{"id":118431,"date":"2020-02-01T12:15:38","date_gmt":"2020-02-01T17:15:38","guid":{"rendered":"https:\/\/ipwatchdog.com\/?p=118431"},"modified":"2020-01-31T18:26:39","modified_gmt":"2020-01-31T23:26:39","slug":"winning-ai-race","status":"publish","type":"post","link":"https:\/\/ipwatchdog.com\/2020\/02\/01\/winning-ai-race\/id=118431\/","title":{"rendered":"Who is Winning the AI Race?"},"content":{"rendered":"
\u201cThe U.S. Patent and Trademark Office in 2019 granted 14,838 patents that mentioned AI or ML, of which 1,275 specifically mentioned AI or ML in their titles or abstracts. That is roughly double the issuance in 2018.\u201d<\/p>\n<\/div>\n
<\/a>Much has been written about how artificial intelligence<\/a> (AI) and machine learning<\/a> (ML) are about to transform the global productivity, working patterns and lifestyles and create enormous wealth. Gartner projects that by 2021, AI augmentation will create $2.9 trillion of business value<\/a> and $6.2 billion hours of worker productivity globally. McKinsey forecasts AI potentially could deliver additional economic output<\/a> of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year. Companies around the globe are all racing to adopt and innovate AI and ML technologies. Indeed, by any account, much progress has been made and the adoption and innovation rates are quickening. But who is winning or leading in the race? A quick review of U.S. patent data may provide a glimpse into the state of the race.<\/p>\n The U.S. Patent and Trademark Office (USPTO) in 2019 granted 14,838 patents that mentioned AI or ML, of which 1,275 specifically mentioned AI or ML in their titles or abstracts. That is roughly double the issuance in 2018, where 8,227 granted patents mentioned AI or ML, and 515 specifically mentioned AI or ML in their titles or abstracts.<\/p>\n The U.S. AI\/ML patents granted in 2019 cover a wide range of areas, from adoption and application of AI\/ML technologies to life science, engineering, computing, e-commerce, to business\/finance to innovation in machine training and neural network technologies themselves. Not surprisingly, classification 706, data processing: artificial intelligence, has the highest number of patents granted that specifically mentioned AI\/ML in either the title or abstract, at 151. Examples of class 706 patents are U.S. Patent No. 10198399<\/a>, Cryptographically Secure Machine Learning, and U.S. Patent No. 10198698<\/a>, Machine Learning Auto Completion of Field. Other classes with more than 50 patents granted that specifically mentioned AI\/ML in either the title or abstract are:<\/p>\n The Art Units designed to handle these applications are:<\/p>\n The average time it took from filing to issuance was 850 days. The patent that took the longest time to issue was U.S. Patent No. 10410308<\/a>, System, method, and device for personal medical care, intelligent analysis, and diagnosis. It took 4,530 days. The fastest issuance was U.S. Patent No. 10470510<\/a>, Systems and Methods for Full Body Measurements Extraction Using Multiple Deep Learning Networks for Body Feature Measurements. It took only 94 days.<\/p>\n Not surprisingly, Big Techs are the top recipients of these patents, with IBM leading the pack; the company received 81 of these patents. Following IBM are Microsoft with 56, Amazon with 51, Cisco and Facebook each with 30, and Google with 26. Apple came in at a distant sixth place, having received only 10 of these patents. Perhaps that explains in part Apple\u2019s recent purchase of Seattle\u2019s Xnor.ai for $200 million, following its high-profile purchase of another Seattle startup, Turi, in 2016.<\/p>\n But Big Tech companies are not the only major recipients of these patents. Among the non-Big Tech firms, Capital One received 50, Fanuc Corp received 36, Accenture received 21, and Bank of America received 15 AI\/ML patents. Many of these are adoption patents, like Capital One\u2019s \u201cUtilizing machine learning with self-support actions to determine support queue positions for support calls\u201d U.S. Patent No. 10263862<\/a>. Others are technology centric patents, like \u201cSystems and methods for accelerating model training in machine learning\u201d U.S. Patent No. 10332035<\/a>, and Systems and methods for providing automated natural language dialogue with customers U.S. Patent No. 10322505<\/a>.<\/p>\n For non-U.S. companies, European giant Siemen leads the pack, receiving 18 such patents, followed by Korean\u2019s LG with 11 and Samsung with 9. Among Japanese companies, NEC was the leader, receiving 6 AI\/ML patents, followed by Sony with 7 and Toyota with 2.<\/p>\n A lot has been written about China\u2019s focus on AI and how the AI\/ML filings in China by Chinese companies have significantly increased. Perhaps because of the time it takes from filing to issuance, we are yet to see the Chinese push in issued U.S. patents. Of the Chinese Big Tech names, Baidu leads the pack, receiving 19 of these patents. Huawei received only 2, and Tencent received only 1. However, one wonders if the picture might be significantly different in 2020 and beyond.<\/p>\n The author obtained the data in this article via an independent search of USPTO databases. <\/em><\/p>\n Image Source: Deposit Photos Much has been written about how artificial intelligence (AI) and machine learning (ML) are about to transform the global productivity, working patterns and lifestyles and create enormous wealth. Gartner projects that by 2021, AI augmentation will create $2.9 trillion of business value and $6.2 billion hours of worker productivity globally. McKinsey forecasts AI potentially could deliver additional economic output of around $13 trillion by 2030, boosting global GDP by about 1.2 percent a year. Companies around the globe are all racing to adopt and innovate AI and ML technologies. Indeed, by any account, much progress has been made and the adoption and innovation rates are quickening. But who is winning or leading in the race? A quick review of U.S. patent data may provide a glimpse into the state of the race.<\/p>\n","protected":false},"author":110147,"featured_media":118433,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[844,6998,845,228,3,37020,187],"tags":[6513,5531,49,50332,33,40,34,172,8727],"yst_prominent_words":[61500,61495,61499,61506,23621,16352,16510,15940,61498,61497,61503,21315,61496,16750,15231,61501,61505,61502,28546,61504],"acf":[],"_links":{"self":[{"href":"https:\/\/ipwatchdog.com\/wp-json\/wp\/v2\/posts\/118431"}],"collection":[{"href":"https:\/\/ipwatchdog.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ipwatchdog.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ipwatchdog.com\/wp-json\/wp\/v2\/users\/110147"}],"replies":[{"embeddable":true,"href":"https:\/\/ipwatchdog.com\/wp-json\/wp\/v2\/comments?post=118431"}],"version-history":[{"count":0,"href":"https:\/\/ipwatchdog.com\/wp-json\/wp\/v2\/posts\/118431\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ipwatchdog.com\/wp-json\/wp\/v2\/media\/118433"}],"wp:attachment":[{"href":"https:\/\/ipwatchdog.com\/wp-json\/wp\/v2\/media?parent=118431"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ipwatchdog.com\/wp-json\/wp\/v2\/categories?post=118431"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ipwatchdog.com\/wp-json\/wp\/v2\/tags?post=118431"},{"taxonomy":"yst_prominent_words","embeddable":true,"href":"https:\/\/ipwatchdog.com\/wp-json\/wp\/v2\/yst_prominent_words?post=118431"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Digging Into the Data<\/strong><\/h2>\n
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Who\u2019s Leading the Pack?<\/strong><\/h2>\n
Eye on China<\/strong><\/h2>\n
\nImage ID: 33795973
\nCopyright: londondeposit\u00a0<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"