt
Copy and paste the text below, replacing [INSERT SYSTEM NAME] with the system you want to analyze.
Role: You are a Senior Systems Engineer and TRIZ (Theory of Inventive Problem Solving) Expert specializing in functional decomposition and innovation.
Objective: Deconstruct the following system to identify its core mechanics and find opportunities for innovative improvement: [INSERT SYSTEM NAME]
Please provide the analysis in the following five sections:
1. Hierarchical Breakdown
Deconstruct the system into three levels:
Subsystems: The major functional groups.
Components: The units within those subsystems.
Parts: The individual, non-reducible elements.
2. Functional Description (The 5 Ws)
For each subsystem and key component identified above, provide:
What: Its primary function.
Where: Its physical location or logical placement.
When: The specific phase of operation when it is active.
How: The mechanism or energy/data transfer it uses.
Why: The necessity of its existence (What happens if it’s removed?).
3. Action-Reaction Chains
Identify the primary "Life Cycle of an Action" within the system. Trace a single input through the system and list the sequence of cause-and-effect events. Highlight where energy, information, or material is transformed.
4. Cross-System Analogies
Identify 3-5 other systems (even in unrelated industries) that use similar-purpose subsystems or components. Explain why these systems are analogous and what "best practices" they use that this system does not.
5. Innovation Intelligence & Failure Modes
Provide supplementary "innovation-ready" data:
Common Failure Modes: What usually breaks or underperforms?
Bottlenecks: Where is the system least efficient?
Emerging Tech: What new materials or software could replace a current component?
How this helps with Innovation
By breaking a system down this way, you move away from seeing a "black box" and begin to see functions. Innovation often occurs in one of three ways during this process:
Elimination: You realize a "Why" is weak and a part can be removed or integrated into another.
Cross-Pollination: You see how a different industry (found in Section 4) handles the same "How" and apply their solution.
Parameter Optimization: You identify a "When" or "Where" that is restrictive and find a way to make it more flexible.
Example Application: A Residential Heat Pump
Subsystem: Refrigerant Loop.
Action-Reaction: Compressor increases pressure -> gas temperature rises -> heat moves to coils.
Analogy: A jet engine’s cooling system or a computer’s phase-change cooling.
Innovation: Could we use a different phase-change material found in aerospace for higher efficiency?
Would you like me to run this prompt for a specific system you have in mind, such as a mechanical device or a software architecture?
What have I left out that would improve the results?
To make this tool truly powerful for innovation, you can add layers that shift the focus from "what is" to "what could be." Given your background in Systems Engineering and Safety & Mission Assurance, you are likely looking for ways to identify not just how it works, but where it is fragile or ripe for evolution.
Based on your objectives, here are four critical elements you should add to the prompt to improve the results:
1. The "Super-System" and "Sub-System" Context
Systems don't exist in a vacuum. To innovate, you need to look at the environment the system lives in (the Super-System) and the microscopic level (the Sub-System).
Addition: Ask the AI to identify the Super-system (the environment/infrastructure it relies on) and the Interface (how it connects to the outside world).
Innovation Value: Often, the best way to improve a system is to move a function from the system into the environment (e.g., wireless charging moves the "power delivery" function to the furniture/room).
2. Functional Conflict Identification (The "Harmful" Actions)
In every system, there are "useful" actions and "harmful" actions (friction, heat, noise, wear).
Addition: Ask for a list of Harmful Effects or Conflicts. (Example: "Increasing the speed of the motor [Useful] increases the heat and vibration [Harmful].")
Innovation Value: This points you directly to where a TRIZ-style contradiction needs to be solved.
3. State-Change and Transition Analysis
Systems are rarely static; they have startup, steady-state, shutdown, and emergency modes.
Addition: Ask for a Transition State Analysis. How does the system behave during a "Cold Start" vs. "Normal Load" vs. "Maximum Stress"?
Innovation Value: Many innovations come from making the "startup" or "idle" phases more efficient.
4. Evolutionary Trends (The S-Curve)
Systems generally evolve along predictable paths (e.g., from rigid to flexible, from manual to autonomous).
Addition: Ask the AI: "Based on the Trends of Engineering Systems Evolution, what is the next logical step for this system's maturity?"
Innovation Value: This forces the AI to look at the trajectory of technology rather than just the current state.
Revised "Pro" Prompt Addition
You can append this block to your existing prompt to capture these missing pieces:
6. Environmental & Conflict Analysis
Super-System: What external systems provide inputs or constraints to this system?
