Patent research is a critical part of innovation and development processes for many companies. However, traditional methods such as keyword-based search queries often present challenges. They are time-consuming, yield inaccurate results, and frequently require complex Boolean queries that demand years of experience, technical expertise, and strong search skills from researchers.
Artificial intelligence (AI), machine learning, and generative AI are increasingly driving innovations across various sectors. The field of patent research significantly benefits from these technologies. INTERGATOR Patent Search enables semantic searches that identify similar documents based on text inputs, eliminating the need for users to formulate complex Boolean queries.
INTERGATOR Patent Search provides an integrated monitoring research assistant that generates a complex search query based on input of relevant parameters. Advanced semantic analysis techniques automatically expand extracted concepts with related terms and synonyms to enhance the relevance of search results. This ensures comprehensive monitoring research, identifying potentially relevant patents and intellectual property rights that may not have been captured by the original search query.
On the homepage of INTERGATOR Patent Search, we select the novelty search assistant, guiding us through the entire process. Initially, we input a detailed description of our invention – in this case, a double-walled cannula made from nanomaterial, preventing adhesions and ensuring high sterility. INTERGATOR then searches for relevant patents, treating each concept as a separate filtering element and expanding them with semantically similar terms. This flexibility, coupled with additional filtering options like patent status, enables precise and comprehensive novelty searches. Utilizing the integrated patent preview, we can meticulously examine and efficiently evaluate search results.