Companies with the most machine learning & AI patents worldwide 2010-2019

As of July 2019, IBM was the largest owner of active machine learning and artificial intelligence (AI) patent families worldwide with 5,570 families owned. In 2018, the company had claimed the leading position from Microsoft now ranked second with 5,330 active families owned. Samsung ranked third with slightly above five thousand patent families. The statistic is based on data provided by PatentSight. The three leading companies had a lead of more than a thousand patent families to the Chinese State Grid Company and the Chinese Academy of Sciences who both owned less than four thousand active patent families in this technology field.

Largest machine learning and artificial intelligence (AI) patent owners worldwide from 2010 to July 2019, by number of active patent families

Reporting DateIBMMicrosoftSamsungState Grid CorpChinese Academy of SciencesAlphabetBaiduIntelCanonSiemens
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Source

Release date

July 2019

Region

Worldwide

Survey time period

2010 to July 25th 2019

Supplementary notes

* A patent family is defined as a group of patent documents all of which have the same set of priorities. I.e., it is a group of patent documents that protect the same invention across authorities.

The owner of a patent includes all patents owned by a company and its subsidiaries (by at least 50%) based on comprehensive research on corporate structures, M&A, Spin-offs, company names changes, patent transactions. PatentSight also tracks former names of companies and subsidiaries, ongoing mergers and acquisitions, acquisitions of the past, reassignments, companies that have the same applicant, and transliterations by patent offices.

Technology field definition by Swiss Federal Institute of Intellectual Property – ip-search: "This field covers machine learning as an ubiquitous technology employed in a variety of technical applications, such as image, text or speech analysis in areas from digital data analysis, predictive maintenance to autonomous driving to name only few. In this technology field, machine learning is defined as signal and data processing algorithms, implementing devices and systems which involve schemes based on given observations and outcomes. Distinction to calibration and parametrization, as well as rule-based systems is made in that documents in this field explicitly aim at optimizing generalization abilities of the proposed invention. Thus, the field contains inventions solving classification, clustering and regression in high dimensional data spaces as well as patents describing artificial intelligence of any form. Modelling paradigms include biological inspired approaches, e.g. neural networks or genetic algorithms, kernelized methods such as support vector machines, reinforced or deep learning."

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