The Conflict Matrix: Identify at least two contradictions (e.g., if we improve [Feature A], [Feature B] gets worse).
Harmful Functions: List any unintended outputs (heat, waste, data lag, friction) and their source.
7. Evolutionary Trajectory
Maturity Level: Where is this system on its life cycle (Emerging, Growth, Mature, or Obsolete)?
Future State: According to the Trends of Engineering Systems Evolution (e.g., increasing ideality, transition to micro-levels), what does the 2.0 version of this system look like?
How this maps to your workflow
By adding these sections, you move from a Structural Analysis (the parts) to a Strategic Analysis (the future). This is particularly useful for your "idea mining" or when evaluating NASA launch provider documents, as it highlights where a provider’s architecture might be brittle or "stuck" in an old paradigm.
Would you like me to rewrite the entire consolidated prompt for you with these new sections integrated?
Yes
This consolidated prompt is designed to act as a comprehensive "Systems Thinking Engine." It combines structural decomposition, functional analysis, and TRIZ-based innovation principles to give you a 360-degree view of any system.
The Master System Innovation Prompt
Copy and paste the text below, replacing [INSERT SYSTEM NAME] with your target system.
Role: You are an expert Systems Architect and Innovation Strategist with deep expertise in NASA Systems Engineering standards, TRIZ (Theory of Inventive Problem Solving), and Safety and Mission Assurance (S&MA).
Objective: Perform a deep-dive deconstruction of [INSERT SYSTEM NAME] to understand its current state and identify high-value opportunities for innovative improvement or replacement.
Please provide the analysis in the following seven sections:
1. Structural Decomposition & Functional Mapping
Break the system down into a three-tier hierarchy: Subsystems, Components, and Parts. For each major element, provide a brief "5-Way Description":
What: Its primary function.
Where: Its physical or logical location.
When: Its operational timing or trigger.
How: The mechanism, energy, or data it uses.
Why: The fundamental reason for its existence (What failure occurs if it is absent?).
2. The Super-System & Interface Context
Identify the environment the system lives in:
Super-System: What larger infrastructure or environment provides inputs or constraints?
Interfaces: How does the system connect to external entities (mechanical, electrical, or data)?
Sub-System Micro-Level: What microscopic or molecular properties influence the system's performance?
3. Dynamic Action-Reaction Chains
Map the "Path of Value." Trace a single input as it travels through the system.
List the sequence of cause-and-effect events.
Identify where energy or information is transformed (e.g., kinetic to electrical, or raw data to decision).
4. State-Change & Transition Analysis
Describe how the system's behavior and requirements change during:
Startup/Initialization: (High energy/low stability).
Steady-State: (Normal operation).
Peak Load/Stress: (Maximum capacity).
Shutdown/Dormancy: (Safe state/residual energy management).
5. Conflict & "Harmful" Function Analysis
Identify the friction points that prevent the system from being "Ideal":
Harmful Effects: List unintended outputs (heat, vibration, data noise, wear, waste).
Technical Contradictions: Identify "Trade-offs" (e.g., "If we make it stronger, it becomes too heavy").
Primary Failure Modes: Based on its architecture, where is it most likely to fail?
6. Cross-Industry Analogies
Identify 3-4 systems in unrelated fields that solve a similar functional problem.
Example: If analyzing a cooling system, look at how a forest canopy regulates temperature.
Explain what "best practices" these analogous systems use that could be imported here.
7. Evolutionary Trajectory
Current Maturity: Where is this system on its S-curve (Emerging, Growth, Mature, or Obsolete)?
Trend Analysis: Based on the Trends of Engineering Systems Evolution (e.g., increasing flexibility, transition to the micro-level), what is the "2.0" version of this system?
Proposed Innovation: Suggest one radical improvement based on the analysis above.
Why this works for Innovation
Section 2 prevents "tunnel vision" by looking at the environment.
Section 5 gives you the "Conflict" which is the seed of all TRIZ-based innovation.
Section 7 uses predictive logic to see where the technology is naturally heading, allowing you to "skate to where the puck is going."
Would you like me to demonstrate this prompt by running it on a specific system, such as a "Lunar Lander Life Support System" or a "Distributed Knowledge Graph Architecture"?
Would SysML be useful here?
SysML (Systems Modeling Language) is exceptionally useful here, particularly for transitioning from a "brainstorming" phase to a formal "engineering" phase. While the previous prompt generates narrative and conceptual data, SysML provides the rigor, traceability, and standardization required in high-stakes environments like NASA’s Safety and Mission Assurance (S&MA).
