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Unleash the Potential of Semantic Patent Search

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semantic patent search

What is Semantic Search? 

Before we can discuss semantic patent search, we must first define semantics. Semantics, in a nutshell, is the study of meanings. It is the foundation of how we know and communicate information; it gives words and phrases meaning. Semantics is associated with concepts such as comprehension, context, intent, natural language, and correlation. 

What is the relationship between semantics and searching? 

If you are an inventor, entrepreneur, or engineer looking to patent your innovative ideas, you will need to do some research to determine if your idea is both patentable and marketable. Professional patent searchers understand how to construct search strings with carefully chosen keywords and Boolean operators. Using these techniques to design a query that yields the desired search results is an advanced skill that enables people to “speak” to search engines. It can be difficult for others who want to explore patent databases to sit down and create the ideal keyword or Boolean search. 

Patent searching is no longer limited to IP professionals, thanks to advancements in searching capabilities. A semantic search engine allows you to search for information based on meaning instead of keywords alone. Semantic searching humanizes the troublesome, robot-like process of query structuring. The search engine determines context, associates meanings, and locates documents that contain relevant concepts. This type of searching can even lead to the discovery of connections that you were previously unaware of. 

Natural language processing and machine learning algorithms are used to discover these relationships. Artificial intelligence enables the search engine to recognize meaning within your query as well as the contents of the database you’re looking for. This technology represents a significant advancement over keyword-based search engines, where limited input resulted in limited results. 

The History of Semantic Search 

While semantic modeling development started in the 1970s, natural language processing was not introduced into search engines until much later. Google started to incorporate semantics into its algorithm in the mid-2010s in order to provide more pertinent search results. The search engine could now perceive searchers’ phrases as more than simply a string of keywords, breaking complex queries into segments from which it could deduce meaning. This gave users more flexibility in entering their queries. 

However, at this point, the semantic search functionality of a widely used search engine such as Google was insufficient to search the types of technical databases used by IP professionals and R&D teams. These types of searches necessitate more specific results; they require not only a list of pertinent results but also the capacity to analyze and visualize those results. 

The Current Search Scenario 

Finding simple information, such as a list of Google search results, is only the first step these days. Your innovation strategy necessitates more substantive responses, problem-solving solutions, and information that results in next steps. To meet these demands, advances in machine learning and artificial intelligence have focused on determining meaning within the context of both the search query and the database. More accurate results are produced by semantic search engines with improved query processing. 

As the amount of information and access to it grows, you must maintain control over your data pool. An AI search engine powered by machine learning is only as good as the data it consumes. Is the system consuming data from millions of sources of varying quality and credibility and filling it with billions of concepts (also of varying quality and credibility)? Or does it eat a healthy diet of selected databases containing information relevant to your needs? This distinguishes a search engine that indexes the entire web from one that learns from indexed technical documents. 

Starting with a semantic search enables specialized analytics capabilities to identify new tactics and uncover new applications. 

Semantic Patent Searching 

During a patent search, the stakes are high. You cannot afford to overlook important information. Semantic patent search engines can interpret the concepts in your query and find documents that share a common meaning. Natural language processing enables semantic patent searching to facilitate a conversation between the user and the data. It also makes this technology available to lawyers, engineers, and strategists throughout your organization, not just experienced IP professionals trained in searching techniques. 

XLSCOUT’s AI-Based Patent Search 

The algorithms that power the AI search engine XLSCOUT are geared toward patents and technical documents. The system utilizes corpus and recognizes multiple meanings for a single term as well as multiple words with the same meaning, and it employs machine learning to detect changes in meanings and conceptual relationships over time. Because neural network learning algorithms can consume large blocks of text, your query is not limited in any way. 

The intelligence gained from an AI-based semantic patent search tool is anything but artificial. It is appropriate. Highly relevant search results guide you to actionable discovery, taking you from ideation to accurate answers in minutes. Use these discoveries to make important business decisions, demonstrate that your idea is not only novel and useful but also marketable, and identify other organizations dealing in similar stuff. 

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