用AI自动筛选高价值专利，助力技术转移决策
A Hybrid Ai Framework For Strategic Patent Portfolio Pruning: Integrating Learning To-Rank And Market Need Analysis For Technology Transfer Optimization
- 结合排序学习与市场需求数智代理，自动化评估专利价值
- 通过匹配专利能力与真实市场需求，识别可转化核心资产
- 支持人工校验，适合高校与科技企业优化专利布局
本文提出一种多阶段混合智能框架，用于专利组合精简，以识别适合技术转移的高价值资产。现有专利估值方法多依赖回顾性指标或耗时的手动分析。本框架通过融合学习排序（LTR）模型与独特的“需求-种子”代理系统，实现流程自动化与深度化。其中，“需求代理”利用自然语言处理从非结构化市场和行业数据中挖掘明确的技术需求；“种子代理”则基于微调的大语言模型分析专利权利要求，映射其技术能力。系统构建“核心本体框架”，将高潜力专利（种子）与已记录的市场需求（需求）匹配，为剥离决策提供战略依据。框架包含动态参数加权机制与关键的人在回路（HITL）验证协议，确保适应性与现实可信度。
原文摘要 · Abstract (English)
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.
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