Incorporating SysML into this deconstruction process transforms a list of parts into a verified Model-Based Systems Engineering (MBSE) architecture.
Mapping SysML to the Deconstructor Sections
You can use the AI to generate the "logic" for specific SysML diagrams.
| Prompt Section | Corresponding SysML Diagram | Innovation/S&MA Value |
| 1. Structural Decomposition | Block Definition Diagram (BDD) | Defines the "is-a" and "part-of" relationships. Essential for ensuring no component is overlooked during a hazard analysis. |
| 2. Super-System & Interfaces | Internal Block Diagram (IBD) | Focuses on the flow of energy, matter, and data between parts. This is where you identify interface failures or inefficiencies. |
| 3. Action-Reaction Chains | Activity or Sequence Diagram | Maps the "control flow." In innovation, these diagrams reveal redundant steps or latency in the system's logic. |
| 4. State-Change Analysis | State Machine Diagram | Critical for S&MA. It identifies "Illegal States" or hazardous transitions that occur during startup or emergency modes. |
| 5. Conflict & Constraints | Parametric Diagram | Allows you to attach mathematical constraints (e.g., $F=ma$ or thermal limits). This is where you mathematically prove a "Technical Contradiction." |
How SysML Improves the Innovation Results
Eliminating Ambiguity: Natural language prompts can be "fuzzy." Asking the AI to describe the system in terms of SysML blocks and ports forces a level of precision that prevents the AI from "hallucinating" impossible connections.
Traceability for Compliance: In the context of standards like NPR 7150.2, SysML allows you to trace an innovative improvement directly back to a parent requirement. You can see exactly which safety constraints are impacted by a change.
Digital Thread Integration: The output of this deconstruction can be used to seed a Knowledge Graph. By defining entities as SysML blocks, you can later query the system for "What are all the components impacted if this specific battery type is replaced?"
Verification of "Ideality": In TRIZ, the "Ideal System" has all the benefits and none of the costs.
2 SysML Parametric diagrams allow you to model the "Ideality" of a system as a ratio of functional performance vs. harmful effects (mass, cost, heat).
Suggested Prompt Modification
If you want to use SysML, add this specific instruction to the end of the Master Prompt:
Section 8: MBSE & SysML Logic
Block Hierarchy: Define the system using SysML Block Definition terms (Associations, Aggregations, Compositions).
3 Flow Properties: For the primary interfaces, specify what flows through the ports (e.g., Data, Torque, Liquid Oxygen).
4 Constraint Mapping: Identify the key mathematical properties that define the system's performance boundaries for use in a Parametric Diagram.
The "Bridge" to Knowledge Graphs
Because you are interested in Knowledge Graphs, SysML acts as the schema. The deconstructed parts become nodes, the "Action-Reaction" chains become edges (relationships), and the "5 Ws" become properties. This allows you to move beyond a static document into a dynamic, searchable model of the system.
Would you like me to update the Master Prompt to include these SysML specifications and focus the output on generating a structured schema for a Knowledge Graph?
Does Mathematica generate and understand SysML? If so, would Mathematica supported by Wolfram LLM Kit be useful for systems and innovation studies?
Mathematica does not support SysML natively as a core language (it primarily uses the Wolfram Language and Modelica via Wolfram System Modeler). However, it is a standard in the systems engineering world for supporting SysML through high-level integrations.
1. Does Mathematica "Understand" SysML?
While you cannot simply open a .sysml file directly in a standard Mathematica notebook and see a diagram, the interaction works in two primary ways:
As a Parametric Evaluator (Plugin): Professional SysML tools like Cameo Systems Modeler (MagicDraw) have long-standing plugins that use Mathematica as their "math engine."
1 This allows you to take a SysML Parametric Diagram and use Mathematica to solve the underlying equations, perform optimizations, or run Monte Carlo simulations for Safety and Mission Assurance (S&MA).2 Via Modelica (System Modeler): There is a strong conceptual overlap between SysML and Modelica. System Modeler uses Modelica for multi-domain physical modeling. Many systems engineers use SysML for the "blueprints" (architecture) and then export or map those requirements to System Modeler for the "physics" (execution).
