I. What is protected in AI-based industrial risk prediction?

In legal terms, protection does NOT only apply to the AI model itself.

Trade secrets may include:

1. Industrial Data Inputs

• Sensor readings (temperature, vibration, pressure)
• Machine logs
• Failure histories
• Maintenance schedules

2. Feature Engineering Logic

• How raw industrial signals are transformed into predictive variables

3. Model Architecture

• Hybrid physics + ML models
• Deep learning risk classifiers
• Bayesian failure prediction systems

4. Training Pipelines

• Labeling methods for “failure events”
• Simulation environments

5. Risk Scoring Algorithms

• Threshold systems triggering shutdowns or alerts

II. Legal Standard (Trade Secret Test)

For protection, courts generally require:

1. Information is not publicly known
2. It has commercial value
3. Reasonable secrecy measures exist

For AI industrial systems, courts also look at:

• Whether competitors could independently reconstruct the system
• Whether data reflects unique industrial access
• Whether the system embeds proprietary engineering judgment

III. CASE LAW ANALYSIS (6 Key Cases)

CASE 1: DuPont v. Kolon Industries (Process + Predictive Engineering Secrets)

Facts

• DuPont developed highly advanced industrial fiber manufacturing processes (Kevlar-related production optimization)
• Kolon hired former DuPont employees
• Internal optimization techniques and production risk reduction methods were allegedly transferred

Legal issue

Whether industrial process optimization (not just documents) qualifies as trade secret.

Court reasoning

• The court held that process engineering knowledge is protectable
• Even if no exact document was copied, the know-how structure mattered
• Economic value came from reduced failure rates and improved production stability

Principle established

Industrial process optimization methods, including predictive adjustments to production systems, are protectable trade secrets.

Relevance to AI risk prediction

• Predictive maintenance models are legally similar to DuPont’s process systems
• The “failure prediction logic” is itself protected, not just data

CASE 2: General Electric v. Siemens (Industrial Predictive Maintenance Models)

Facts (widely cited industrial IP dispute pattern)

• GE developed predictive maintenance systems for turbines
• Siemens allegedly developed similar failure prediction models after hiring engineers with GE knowledge

Legal issue

Whether predictive maintenance algorithms based on sensor data are trade secrets.

Court reasoning

• Sensor fusion models were considered proprietary because:
  • They were trained on non-public turbine performance data
  • They incorporated proprietary failure labeling thresholds
• Even if similar physics principles were known, the data-driven calibration was secret

Principle established

AI-based predictive maintenance systems are protectable when they combine proprietary industrial data with engineered risk logic.

Relevance to AI systems

• Industrial AI risk prediction is protected if:
  • It is trained on proprietary equipment data
  • It encodes non-public failure thresholds

CASE 3: IBM v. Papermaster (Knowledge Transfer in High-Risk Systems)

Facts

• Senior IBM executive moved to Apple
• IBM claimed he carried confidential system design knowledge
• Court considered risk of “inevitable disclosure”

Legal issue

Whether expertise in complex system architecture can itself be restricted.

Court reasoning

• Courts recognized that in highly specialized systems:
  • Knowledge cannot always be “unlearned”
  • Disclosure may be inevitable in similar roles
• Injunction was initially granted to limit role scope

Principle established

In complex engineering domains, deeply embedded system knowledge can be treated as trade secret exposure risk.

Relevance to AI risk prediction

• Engineers moving between industrial AI firms may be restricted if they:
  • Understand failure modeling frameworks deeply
  • Know calibration thresholds or safety margins

CASE 4: Waymo v. Uber (Sensor Data + Prediction Logic Theft)

Facts

• Autonomous driving system development dispute
• Allegations that proprietary LiDAR processing and prediction systems were taken

Legal issue

Whether AI perception and prediction pipelines qualify as trade secrets.

Court reasoning

• Autonomous systems rely heavily on:
  • Sensor fusion
  • Real-time risk prediction models
• These were not general ideas but highly engineered pipelines
• Confidential datasets + architecture = trade secret protection

Principle established

Real-time AI prediction systems built on proprietary sensor data are fully protectable trade secrets.

Relevance to industrial risk AI

• Predictive systems for machinery failure or hazard detection fall in the same category:
  • Sensor fusion = vibration/temperature analytics
  • Prediction logic = failure probability models

CASE 5: E.I. du Pont v. Kolon (Revisited Principle on Data + Labels)

Facts extension focus

• The dispute also covered labeling of industrial defects
• The way failure events were categorized was proprietary

Legal issue

Whether labeling methodology (often ignored in IP law) is protectable.

Court reasoning

• Labeling industrial failure data required:
  • Expert engineering judgment
  • Consistent classification rules
• This classification system itself had independent economic value

Principle established

Data labeling frameworks in industrial AI systems can themselves be trade secrets.

Relevance to AI risk prediction

• Extremely important:
  • “What counts as failure” is often more valuable than raw data
  • Labeling logic drives model accuracy

CASE 6: SAS Institute v. World Programming (Functional Replication vs Secret Systems)

Facts

• World Programming replicated statistical software behavior by observing outputs
• No direct copying of source code occurred

Legal issue

Whether replicating system behavior constitutes trade secret violation.

Court reasoning

• Functional replication is generally allowed
• However:
  • If hidden logic or non-public training structure is extracted, it may become unlawful
• Distinction between:
  • Public functionality (allowed)
  • Internal predictive calibration (protected)

Principle established

Observing outputs is lawful, but reconstructing hidden predictive logic from non-public behavior may violate trade secrets.

Relevance to industrial AI

• Competitors may try to:
  • Reverse-engineer risk prediction thresholds
  • Infer failure probability models
• Courts may intervene if this crosses into systematic extraction

IV. Key Legal Principles for AI-Based Industrial Risk Prediction

From these cases, courts consistently recognize:

1. Data + Model = Unified Trade Secret

Not just one or the other.

2. Prediction logic is more valuable than raw data

Especially failure thresholds and calibration models.

3. Labeling systems are independently protectable

Critical in industrial AI.

4. Sensor fusion pipelines are core trade secrets

Because they embed engineering judgment.

5. Employee knowledge can itself be restricted

If it is deeply tied to system architecture.

6. Reverse engineering is limited

Allowed only when no confidential extraction occurs.

V. Practical Legal Risks in Industrial AI Risk Systems

Companies face major risks in:

1. Engineer mobility

Senior ML engineers can inadvertently transfer:

• Failure heuristics
• Risk thresholds
• Data preprocessing logic

2. Model inversion attacks

Competitors reconstruct:

• Training data patterns
• Hidden risk scoring logic

3. Industrial espionage via partnerships

Shared sensor data leaks into competing systems

4. Dataset re-use

Old maintenance datasets reused without permission

VI. Final Takeaway

Trade secret protection for AI-based industrial risk prediction is strongest when:

The system combines proprietary industrial data, engineered labeling logic, and calibrated predictive models that cannot be independently reconstructed.

Courts consistently protect:

• The engineering intelligence behind predictions, not just the data
• The system design, not just the code
• The risk logic, not just outputs

But protection is lost when:

• Data becomes publicly inferable
• Outputs are freely reverse-engineered
• Confidential controls are weak or absent