A Hybrid Ai Framework For Strategic Patent Portfolio Pruning: Integrating Learning To-Rank And Market Need Analysis For Technology Transfer Optimization

2025-08-31

AI Summary

Current patent evaluation relies heavily on retrospective metrics or manual analysis, hindering efficient identification of high-value patents to support technology transfer. To address this, we propose a multi-stage hybrid intelligence framework. Methodologically, it integrates a “demand–seed” dual-agent system with a domain-specific core ontology, synergizing Learning-to-Rank (LTR), fine-tuned large language models (LLMs), patent semantic analysis, and market data mining—augmented by dynamic parameter weighting and human-in-the-loop validation. Our key contributions are: (1) the first integration of agent-based modeling and LTR for dynamic, context-aware patent-value matching; and (2) an interpretable, traceable, comprehensive assessment across 30+ legal and commercial dimensions. Empirical evaluation demonstrates substantial improvements in high-value patent identification accuracy, significantly enhancing both the strategic robustness and operational efficiency of technology transfer decision-making.

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Search and Optimization: Evaluation and Analysis

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This paper introduces a novel, multi stage hybrid intelligence framework for pruning patent portfolios to identify high value assets for technology transfer. Current patent valuation methods often rely on retrospective indicators or manual, time intensive analysis. Our framework automates and deepens this process by combining a Learning to Rank (LTR) model, which evaluates patents against over 30 legal and commercial parameters, with a unique "Need-Seed" agent-based system. The "Need Agent" uses Natural Language Processing (NLP) to mine unstructured market and industry data, identifying explicit technological needs. Concurrently, the "Seed Agent" employs fine tuned Large Language Models (LLMs) to analyze patent claims and map their technological capabilities. The system generates a "Core Ontology Framework" that matches high potential patents (Seeds) to documented market demands (Needs), providing a strategic rationale for divestment decisions. We detail the architecture, including a dynamic parameter weighting system and a crucial Human in the-Loop (HITL) validation protocol, to ensure both adaptability and real-world credibility.

Problem

• Automates patent portfolio pruning to identify high-value technology transfer assets
• Combines Learning to Rank with market need analysis for strategic divestment decisions
• Maps patent capabilities to market demands using AI and ontology framework

Innovation

• Hybrid AI framework combining LTR and market analysis
• Need-Seed agent system using NLP and LLMs
• Core Ontology Framework matching patents to market demands