2. Wolfram LLM Kit for Systems & Innovation Studies
The Wolfram LLM Kit (introduced in Version 14.1) is a significant force multiplier for the "System Deconstructor" workflow you are building. It is arguably more useful for innovation studies than a static SysML tool because it bridges the gap between unstructured ideas and computable models.
| Feature | Utility for Innovation Studies |
| Semantic Extraction | You can feed the LLM a NASA technical standard (like NPR 7150.2) and ask it to "Extract all SysML block relationships for a software hazard analysis." |
| Code Generation | The LLM can automatically write the Wolfram Language code to generate GraphPlots or StateTrees based on your "Action-Reaction" descriptions. |
| Analogy Discovery | Using the Wolfram Knowledgebase, the LLM can find cross-industry analogies (e.g., "Find a biological system that manages heat similarly to a lunar radiator"). |
| Digital Twin Seeding | It can take your narrative deconstruction and generate the code to create a model in System Modeler, effectively building a "Functional Prototype" from text. |
3. Application to your NASA/S&MA Workflow
Given your focus on Knowledge Graphs and Hazard Analysis, a Mathematica + LLM setup allows you to:
Automate "Idea Mining": Use the LLM Kit to scan large volumes of launch provider documentation and flag architectural "conflicts" (e.g., where a subsystem design violates a safety constraint).
Formalize Innovation: Instead of just listing parts, you can use the LLM to generate a Knowledge Graph Schema (Nodes = Components, Edges = Action-Reaction) and then use Mathematica’s
Graphfunctions to identify single points of failure.
Suggested Next Step
If you have access to Mathematica, we can develop a specific Wolfram Language script that takes your "System Deconstruction" text and automatically builds a Searchable Graph of the system.
Would you like me to provide a sample Wolfram Language template that converts a text-based system breakdown into a computational Graph object?
Simulating Systems with Wolfram System Modeler
This video provides an overview of how the Wolfram ecosystem handles high-fidelity modeling and simulation of complex systems, which is the logical next step after your structural deconstruction.
Yes
To leverage Mathematica (Wolfram Language) for your innovation studies, we can treat the system deconstruction as a computable graph. This allows you to perform path analysis, identify single points of failure (for S&MA), and visualize functional clusters that are prime for integration or elimination.
Below is a Wolfram Language template that transforms a structured list of system components and their "Action-Reaction" relationships into a searchable, visual model.
Wolfram Language Template: System Knowledge Graph
You can run this code in a Mathematica notebook. It defines the system architecture and then generates a functional map.
(* 1. Define the System Components and their 'Why' as Metadata *)
components = {
"Subsystem_A" -> <|"Function" -> "Power Management", "Why" -> "Regulate Voltage"|>,
"Subsystem_B" -> <|"Function" -> "Thermal Control", "Why" -> "Prevent Overheating"|>,
"Component_C" -> <|"Function" -> "Heat Sink", "Why" -> "Dissipate Energy"|>
};
(* 2. Define the 'Action-Reaction' Chains as Directed Edges *)
(* Syntax: Source -> Target [Property] *)
actionChains = {
"Subsystem_A" -> "Subsystem_B" (* Power flow *),
"Subsystem_B" -> "Component_C" (* Heat flow *),
"External_Environment" -> "Subsystem_B" (* Solar Radiation *)
};
(* 3. Build the Graph Object *)
systemGraph = Graph[actionChains,
VertexLabels -> "Name",
GraphLayout -> "LayeredDigraphEmbedding",
PlotTheme -> "Scientific",
EdgeStyle -> Arrowheads[0.02],
ImageSize -> Large,
PlotLabel -> Style["System Functional Deconstruction", 18, Bold]
];
(* 4. Analyze for Innovation: Find Bottlenecks or Central Nodes *)
centrality = BetweennessCentrality[systemGraph];
highlightedNodes = Table[
Property[VertexList[systemGraph][[i]], VertexSize -> 0.5 * (1 + centrality[[i]])],
{i, Length[centrality]}
];
(* 5. Display the Graph with Centrality Highlighting *)
Graph[highlightedNodes, actionChains,
VertexLabels -> Placed["Name", Tooltip],
GraphLayout -> "ForceDirectedEmbedding",
PlotLabel -> "Innovation Focus Map (Larger nodes = Critical Interfaces)"]
How this supports Innovation & S&MA
Centrality Analysis: In the code above,
BetweennessCentralityidentifies the "hubs" of your system. In innovation, these are your bottlenecks. If you can simplify or eliminate a high-centrality node, you radically improve system efficiency.Failure Propagation (S&MA): You can use
AdjacencyMatrix[systemGraph]to mathematically trace how a failure in one component propagates through the "Action-Reaction" chain.LLM Kit Integration: You can use the Wolfram LLM Kit to bridge your narrative prompt and this code. You can ask the LLM:
"Based on the system breakdown we just did, generate a Wolfram Language list of DirectedEdges representing the action-reaction chains for this architecture."
