Anyone who has ever opened a patent document knows the challenge: claim texts often resemble a labyrinth of legal formulations and technical details. For professionals in patent research, this is a daily obstacle, as the essential information is hidden behind long and complex descriptions. This is where the component graph, created with the help of generative AI, comes in. It reveals connections that are difficult to identify with traditional methods.
Generative AI in claims research is changing how students, researchers, and patent attorneys work. It also simplifies access to complex texts and makes analysis faster. Patent claims form the core of every patent. Yet they are often written in a complicated mix of legal language and technical detail. With INTERGATOR Patent Search, there is now a tool that analyzes these texts automatically, extracts key features, and presents them in a clear structure.
The use of Artificial Intelligence (AI) significantly improves research efficiency. Through semantic analysis and natural language processing, AI can grasp search terms in context. This enables a more intelligent search that not only considers exact terms but also thematically related concepts.
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.
