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

arXiv: https://arxiv.org/abs/2509.00958 · HTML: https://mlsanity.com/paper/2509.00958/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 94 references that are in the map.
- First posted on arXiv 31 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [A Comprehensive Survey on Automatic Knowledge Graph Construction](https://mlsanity.com/paper/2302.05019/) (2023)
- [Natural Language Processing in the Patent Domain: A Survey](https://mlsanity.com/paper/2403.04105/) (2024)
- [Agentic Systems: A Guide to Transforming Industries with Vertical AI Agents](https://mlsanity.com/paper/2501.00881/) (2025)
- [Engineering Knowledge Graph from Patent Database](https://mlsanity.com/paper/2106.06739/) (2021)
- [Dynamic Multi-Reward Weighting for Multi-Style Controllable Generation](https://mlsanity.com/paper/2402.14146/) (2024)
- [PaECTER: Patent-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2402.19411/) (2024)
- [A Survey on Patent Analysis: From NLP to Multimodal AI](https://mlsanity.com/paper/2404.08668/) (2024)
- [Formalising Human-in-the-Loop: Computational Reductions, Failure Modes, and Legal-Moral Responsibility](https://mlsanity.com/paper/2505.10426/) (2025)
- [Predictive Patentomics: Forecasting Innovation Success and Valuation with ChatGPT](https://mlsanity.com/paper/2307.01202/) (2023)
- [ILMART: Interpretable Ranking with Constrained LambdaMART](https://mlsanity.com/paper/2206.00473/) (2022)
- [Design of reliable technology valuation model with calibrated machine learning of patent indicators](https://mlsanity.com/paper/2406.05446/) (2024)
- [Artificial Intelligence In Patent And Market Intelligence: A New Paradigm For Technology Scouting](https://mlsanity.com/paper/2507.20322/) (2025)

## Abstract (arXiv)

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.