• last updated : 20 April, 2023

Conceptual Searching: Extracting Relevant Results

Category: Blog
conceptual searching

Keyword-based searching has well-documented limitations. A syntactic search engine will return results solely based on the phrases and words entered by the searcher. In this case, synonyms, translations, and homonyms are all problematic. This technology is simply insufficient to find every piece of patent and non-patent literature associated with your novel idea on its own. On the other hand, conceptual searching takes into account the meaning of the searcher’s query. It employs this comprehension of the search to determine the most pertinent results, irrespective of the words used in the search query or publication to communicate the concept. The most advanced search solutions can identify results that go beyond synonyms, utilizing AI to truly comprehend the concepts described.

The stakes are high when pushing a novel concept through the innovation pipeline. R&D resources must be directed toward the most promising projects. Patenting has legal and administrative costs that have to be carefully considered. Deciding if or not an invention is marketable necessitates a thorough understanding of the technology landscape. This can only be done with a search engine that tries to find the most pertinent patent and non-patent literature, regardless of how your technology is described. 

Finding relevant results (and confidently eliminating irrelevant results) with keyword-based search engines necessitates at least some expertise in specialized techniques such as Boolean logic. Conceptual searching democratizes patent searching by removing bottlenecks in the innovation lifecycle. It provides confidence to the searchers from all professional fields in their search results. 

Concept searching is used by the AI-powered search engine that powers XLSCOUT‘s Novelty Checker, to deliver highly relevant results. Our mature, intelligent AI engine continuously learns from patent and non-patent literature, improving its comprehension of technical and legal language.

Conceptual Searching for More Accurate Results 

Conceptual search engines not only let you use longer phrases to define what you’re looking for—they excel at it. You can search for similar technologies using a few phrases or your entire invention disclosure. Now, the AI-based search tool can search for similar inventions using your idea and an understanding of what’s most important. These search techniques produce more relevant search results than a handful of keywords. 

Sometimes you don’t know how to express exactly what you’re looking for. Or you define it in a way that would not be understood by someone working in a different field, for a different organization, or in a different country. Conceptual searching solves this problem because the engine recognizes the idea regardless of how you express it. The language used in the most relevant results can then be used to conduct additional research. This ensures that you’re finding all of the relevant literature, both in your industry and across industries.

Our Methodology

XLSCOUT put the use of reinforcement learning to its AI-based Novelty Checker patent search tool to get quality patent research reports in just 10 minutes. The Novelty Checker uses reinforcement learning to filter the noise from the prior art by pulling up the relevant results on top of the list. To be precise, it assists in conducting to patentability search/patent research 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 and patentability search report by selecting these Top-10 or 20 results. The Novelty Checker prior art search reports include a list of results along with relevant text mapping with the key features of the invention for enabling quick decision-making. 

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