Decision Tree Innovation Discovery With Human-Guided AI Validation

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Solution Overview

Problem

Existing innovation processes lack a systematic lifecycle and rely heavily on human expertise, leading to inefficiencies and instability, particularly when combined with the limitations of current AI systems, which struggle to handle new knowledge domains and are prone to hallucinations and data staleness.

Innovation Solution

A machine learning architecture using decision tree algorithms trained by human experts, with continuous data refreshes and reinforcement, to generate predictive models for innovation discovery, incorporating non-data and data types through a closed-loop ecosystem.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If AI systems are used for innovation discovery, then productivity is improved, but reliability deteriorates due to hallucinations and inability to handle new knowledge domains

Engineering Contradiction:

Improveinnovation discovery efficiencyVSAvoidAI prediction accuracy

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

Human experts serve as intermediaries between the decision tree algorithm and innovation data. The human expert guidance system processes AI-generated predictions and validates them against domain knowledge, preventing hallucinations while maintaining high productivity in innovation discovery

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system segments the innovation discovery process into distinct phases: data collection by AI, pattern recognition by decision trees, and validation by human experts. This segmentation allows each component to operate within its strengths, improving overall reliability while maintaining productivity

Inventive Principle:

Principle #1Segmentation

2Reliability

If continuous data refreshes are implemented, then reliability is improved by preventing data staleness, but loss of time increases due to continuous training requirements

Engineering Contradiction:

Improvemodel accuracyVSAvoidtraining time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system implements periodic data refreshes and model retraining instead of continuous operations. Data is collected and stored in databases, then refreshed at scheduled intervals, allowing the system to maintain reliability while minimizing time loss through batch processing rather than continuous training

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

Data is pre-collected and stored in databases before training is needed. This preliminary data preparation allows rapid model updates when refreshes occur, reducing the actual training time while maintaining up-to-date predictions

Inventive Principle:

Principle #10Preliminary action

3Reliability

If decision tree algorithms with human expert guidance are used, then reliability is improved by reducing hallucinations, but device complexity increases

Engineering Contradiction:

Improveprediction stabilityVSAvoidsystem architecture

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The human expert guidance system serves multiple functions: validating predictions, interpreting results, providing domain knowledge, and guiding data collection. This multi-functionality justifies the added complexity by consolidating multiple reliability-enhancing operations into a single integrated component

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20260087375A1Decision Tree Algorithms in Machine Learning To Learn and To Predict Innovations

Publication Date: 2026.03.26 JOHNSON MARGUERITE

AI summary

An expert system for innovation discovery in the field of artificial intelligence (AI) applies decision tree algorithms structured around a rules-based reasoning methodology. The system trains on innovation datasets comprising both data and non-data types, including target variables representing key attributes of innovations and proximal variables that approximate them. Through a machine learning architecture, decision nodes are configured to evaluate these variables and generate predictive models. The architecture enables continual learning via reinforcement mechanisms and communication ports that facilitate data flow from external tools, cloud storage, and software. Differentiated nodes are assigned weights, roles, and activation logic to refine decision-making and improve model accuracy. The expert system integrates human-defined heuristic rules with AI capabilities to support early-stage ideation, concept development, and innovation pattern recognition. This system can operate as an autonomous AI agent, a core reasoning engine, or as part of a digital business model in platform ecosystems to enhance innovation discovery, reduce hallucinations, and support transparent, verifiable AI outcomes.