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Data and Decisions: The risks in relying on inaccurate patent data

by Carsten Guderian, on May 11, 2021 10:46:11 AM

Earlier installments in our series on patent data quality covered which fields are most impacted by inaccurate data in patent applications. In our final post, we're going to discuss a few of the specific instances where bad data can lead to expensive decisions. Because when organizations base critical decisions about how their business operates on unreliable data, those outcomes can be the core driver of negative business results.

One of the main challenges faced by patent information users is the sheer volume of data available for analysis. To put this in perspective, in 2019 there were about 15million patents in force and in total there are more than 13 million active patent families, as of December 31, 2020, in the PatentSight database. 

With such large volumes of data to work with, the risk of overlooking incorrect or incomplete data increases exponentially. IP departments all over the world use large amounts of patent data for various analyses and studies. It is based on these analyses that management makes strategic decisions about the future, litigations, or mergers and acquisitions. According to a study by the International Bureau at the WIPO, applicant name inconsistencies can cause errors in the findings of any of the following patent analyses, leading to inherently flawed decision making.

Freedom to operate (FTO) searches—help organizations develop and market products without legal liability to 3rd parties. Inaccurate information returned from FTO searches exposes organizations to the potential liability of infringing a patent owned by another company. The cost of defending against patent infringement lawsuits can cost north of 1 million dollars and cases frequently do. If the applicant names in the data that a copmany uses to perform an FTO search contains errors, it becomes an uphill battle for getting their invention patented, from the get-go.

Company Analyses—are performed by innovation leaders to evaluate portfolios of companies (especially ones that own large numbers of patents) to accurately strategize their own next moves in terms of mergers and acquisitions, licensing negotiations, or litigation. Imagine deciding to acquire a company based on a patent database that does not contain the correct owner names? That’s going to be a multi-million dollar mistake in the balance sheet.

Portfolio benchmarking—conducted without accurate patent ownership information, would be an absolute waste of resources. Take the case of Panasonic for instance. In 2008, when Matsushita Electric changed its name to Panasonic Corporation there were 10,00 patents granted under the name of Matsushita Electric. Today, if anybody is benchmarking Panasonic's innovation prowess based on a patent database in which these patents have not been correctly reassigned to Panasonic, they do not get to see the full picture.

Technology landscape analyses— shed light on the innovation activity in a specific technology field. This type of analysis relies heavily on ownership information. If all the companies active in a technology field aren’t identified, businesses will miss innovation opportunities to gain competitive advantages.

Each one of the assessments mentioned above is critical to businesses that want to gain data-driven business advantage and mitigate the risk associated with negative outcomes.

Download our white paper “A Handbook for Patent Data Quality" to learn how PatentSight helps patent data users avoid such expensive mistakes.

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Topics:IP Data QualityBig DataBenchmarkingPatent Landscape


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