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Artificial Intelligence

The Power (and Shortcomings) of AI-based Patent Search

AI-driven search is revolutionizing patent search tools. The most precise and sophisticated prior art tools on the market utilize AI to streamline and automate searches. Engineering and IP teams are both able to work faster, identifying opportunities and resolving hurdles more quickly thanks to machine-learning and deep-learning AI algorithms.

With millions of results to search through in both domestic and international databases, many in foreign languages or non-standardized formats, it can be difficult to know if searches on a particular topic are exhaustive. AI makes it easier by searching more, faster—and with higher-relevance results. Patent research and verification projects that used to take days, even weeks, can be consolidated into a matter of minutes.

Pitfalls of AI Searches

However, because AI search tools are largely based on the data accumulated from thousands of human-based manual searches they act as a proxy for both human intent and error. By failing to highlight the correct term taxonomies, irrelevant results can appear valid. That’s why it is key to rely on both manual and AI searches in a thoughtful and strategic way that incorporates the wide net of AI with the precision of Boolean search.

In addition to the aforementioned issues of searching multiple databases containing data of varying formats, AI can lean into existing information biases to favor certain results over others. You may not automatically know where the blind spots are. This is particularly true for search engines with “black box” algorithms, which are therefore not always well-understood by those using them. Without knowing the basic criteria or even the conceptual basis for how a search generates results, it leaves you uncertain that you have the complete picture.

Adding Manual Boolean Search to Patent Research Projects

When engineers are tasked with moving quickly to identify the patentability of a project, they have to consider both accuracy and speed. While AI searches are certainly faster, they do not totally replace the role of traditional Boolean searches for accuracy. As prior art searches can require highly in-depth analyses of legal, academic, and commercial documents (like’s Corporate Tree integration), it is important to approach each search knowing when to add manual techniques to your workflow.

An effective tactic is a two-pronged approach combining both manual and semantic searches; however, your order of operations can depend on the scope and goals of your research project. If you don’t know exactly what you are looking for—or perhaps do, and expect your search to yield an unmanageable level of results—you can start with AI to act as a filtering tool to eliminate irrelevant data.

Classic Boolean keyword searches involve using strings of language that can be manipulated by operators or modifiers to render results containing exact phrases or instances of words. AI searches use a technology called natural language processing and machine learning to identify semantically-related phrases. For instance, searching for the term “patent” would not only generate all instances of the word “patent” but also related terms such as “intellectual property” or “prior art.”

Using AI-automated search initially can help identify families of related terms, narrowing down results within which you can use basic or advanced Boolean searches to find more targeted results. The result is an integrated search technique that can be further hybridized with’s enhanced Boolean search tools.