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Patent Search & Analytics

Unlock the Power of Semantic Patent Search

By March 29, 2021April 6th, 2021No Comments

Before we talk about semantic patent search, we must agree on what semantics is. In short, semantics is the study of meanings. It is the basis of how we know and communicate information; it brings meaning to words and phrases. Concepts like understanding, intent, context, correlation, and natural language are associated with semantics. 

What is Semantic Search?

What does semantics have to do with searching? 

If you are an entrepreneur, an inventor, or an engineer hoping to push your innovative ideas through the patenting process, you need to do some searching to find out whether your idea is patentable and marketable. Professional patent searchers know how to craft search strings using strategically selected keywords and Boolean operators. Designing a query that produces the desired search results using these techniques is an advanced skill that allows people to “speak” to search engines. For others looking to explore patent databases, sitting down to build the perfect keyword or Boolean search is daunting.

Thanks to advancements in search capabilities, patent searching is not just for IP professionals anymore. A semantic search engine allows you to browse a body of data based on meaning rather than keywords alone. Semantic searching humanizes the uncomfortable, robot-like query structuring process. The search engine derives context, associates meanings, and identifies documents containing relevant concepts. This kind of searching even allows you to find connections that you might not have realized exist at all. 

These relationships are discovered through natural language processing and machine learning algorithms. Artificial intelligence allows the search engine to identify meaning within your query and the contents of the database you’re searching. This technology is a huge advancement from keyword-based search engines, where limited input led to limited discovery. 

The History of Semantic Search

While semantic modeling development began in the 1970s, it wasn’t until much later that natural language processing was introduced into search engines. In the mid-2010s, Google began incorporating semantics into its algorithm to deliver more relevant search results. The search engine could now interpret searchers’ phrases as more than just a string of keywords, breaking down complex queries into pieces from which it could decipher meaning. This gave searchers more flexibility in how they input their queries as they looked for everything from recipes to jobs to clothes.

At this point, however, the semantic searching capabilities of a widely used search engine like Google were not powerful enough to search the type of technical databases IP professionals and R&D teams use. These types of searches require more specific output; they need not only a list of relevant results but the ability to further analyze and visualize those results.

Present Semantic Search

Today, finding straightforward information—like a list of search results in Google—is just the first step. Your innovation strategy requires more substantive answers, solutions to problems, and information that leads to next steps. To meet these needs, developments in machine learning and artificial intelligence focused on identifying meaning within the context of both the search term and database. Semantic search engines with better query processing produce more accurate results. 

As the access to and quantity of information increase, you must manage your data pool. An AI search engine based on machine learning is only as healthy as the data on which it feeds. Is the system consuming information from millions of sources of varying quality and credibility filled with billions of ideas (also of varying quality and credibility)? Or, does it have a healthy diet of select databases that contain information significant to your purposes? This is the difference between a search engine that indexes the entire web and a search engine that learns from cataloged technical documents.

Beginning with a semantic search supports specialized analytics capabilities that can identify new strategies and uncover new applications. 

Semantic Patent Searching

The stakes are high during a patent search. You can’t afford to miss relevant information. Semantic patent search engines have the power to interpret the concepts within your query and uncover documents with a shared meaning. Semantic searching, thanks to natural language processing, facilitates a conversation between you and the data. It also makes this technology accessible to not only practiced IP professionals trained in search techniques but engineers, lawyers, and strategists throughout your organization. 

IP.com’s AI-Based Patent Search

The algorithms powering the AI search engine in InnovationQ Plus® have a technical focus, adapted for patents and technical documents. The system recognizes many meanings for one term as well as multiple words with the same meaning and uses machine learning to identify changes in meanings and conceptual relationships over time. The neural network learning algorithms provide the capacity to consume large blocks of text, so your query is in no way limited. 

The intelligence you gain from AI-based patent search is far from artificial. It is applicable. Highly relevant search results lead you to practicable discovery, moving from ideation to precise answers in a matter of minutes. Leverage these discoveries to make critical business decisions; prove your idea is not only new and useful, but also marketable; and find other organizations doing similar work.