[av_textblock size=” font_color=” color=” av-medium-font-size=” av-small-font-size=” av-mini-font-size=” av_uid=’av-k3o8uqx5′ id=” custom_class=” admin_preview_bg=”]

Why should you complement your traditional Boolean search queries with AI- driven semantic search?

Conventional, patent searching approach involves searching for keywords and phrases that are grouped in a query using Boolean and proximity operators. The queries are further combined with other queries using query-identifiers, thereby creating a query script which often run into more than 50+ strings. This process is tedious, and a lot of thought process goes into deciding the key terms, synonyms and classifications that are to go into the query. The traditional process albeit time-consuming, still ranks high on precision. It is also fraught with limitations, as missing out a single relevant term would result in missing out on important prior art.

With the exponential increase in the amount of information and working under time constraints, effectively bringing forth the right search results has become the need of the hour. This is where AI-driven semantic search helps.

Semantics implies something that conveys meaning or provides logic. Semantic search tries to improve search precision by recognizing the searcher’s purpose and providing more complete results. Searchers can also dig through data and find links that they may not have realised existed

Why it has become imperative to club semantic search with your keyword searches?

A semantic search tool understands the different ways a concept is conveyed and in what perspective a term is used. The input paragraph is converted into a contextual search vector that looks at not just the direct matches but also similar matches. Here are a few reasons to supplement your Boolean queries with semantic searches.

Reduce your chances of missing a prior art when constrained for time

Even a single critical prior art missed by a searcher may come to haunt the patent applicant/patentee later as it may hamper the patent prosecution or become a ground for invalidation or infringement. Patents are difficult to read, and many are intentionally written in a vague manner which is tough to understand and search. A drafter can also use novel terminology when drafting a patent application which is not easily known at the time of search. Semantic search works over contextual meaning of the input sentence or paragraph and so can help uncover missed results. You can easily NOT the results of the Semantic search with your Boolean query to see the delta results and scan them for any relevant record.

For prior art or invalidity searches with a short turnaround time, you can also directly start with a semantic search. In many cases you will find the matching set of prior art with just this approach. Both these types of searches might not need 100% precision and if you can make your case with a portion of the relevant prior art then further searches become redundant.

To know more visit – Boolean search queries with AI- driven semantic search?
[/av_textblock]