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  • last updated : 05 September, 2022

Efficient use of AI Search tools for Prior Art Searching

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prior art search

Patents are one of the most important assets for companies as they form the core of technologies and innovations that might become the next big thing. Owning patents puts companies in a dominating position in the market and helps them grow parallel revenue streams through patent licensing. Patents are valuable IP assets not just for businesses, but also for startups and academic institutions. However, the patent grant is a monetarily heavy process that involves drafting fees, renewal fees, and others. Hence it is very important to understand the subject matter and its prior art before proceeding with the patent application. Currently, filing entities either invest in creating a team of in-house subject matter experts or outsource patentability and prior art searches to technology expert vendors.

Recently, there has been a shift in this dynamic with a third alternative: the hybrid approach.

Most companies prefer doing in-house prior art searching because of expensive outsourcing charges and data confidentiality issues. However, setting up an in-house expert team is an equally expensive approach, and hence, companies are now opting for the hybrid approach.

One way to approach hybrid methodology is to conduct a first-pass analysis in-house. Professionals with technical knowledge of the subject matter can conduct patentability searches.

The first-pass prior art analysis provides a sneak peek into the topic and provides an extra layer of insight into the subject matter. This helps in streamlining the ideas and adds a new dimension to subject-specific avenues.

There are plenty of resources out there that provide first-pass prior art search assistance. However, certain factors are considered before approaching the prior art search.

Identifying relevant concepts and aggregating prior art

The first step in conducting a prior art search begins with identifying the accurate data set. This data set can be further refined using relevant keywords, semantics, synonyms, and word forms. It helps in defining the data set comprehensively and accurately.

Identifying a comprehensive list of keywords is crucial as prior art search depends on it. Missing critical keyword(s) increases the likelihood of overlooking critical prior art, resulting in inefficiency and poor search quality.

XLSCOUT supports a machine-trained corpus with more than 3 billion words that can enable identifying concepts and technical variations.

Ensure that an exhaustive list of synonyms or alternative concepts has been taken into consideration. As an example, a concept named “screen guard” can also mean “screen protector” or “screen glass”.

An incomplete list of alternative words is likely to lead to an incomplete prior art search, clustering, or categorization. A machine learning-based corpus is recommended in these cases.

Selecting right sources

Prior art can be anything, a blog post, a research paper, a patent application, an already existing patent, etc. Choosing and searching for the right sources is pivotal, which is also emphasized in a report published by CAS. There are numerous free and paid search databases available that assist in identifying non-patent sources.

Fine-tune prior art search

With an insanely large dataset to analyze, it is humanly impossible to go through each and every piece of document and analyze whether it overlaps with the invention disclosure or not.

Advanced AI tools help in narrowing down the database through smart filters, which funnel the search to the relevant prior art. These tools help in removing clutter and performing operations that aid in understanding prior art around specific concepts.

Cluster prior art

Often, clustering patents goes a long way in expediting patent search. Clustering patents can help in creating context-based funneling, which further contributes to the quality of the patent search results.

Performing a prior art search is a daunting task, but with advanced tools at our disposal and basic search techniques, the searches can be reduced to insightful outputs and interactive visualizations.

XLSCOUT supports dedicated prior art search modules. Novelty Checker, an AI-backed tool for first-pass patentability search, enables users to search for prior art by inputting their idea in natural language. Integrated with Para-BERT technology, Novelty Checker understands the context of each paragraph in a patent document to provide relevant results. The Novelty Checker incorporates Reinforcement Learning for users to quickly generate an automated novelty and patentability search report by selecting the top 10 or 20 results. The Novelty Checker prior art search reports include a list of results along with relevant text mapped with the key features of the invention for quick decision-making.

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