Relevance Analysis

Semantic search using AI delivers impressive results, but sometimes there is a challenge in understanding why specific results are deemed relevant. Our Relevance Analysis feature eliminates this black box phenomenon, providing users with a transparent view of the factors contributing to result relevance. It also offers a practical jump-start, allowing users to navigate directly to the most relevant sections instead of combing through an entire document.

Generating Relevance Analysis

Relevance analysis can be done after conducting a search. Click a result and then go to the “Relevance Analysis” tab in the pop-up. 

Then, click the “Generate Relevance Analysis Button” which will trigger our AI system to provide a detailed breakdown of why the selected patent is relevant to your query.

Relevance Analysis tailors its insights for each patent, delving into specific details such as why particular claims are relevant or irrelevant to the query, extracting key quotes from the query and the patent, and highlighting relevant and irrelevant features. Generally, it will point out what is similar and dissimilar about the patent to the query, followed by a conclusion.

Previously, users had to copy and paste the relevance analysis into the Notes Section of NLPatent to export it. However, now you can export it directly by clicking the “Relevance Analysis” section in the export menu.

Understanding Relevance Drop Off 

Beyond understanding why a patent is deemed relevant, this feature helps users determine where the drop-off point of relevancy is. In Boolean searches, users must sift through documents that meet the specific Boolean expression- the order of results have no correlation to relevancy . Whereas in NLPatent, documents are ordered semantically, bringing the most similar ones to the top.

It can sometimes be challenging to identify where relevance diminishes. We recommend using the relevance analysis feature for this purpose. For example, if the conclusion indicates limited relevance, this likely suggests that the patent is not very pertinent to the query.

The Relevance Analysis feature is highly valued by NLPatent users as it significantly expedites their patent search efforts. In conclusion, Relevance Analysis serves as a beacon, eliminating the black box effect that can occur with semantic search. It equips users with a deeper understanding of why a patent is relevant to their query, ultimately enhancing the efficiency of a patent search.

Disclaimer

While Relevance Analysis undergoes rigorous monitoring, occasional false results may occur. We emphasize the importance of user due diligence and cross-verification of generated results. Relevance Analysis serves as a powerful tool for expediting searches, but it is not a replacement for professional human opinions.