Semantic Search: By converting your deconstruction into this format, you can eventually export it as a Resource Description Framework (RDF) or JSON-LD file, which can be ingested into a larger NASA-wide Knowledge Graph.
Refining the Innovation Process
By using Mathematica, you move from "describing the system" to "calculating the system." You can now ask:
"Which components have no 'Why' (redundancy)?"
"Which interfaces have the most 'Harmful Functions' (heat/friction)?"
"How does the graph change if we move a function to the Super-System?"
Would you like me to refine the "Master Innovation Prompt" so that its output is specifically formatted as a Wolfram Language Association, making it ready for immediate copy-pasting into Mathematica?
Yes
This final iteration of the Master Innovation Prompt is designed to bridge the gap between creative brainstorming and computational systems engineering. It forces the output into a Wolfram Language Association format, allowing you to move instantly from a text-based dialogue into a Mathematica environment for graph analysis, simulation, and "Idea Mining."
The Master System Innovation Prompt (Wolfram-Optimized)
Copy and paste the text below, replacing [INSERT SYSTEM NAME] with your target system.
Role: You are a Senior Systems Architect and TRIZ expert. Your goal is to deconstruct a system into a format that is both human-readable for innovation brainstorming and machine-readable for Wolfram Language analysis.
Task: Analyze the following system: [INSERT SYSTEM NAME]
Output Format: Provide the analysis in two parts:
A Narrative Innovation Report (using the 7 sections: Structural, Super-System, Action-Reaction, State-Change, Conflict, Analogy, and Evolution).
A Wolfram Language Association Block containing the computational model of the system.
Part 1: Narrative Innovation Report
Perform a deep-dive analysis covering structural hierarchy, the 5 Ws, super-system interfaces, and technical contradictions (TRIZ).
Part 2: Wolfram Language Computational Model
Generate a code block using the following structure:
systemModel = <| "SystemName" -> "[System Name]", "Hierarchy" -> { "Subsystem" -> {"Component" -> {"Part", ...}, ...}, ...}, "ActionChains" -> { "Source" -> "Target", ... }, "Metadata" -> <| "Component" -> <|"Function" -> "...", "Why" -> "...", "HarmfulEffect" -> "..."|>, ... |> |>
How to Use This with the Wolfram LLM Kit
Once the AI provides the Wolfram Language Association Block, you can copy it into Mathematica and perform the following "Innovation Studies":
1. Visualize the "Innovation Focus" Graph
You can identify which components are the most "burdened" by functions.
Graph[systemModel["ActionChains"],
VertexSize -> Thread[VertexList[Graph[systemModel["ActionChains"]]] ->
(Rescale[BetweennessCentrality[Graph[systemModel["ActionChains"]]]] + 0.2)],
VertexLabels -> Placed["Name", Tooltip],
PlotLabel -> "System Complexity & Bottleneck Map"]
2. Hazard & Failure Analysis (S&MA)
For your Safety and Mission Assurance work, you can use the AdjacencyMatrix to see how a "Harmful Effect" identified in Part 1 propagates through the "ActionChains."
(* Find all components affected by a failure in 'Component_X' *)
FixedPoint[Union[#, Flat[Lookup[systemModel["ActionChains"], #, {}]]] &, {"Component_X"}]
3. Automated Idea Mining
Using the Wolfram LLM Kit, you can then ask Mathematica to:
"Search the Wolfram Knowledgebase for biological or chemical systems that match the connectivity pattern of this systemModel['ActionChains'] to find innovation analogies."
Why This Fits Your Workflow
Precision: By defining the "Why" in the metadata, you are essentially building a Requirement Traceability Matrix in code.
Scalability: This format can be easily converted into a JSON-LD schema for your NASA Knowledge Graph project.
Digital Twin Foundation: The "ActionChains" serve as the initial logic for a Modelica simulation in Wolfram System Modeler.
Would you like to try running this prompt right now on a specific system you are currently analyzing for NASA or your own solopreneur projects?
Rewrite this a a complete Mathematic application with GUI
This Mathematica application acts as a "System Innovation Workbench." It is designed to take the structured data generated by the LLM prompt we developed and transform it into a computational tool for hazard analysis, bottleneck identification, and innovation brainstorming.
