5 Consequences of Patent Data Errors: The Applicant Name Field

5 Consequences Of Patent Data Errors: The Applicant Name Field

Carsten Guderian


Intellectual Property (IP) departments all over the world use large amounts of patent data to guide their strategic decision-making processes around corporate functions like legal, research and development, mergers and acquisitions, business development, procurement, strategy, corporate venture capital, and marketing. The analysis of this information – Patent Analytics- is a part of an organization’s routine data-driven intelligence activities. But just like data analysis across any functional part of an organization, if the data behind the analysis is inaccurate, the results of the analysis will be just as inaccurate and unreliable. For patent information users, the consequences of patent data errors can range anywhere from wrongful litigation to incorrect competitive benchmarking, to misguided strategic decisions all of which end up costing organizations time and money.  

One field, many errors

Of all of the information that is contained in a patent document, one of the most problematic for data accuracy seems basic and straightforward: the applicant name field. Turns out, it’s anything but. This field captures the name of the company/entity that has filed for the respective patent application, claiming ownership to the underlying invention the patent protects. Patent data inaccuracy here can result in costly and damaging repercussions to the business. Let’s explore them.

1. Patent assigned to subsidiaries

Large organizations, operating in highly innovative and competitive industries, will often try to protect their innovations from competitors by filing patents through a subsidiary or even a subsidiary of a subsidiary. However, filing this way makes it extremely difficult to link the innovations back to the parent company. 

From a patent data consumer’s perspective, if these patents are not correctly assigned to the ultimate parent company that owns the subsidiary, any analysis performed with these patent data errors will be inherently flawed, since the actual current portfolios are not being analyzed. Linking subsidiaries to their parent companies requires extensive research and knowledge of the international business landscape. 

2. Alternate names of the same applicant

Patent offices usually publish documents exactly as they were filed. Since every applicant does not follow a uniform code while filing the application documents, one company can have various names, depending on the people who filled out the form when applying for the patent. 

Here’s an example –  in the case of an academic applicant, ETH Zürich, patents are filed under the following name variations:  ETH Zürich,  Eidgenössische Technische Hochschule Zürich, and ETH Zurich. All different names to the untrained eye, but the patents rightfully belong to one ultimate commercial owner, the ETH University in Zurich, Switzerland. 

3. Different assignee names in one patent family

When two patents belonging to the same family of patents are reassigned to different companies in each of the respective countries, there are no widely accepted guidelines for assigning the patent family. According to the EPO, a patent family is defined as a collection of patent applications covering the same or similar technical invention.

This means that if two independent companies, A (operating in the U.S.) and B (operating in Beijing, China) acquire patents that protect the same technology within their respective authorities, there are no official guidelines that explain which company owns the whole patent family. Although this is rare, this issue has the potential to skew the portfolio sizes of companies impacting analytics with unadjusted data.

4. Different or multiple applicant names for patents in the same patent family

This is an issue that rises when multiple companies own patents belonging to the same patent family. In this case, it would be impossible for a user to identify exactly to whom a patent belongs. This can also be the case when a patent is co-owned by multiple companies. If the two companies from the previous example worked together on an invention and filed for a patent together, then the patent is co-owned by company A and company B. 

5. Misspellings of the applicant name

There can be any number of variations of spelling the same company name in an application. International Business Machines Corporation has patents filed under approximately 20 variations of the spelling and usage of the IBM acronym. If this is not corrected before data is fed into a database, there can be a significant difference in the number and quality of patents owned by the company. This type of error can lead to incorrect and dangerous decision-making, especially at the corporate level.

Data cleansing: the right way do do it

The right combination of people and technology can enable patent data users to mitigate the risks associated with performing corporate functions against incorrect or incomplete data. 

At LexisNexis, we do that with our product LexisNexis® PatentSight®, the market-leading patent analytics tool that offers a wealth of data for corporate analysis that has been combed for patent data errors so that decision-makers can feel confident that they are using the highest-quality information to make informed decisions.  The PatentSight database contains more than 48 million patent families (both active and inactive patents) as of today, and we receive weekly data updates from the various patent offices around the world, that need to be then checked for correctness of the matched ultimate commercial owner using our proprietary algorithm. 

Once that process is complete, we have a team of global researchers who work with our industry partners and customers, focused on checking the patent data in our database with a fine-tooth comb to correct inaccuracies. They analyze individual patent documents and the linked legal status information to check for any reassignments, sales, mergers, and acquisitions, or any other events that could impact data accuracy. 

Learn more about PatentSight®.

Excellent data quality is the foundation of reliable analyses. Learn how PatentSight enhances patent data here.   

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About the author: Carsten Guderian

Carsten is a Senior Project Leader and has a background in Economics and Business Administration, particularly innovation management and patent analytics. He has been affiliated with PatentSight since 2012.

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