As a crucial innovation paradigm, technology convergence (TC) is gaining ever-increasing attention. Yet, existing studies primarily focus on predicting TC at the industry level, with little attention paid to TC forecast for firm-specific technology opportunity discovery (TOD). Moreover, although technological documents like patents contain a rich body of bibliometric, network structure, and textual features, such features are underexploited in the extant TC predictions; most of the relevant studies only used one or two dimensions of these features, and all the three dimensional features have rarely been fused. Here we propose a novel approach that fuses multi-dimensional features from patents to predict TC for firm-specific TOD. Our method comprises three steps, which are elaborated as follows. First, bibliometric, network structure, and textual features are extracted from patent documents, and then fused at the International Patent Classification (IPC)-pair level using attention mechanisms. Second, IPC-level TC opportunities are identified using a two-stage ensemble learning model that incorporates various imbalance-handling strategies. Third, to acquire feasible firm-specific TC opportunities, the performance metrics of topic-level TC opportunities, which are refined from IPC-level opportunities, are evaluated via retrieval-augmented generation (RAG) with a large language model (LLM). We prove the effectiveness of our proposed approach by predicting TC opportunities for a leading Chinese auto part manufacturer, Zhejiang Sanhua Intelligent Controls co., ltd, in the domains of thermal management for energy storage and robotics. In sum, this work advances the theory and applicability of forecasting firm-specific TC opportunity through fusing multi-dimensional features and leveraging LLM-as-a-judge for technology opportunity evaluation.

翻译：作为关键创新范式，技术融合正日益受到关注。然而，现有研究主要聚焦于产业层面的技术融合预测，鲜少关注面向企业技术机会发现的技术融合预测。此外，尽管专利等技术文献蕴含丰富的文献计量、网络结构与文本特征，但这些特征在现有技术融合预测中未得到充分利用；多数相关研究仅使用其中一或两个维度的特征，三种维度特征鲜有融合。本文提出一种融合专利多维特征以预测企业技术机会发现的创新方法，包含以下三个步骤：首先，从专利文献中提取文献计量、网络结构与文本特征，并通过注意力机制在国际专利分类号（IPC）对层面进行融合；其次，采用融合多种不平衡处理策略的两阶段集成学习模型识别IPC层面的技术融合机会；第三，为获取可行的企业级技术融合机会，通过基于大语言模型的检索增强生成技术，对从IPC层面机会中精炼出的主题层面技术融合机会进行性能评估。我们通过预测领先的中国汽车零部件制造商——浙江三花智能控制股份有限公司在储能热管理与机器人领域的技术融合机会，验证了该方法的有效性。总之，本研究通过融合多维特征并利用大语言模型作为裁判进行技术机会评估，推动了企业级技术融合机会预测的理论发展与应用实践。