A method for identifying product opportunities and innovative design based on dynamic demand evolution

Contemporary product user demand exhibits dynamic and diversified characteristics, making the effective identification of product innovation opportunities from dynamically evolving demand data a critical challenge. To address this issue, a method for product opportunity identification and innovation strategy generation based on the dynamic evolution analysis of demand is proposed by integrating the Large Language Model (LLM) and the Temporal Graph Convolutional Network (TGCN). First, multi-granularity demand extraction is realised through the Multi-grained Requirement Feature Extraction Model (MRFEM), and a dynamic Requirement Evolution Graph (REG) is constructed. Second, the spatial semantic correlation and temporal dependency between demands are fused, and the evolution indicators such as demand evolution intensity and future attention are established. Finally, the opportunity identification and scoring method is proposed, and the demand value correction coefficient is introduced to quantify the innovation value, while the LLM is used to generate interpretable innovation strategies. A smartphone product is used as an example to verify the effectiveness and practicality of this method.