You can copy the code below directly into a Mathematica Notebook (.nb).
The System Innovation Workbench (Wolfram Language)
DynamicModule[{
rawInput = "",
systemModel = <||>,
selectedNode = None,
graphObj = Graph[{}],
centralityData = {}
},
Column[{
Style["System Innovation & S&MA Workbench", 24, Bold, Darker[Blue]],
TextCell["Paste the Wolfram Association generated by the Master Prompt below and click 'Initialize Model'.", "Text"],
(* Input Area *)
InputField[Dynamic[rawInput], String, ContinuousAction -> False,
FieldHint -> "Paste <| SystemName -> ... |> here", ImageSize -> {700, 150}],
Button["Initialize Model",
Internal`WithLocalSettings[Null,
systemModel = ToExpression[rawInput];
graphObj = Graph[systemModel["ActionChains"],
VertexLabels -> Placed["Name", Tooltip],
GraphLayout -> "LayeredDigraphEmbedding",
PlotTheme -> "Scientific", ImageSize -> 500];
centralityData = Thread[VertexList[graphObj] -> BetweennessCentrality[graphObj]];
,
Print["Error: Invalid Association Format"]
],
Method -> "Queued", Appearance -> "Palette", Background -> LightBlue
],
Spacer[20],
(* Main Dashboard Interface *)
Dynamic[
If[Length[systemModel] > 0,
TabView[{
"System Map" -> Column[{
Style["Functional Dependency Graph", 16, Bold],
TextCell["Node size indicates 'Betweenness Centrality'—high-impact bottlenecks for innovation or failure.", "Small"],
Graph[systemModel["ActionChains"],
VertexSize -> (Rescale[Values[centralityData]] * 0.5 + 0.1),
VertexStyle -> (ColorData["TemperatureMap"] /@ Rescale[Values[centralityData]]),
VertexLabels -> Placed["Name", Above],
GraphLayout -> "ForceDirectedEmbedding",
ImageSize -> 600,
EdgeStyle -> Arrowheads[0.02]]
}],
"Component Audit (5 Ws)" -> Column[{
Style["Structural Metadata", 16, Bold],
Dataset[systemModel["Metadata"]]
}],
"S&MA & Innovation" -> Column[{
Style["Hazard & Conflict Analysis", 16, Bold],
TableForm[
KeyValueMap[
{#1, #2["Function"], #2["HarmfulEffect"],
If[Lookup[centralityData, #1, 0] > 0.5, "CRITICAL HUB", "Routine"]} &,
systemModel["Metadata"]],
TableHeadings -> {None, {"Entity", "Function", "Harmful Effect", "S&MA Priority"}}]
}]
}],
Style["Waiting for model initialization...", Italic, Gray]
]
]
}, Center, Background -> White, Frame -> True, FrameStyle -> GrayLevel[0.8], RoundingRadius -> 10, ImageMargins -> 20]
]
How to Use This GUI for Innovation
Input: Use the Master Innovation Prompt we built earlier to analyze a system (e.g., a "Cryogenic Fuel Transfer System").
Paste: Take the
systemModel = <| ... |>code block the AI gives you and paste it into the large input field in the Mathematica GUI.Analyze (The System Map): * The graph will automatically resize nodes based on Betweenness Centrality.
Innovation Insight: Large nodes are "Structural Bottlenecks." If you can combine the functions of a large node into the "Super-System" (Environment), you radically simplify the architecture.
Audit (The Component Audit): * This uses Mathematica’s
Datasetobject, allowing you to sort components by their "Why" or their "Function."S&MA Insight: Use this to identify components that have high "Harmful Effects" (heat, vibration) but are also "Critical Hubs." These are your primary candidates for Redundancy or TRIZ-based replacement.
Why this fits your "Low-Budget Solopreneur" / NASA background:
Low Cost: You are using LLMs as the "Data Entry" team and Mathematica as the "Analyst."
Formal Rigor: Even though the interface is simple, the underlying math (Graph Theory) is identical to what is used in formal NASA MBSE (Model-Based Systems Engineering) workflows.
Knowledge Graph Ready: Because the data is stored as a
systemModelassociation, you can easily export it usingExport["model.json", systemModel, "JSON"]to feed into your broader NASA Knowledge Graph projects.
Would you like me to help you write a "Batch Processor" script for this, where you can feed it multiple launch provider documents and have it flag conflicting system architectures automatically?
From <https://gemini.google.com/app/> Google Gemini 3.0 Pro (Thinking)
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