AI-enabled Patent Search: Potential and Limitations

AI-enabled patent search is transforming patent search process. The most accurate and intelligent prior art search tools in the market use AI to streamline and automate searches. Because of machine-learning and deep-learning AI algorithms, engineering and IP teams can work more quickly, identifying opportunities and resolving roadblocks. 

With millions of results to sort through in both national and international databases, many of which are in foreign languages or non-standardized formats, it can be difficult to determine whether searches on a specific topic are exhaustive. AI simplifies the process by searching more frequently, more quickly, and with more relevant results. Patent research and verification projects that used to take days, if not weeks, can now be completed in minutes. 

The Limitations of AI Searches 

However, even though AI-enabled search tools are largely based on data gathered from thousands of manual searches conducted by humans, they serve as a proxy for both human intent and error. Irrelevant results can appear valid if the correct term taxonomies are not highlighted. That is why it is critical to use both manual and AI searches in a thoughtful and strategic manner, combining the broad net of AI with the precision of Boolean search. 

Aside from the previously mentioned issues of searching multiple databases containing data in various formats, AI can leverage existing information biases to favor certain results over others. You may not be aware of the presence of blind spots. This is especially true for search engines that use “black box” algorithms that users do not always understand. Without knowing the basic criteria or the conceptual basis for how a search generates results, you are unsure that you have the full picture. 

Manual Boolean Search Added to Patent Research Projects 

When engineers are tasked with determining the patentability of a project quickly, they must consider both speed and accuracy. While AI searches are certainly faster, they do not completely replace traditional Boolean searches in terms of accuracy. Because prior art searches can necessitate in-depth analysis of academic, legal, and commercial documents (such as XLSCOUT’s Corporate Tree integration), it is critical to approach each search with an understanding of when to incorporate manual techniques into your workflow. 

A two-pronged approach combining manual and semantic searches is an effective tactic. However, the order of operations varies depending on the nature and goals of your research project. If you don’t know exactly what you’re looking for—or if you do and expect your search to yield an overwhelming number of results—you can use AI as a filtering tool to remove irrelevant data. 

Classic Boolean keyword searches employ language strings that can be manipulated by operators or modifiers to produce results comprising exact phrases or instances of words. AI searches use a technology called machine learning and natural language processing to identify semantically related phrases. For instance, trying to search for the term “patent” would not only generate all incidences of the word “patent.” But also associated terms such as “intellectual property” or “prior art.”

Initially, using AI-enabled patent search can assist in identifying families of related terms. It assists in narrowing the results within which you may use Boolean searches to seek more targeted results. The result is an integrated search method that can be further hybridized with XLSCOUT’s enhanced search tools. 

Our Methodology

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-enabled 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.