Meeting companies’ innovative requirements on online technology trading platforms: A novel large language model-based framework

Abstract

Online technology trading platforms (OTTPs) are critical for companies to publish technology requirements and identify solutions like patents. However, semantic gaps persist between market-driven needs and technical supply texts, which traditional methods fail to bridge. While large language models (LLMs) show promise, their effectiveness in OTTPs is limited by hallucination and temporal unawareness. We propose an LLM framework integrating the Hypothetical Document Embedding (HyDE) framework, where we generate pseudo-supply texts based on technical requirements. These texts are then matched with candidate patents using similarity calculations. To reduce hallucination, we use industry-specific knowledge graphs to guide the text generation process and introduce a self-reflective mechanism to refine the generated texts. To address the lack of time awareness, we enhance the knowledge graph with timestamps, turning it into a temporal knowledge graph. Additionally, we introduce the TPPR (Temporal Personalized PageRank) algorithm to improve the relevance of generated texts. Experiments show that our framework performs better than existing methods in Recall, Precision, and Mean Reciprocal Rank (MRR). This framework advances technology forecasting by enabling dynamic patent matching, offering organizations actionable insights for R&D investments. ​By reducing mismatches and innovation cycle times, it supports sustainable technology transfer—highlighting implications for AI governance in evolving innovation ecosystems.