USPTO Works to Modernize Patent Processing with AI – Nextgov

Artificial intelligence algorithms will play a critical role in the U.S. Patent and Trademark Office’s intellectual property classification system, part of the agency’s plan to streamline patent processing. 

As many other federal organizations look to integrate emerging technologies and AI in their daily operations, USPTO Chief Information Officer Jamie Holcombe emphasized that his agency will tailor new algorithms as extensions of its patent examiner employees.

“Instead of searching in a large way, like Google, we create searches that are unique to each of the individual examiner’s,” Holcombe told Nextgov. “So actually, the AI is an augmentation of the examiner, not a replacement for an extension of the tools in his own head.”

The program, called the AI/ET Partnership, first works to organize patent application data submitted to the USPTO for approval by siphoning it to the appropriate classification for review, cleaning what Holcombe refers to as “dirty data.” 

Training new machine learning algorithms to effectively make these classification decisions is the natural key to achieving automation, a process Holcombe said relies on the human touch. With around 3,600 potential categories, or art units, new algorithms need consistent examiner training to develop a smart and accurate formula for classification. 

“There is definitely a human check,” he said, adding that USPTO officials have had about 27 months of algorithmic training at this point, and the precision in classification rests at the 93rd percentile. 

USPTO officials have taken the popular route of crowdsourcing the code partially through an open source challenge hosted on Kaggle, a code development platform. Through outreach efforts between the USPTO and academic institutions as well as the agency’s own trademark regional centers, officials are offering a $25,000 prize incentive to develop search phase algorithms to handle patent classification. 

Holcombe is particularly fond of this expansive approach, due to the wide pool of talent it can draw from––a demographic the U.S. government has struggled to tap. Part of the coding task is incorporating existing patents into the algorithms to match with newer patent abstracts, improve past classification and help data scientists get more accurate searches. 

“It’s a way to crowdsource code solutions to common problems,” he said. “We’re up to like…over 2,000 competitors, which is awesome. Anybody can [compete] around the world.”

Consistent with a broader federal initiative to enter into more international agreements in emerging tech development and regulation, Holcombe will be traveling to the United Kingdom and Spain.

“A lot of people don’t think of ideas and innovations the way Americans do, and we have to create those treaties and enforce those ideas,” he said. “Making sure that people around the world can get credit for the ideas that they have and that it’s not stolen by others.”

Past automated patent application classification, Holcombe said that the USPTO is also working on piloting new technologies that can further modernize business processes that are still manual in the agency. 

“Now since we have the internet, and we can go to the cloud, there is a prime time from looking at optimizing that workflow,” he said. “And that’s what we’re doing.”

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USPTO Works to Modernize Patent Processing with AI – Nextgov

Artificial intelligence algorithms will play a critical role in the U.S. Patent and Trademark Office’s intellectual property classification system, part of the agency’s plan to streamline patent processing. 

As many other federal organizations look to integrate emerging technologies and AI in their daily operations, USPTO Chief Information Officer Jamie Holcombe emphasized that his agency will tailor new algorithms as extensions of its patent examiner employees.

“Instead of searching in a large way, like Google, we create searches that are unique to each of the individual examiner’s,” Holcombe told Nextgov. “So actually, the AI is an augmentation of the examiner, not a replacement for an extension of the tools in his own head.”

The program, called the AI/ET Partnership, first works to organize patent application data submitted to the USPTO for approval by siphoning it to the appropriate classification for review, cleaning what Holcombe refers to as “dirty data.” 

Training new machine learning algorithms to effectively make these classification decisions is the natural key to achieving automation, a process Holcombe said relies on the human touch. With around 3,600 potential categories, or art units, new algorithms need consistent examiner training to develop a smart and accurate formula for classification. 

“There is definitely a human check,” he said, adding that USPTO officials have had about 27 months of algorithmic training at this point, and the precision in classification rests at the 93rd percentile. 

