AI Reinventing Patent Search

Artificial intelligence is reinventing patent search by assisting organizations in keeping them updated with industry trends and uncovering meaningful innovations, as well as inspiring and informing the development of new products and services. 

Companies and research organizations must use AI to perform the kind of thorough and insight-rich patent searches that yield substantial competitive advantages as the amount of information within patents grows and inventors and researchers continue to innovate. 

AI as a Game Changer: Reinventing Patent Search

One of AI’s most powerful capabilities is its capacity to sift through information with unrivalled speed, efficiency, and relevance. AI technologies can search millions of documents by concepts rather than keywords, pulling information from a variety of sources such as public patent databases, academic papers, research documents, and much more—in seconds and at scale. 

XLSCOUT has trained machine learning algorithms to understand the complex contextual meaning of both queries and the documents being searched. The algorithms decode the language used to convey complex ideas and look for representations that are most closely related to the query. 

Researchers can quickly and easily check, classify, and organize data. Pure keyword searches (as used by most well-known search engines) make it easy to overlook important nuances in a query. For example, when a completely different or even contrasting position is searched, most search engines will return documents only supporting that position. Concept-driven searches aid in alleviating this issue. Finally, this saves organizations and researchers time and energy, allowing them to avoid costly errors of omission and innovate more effectively. 

AI Search for Patents 

Research tools are frequently dispersed across multiple platforms. This makes categorization, accessibility, and analysis difficult. 

AI empowers the search and discovery of patent details and connections. This would be difficult for a single person or team of people to discover on their own. Patents are extremely abstract in nature and thus intellectually limiting because the human mind is incapable of identifying every single potential application of the patent. AI, on the other hand, can provide access to information, enabling inventors to discover, investigate, and connect relevant data points.

AI search can aid in resolving problems such as: 

• Filling knowledge gaps in humans. A keyword search is based on what people believe they are looking for. While AI search looks for similarities between what people are searching for and identifies similar content under a different name. To identify nuances and concepts, AI goes beyond keywords. 
• Conducting patent landscaping and competitive analysis. 
• Patents are narrowed down by forward and backward citations, which aid in identifying competitors’ R&D spending. 

Novelty Checker: The Perfect Solution 

The Novelty Checker makes patent searches easier for inventors. The tool searches for similar inventions to yours to determine whether a patent is feasible.  

A step-by-step guide to conducting an AI-powered patent search with Novelty Checker can be accessed by clicking here. 

XLSCOUT put the use of reinforcement learning to its AI-based Novelty Checker (patent searching tool) to get quality patent research reports in just 5 minutes. The Novelty Checker uses reinforcement learning to filter the noise by showing the relevant results on top of the list. To be precise, it assists in conducting patentability search to help you ensure that your innovation is unique. By selecting a few relevant and non-relevant results, users can apply them to the result set. The system takes the user’s feedback and then learns from it. It uses conceptual searching and re-ranks the results by bringing the quality results to the top and sending the noise to the bottom.     

Without reinforcement learning, users go through hundreds of results manually. By applying this process, users can skip going through the non-relevant results. Reinforcement can also be applied multiple times to a result set according to users’ different requirements/criteria. Users can then view the Top-10 or Top-20 results for each criterion to perform a prior-art analysis for idea validation. Users can quickly generate an automated novelty report by selecting these Top-10 or 20 results.