• last updated : 20 April, 2023

Are You Using These Effective Patent Search Methods?

Category: Blog
patent search methods

We’ve all heard that patent and non-patent literature are rich sources of information that can lead to your company’s next big breakthrough. However, uncovering the insights that propel your company forward necessitates the use of proper patent search methods. Here are four effective patent search methods for making the most of your patent search tool. 

1. How to Find Search Results That Are Highly Relevant? 

Finding the most relevant patent data can be difficult with the hundreds of thousands of patents granted each year in the United States alone. However, highly relevant patents are most likely what you seek. 

The best search queries are six to twelve words long, giving our AI engine a good idea of what you’re looking for. The artificial intelligence (AI) that powers XLSCOUT’s Novelty Checker has been specifically trained on language as it pertains to patent data, giving you trust in the results of your search. 

2. How to Integrate Patent Searching into Ideation? 

Patent searching has traditionally been left to IP specialists and legal teams later in the innovation lifecycle. Now, engineers and scientists are reviewing prior art early in the patent process in order to narrow their focus and ensure that their ideas are novel. You’re using fewer filters at this point to broaden your search across patent and non-patent literature.

Our Ideation Dashboard suggests concepts based on machine learning to help you improve your ideas. It also provides insights about the novelty strength of your idea.

3. How to Examine the Patent Landscape in Depth? 

When examining the patent landscape, detail-oriented patent search methods can assist you in delving deeper into a particular technology. At this point, not only should your search queries be more specific, but you can also enter a whole invention disclosure into the search field. This provides a large number of concepts for the AI engine to search for. Users can apply filters to eliminate undesirable outcomes. Then, using the visualization features of the tool, you can even compare patent portfolios in your field of interest.

The Techscaper tool from XLSCOUT allows users to conduct in-depth analysis of the technology landscapes they are interested in. Furthermore, they can compare and analyze the patent portfolios of their competitors using our competitive intelligence dashboard – Company Explorer.

4. How to Integrate AI and Traditional Search Methods? 

Traditionally, syntax searching means that your search results are based on the exact words and phrases you enter. It also includes any Boolean operators you use to refine or broaden your search.

The concept described in your queries is used by AI-powered search to find relevant results. It may or may not use the same words and phrases to describe a comparable technology. You can broaden your search and understanding of a technology landscape by using conceptual searching. Perhaps even identifying new competitors in a space you wouldn’t have known to search for and therefore wouldn’t have discovered using only syntax searching. The reverse is also an effective patent search method. Here, you can use the AI engine to determine which concepts appear most often in the patents of your competitors. You can also use those concepts to seek relevant patent data.

Our Methodology 

XLSCOUT put the use of reinforcement learning to its AI-based Novelty Checker patent search tool to get quality patent research reports in just 10 minutes. The Novelty Checker uses reinforcement learning to filter the noise from the prior art by pulling up the relevant results on top of the list. To be precise, it assists in conducting patentability search/patent research to help you ensure that your innovation is unique. By selecting a few relevant and non-relevant results, users can apply them to the result set. The system takes the user’s feedback and then learns from it. It uses conceptual searching and re-ranks the results by bringing the quality results to the top and sending the noise to the bottom.   

Without reinforcement learning, users go through hundreds of results manually. By applying this process, users can skip going through the non-relevant results. Users can then view the Top-10 or Top-20 results for each criterion to perform a prior-art analysis for idea validation. They can quickly generate an automated novelty and patentability search report by selecting these Top-10 or 20 results. The Novelty Checker prior art search reports include a list of results along with relevant text mapping with the key features of the invention for enabling quick decision-making. 

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