USPTO officials have taken the popular route of crowdsourcing the code partially through an open source challenge hosted on Kaggle, a code development platform. Through outreach efforts between the USPTO and academic institutions as well as the agency’s own trademark regional centers, officials are offering a $25,000 prize incentive to develop search phase algorithms to handle patent classification. 

Holcombe is particularly fond of this expansive approach, due to the wide pool of talent it can draw from––a demographic the U.S. government has struggled to tap. Part of the coding task is incorporating existing patents into the algorithms to match with newer patent abstracts, improve past classification and help data scientists get more accurate searches. 

“It’s a way to crowdsource code solutions to common problems,” he said. “We’re up to like…over 2,000 competitors, which is awesome. Anybody can [compete] around the world.”

Consistent with a broader federal initiative to enter into more international agreements in emerging tech development and regulation, Holcombe will be traveling to the United Kingdom and Spain.

“A lot of people don’t think of ideas and innovations the way Americans do, and we have to create those treaties and enforce those ideas,” he said. “Making sure that people around the world can get credit for the ideas that they have and that it’s not stolen by others.”

Past automated patent application classification, Holcombe said that the USPTO is also working on piloting new technologies that can further modernize business processes that are still manual in the agency. 

“Now since we have the internet, and we can go to the cloud, there is a prime time from looking at optimizing that workflow,” he said. “And that’s what we’re doing.”

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Your email address will not be published.

USPTO Works to Modernize Patent Processing with AI – Nextgov

Artificial intelligence algorithms will play a critical role in the U.S. Patent and Trademark Office’s intellectual property classification system, part of the agency’s plan to streamline patent processing. 

As many other federal organizations look to integrate emerging technologies and AI in their daily operations, USPTO Chief Information Officer Jamie Holcombe emphasized that his agency will tailor new algorithms as extensions of its patent examiner employees.

“Instead of searching in a large way, like Google, we create searches that are unique to each of the individual examiner’s,” Holcombe told Nextgov. “So actually, the AI is an augmentation of the examiner, not a replacement for an extension of the tools in his own head.”

The program, called the AI/ET Partnership, first works to organize patent application data submitted to the USPTO for approval by siphoning it to the appropriate classification for review, cleaning what Holcombe refers to as “dirty data.” 

Training new machine learning algorithms to effectively make these classification decisions is the natural key to achieving automation, a process Holcombe said relies on the human touch. With around 3,600 potential categories, or art units, new algorithms need consistent examiner training to develop a smart and accurate formula for classification. 

“There is definitely a human check,” he said, adding that USPTO officials have had about 27 months of algorithmic training at this point, and the precision in classification rests at the 93rd percentile. 

USPTO officials have taken the popular route of crowdsourcing the code partially through an open source challenge hosted on Kaggle, a code development platform. Through outreach efforts between the USPTO and academic institutions as well as the agency’s own trademark regional centers, officials are offering a $25,000 prize incentive to develop search phase algorithms to handle patent classification. 

Holcombe is particularly fond of this expansive approach, due to the wide pool of talent it can draw from––a demographic the U.S. government has struggled to tap. Part of the coding task is incorporating existing patents into the algorithms to match with newer patent abstracts, improve past classification and help data scientists get more accurate searches. 

“It’s a way to crowdsource code solutions to common problems,” he said. “We’re up to like…over 2,000 competitors, which is awesome. Anybody can [compete] around the world.”

Consistent with a broader federal initiative to enter into more international agreements in emerging tech development and regulation, Holcombe will be traveling to the United Kingdom and Spain.

“A lot of people don’t think of ideas and innovations the way Americans do, and we have to create those treaties and enforce those ideas,” he said. “Making sure that people around the world can get credit for the ideas that they have and that it’s not stolen by others.”

Past automated patent application classification, Holcombe said that the USPTO is also working on piloting new technologies that can further modernize business processes that are still manual in the agency. 

“Now since we have the internet, and we can go to the cloud, there is a prime time from looking at optimizing that workflow,” he said. “And that’s what we’re doing.”

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