AI-based patent analysis is an emerging field that combines artificial intelligence and machine learning techniques with traditional patent research and analysis. The key aspects of this topic include:
- Leveraging AI algorithms to read, understand, and categorize patent documents at a deeper level than traditional human-based patent searches. This allows for the identification of relevant patents that may have been missed by manual searches.
- Developing taxonomies of technologies and concepts that have been curated by human experts with extensive patent research experience. These taxonomies are then used to train AI models to better contextualize and cluster patent data.
- Applying advanced natural language processing techniques, such as document-topic representations, to extract insights from the text of patent documents beyond just bibliographic data.
- Offering services that provide patent positioning, dynamic landscape evaluation, grant probability prediction, and enhanced patent search capabilities powered by AI.
- Combining the strengths of human patent experts with the scalability and processing power of AI to deliver more comprehensive and accurate patent analysis.
Hence, AI-based patent analysis leverages the latest advancements in artificial intelligence to augment and enhance traditional patent research, with the goal of uncovering deeper insights and identifying promising areas of innovation.
