Patents by Inventor Jacob Burnim
Jacob Burnim has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 10666674Abstract: A machine learning-based system and method for identifying digital threats that includes implementing a machine learning-based digital threat mitigation service over a distributed network of computers; constructing, by the machine learning-based digital threat mitigation service, a subscriber-specific machine learning ensemble that includes a plurality of distinct machine learning models, wherein each of the plurality of distinct machine learning models is configured to perform a distinct machine learning task for identifying a digital threat or digital fraud; constructing a corpus of subscriber-specific digital activity data for training the plurality of distinct machine learning models of the subscriber-specific ensemble; training the subscriber-specific ensemble using at least the corpus of subscriber-specific digital activity data; and deploying the subscriber-specific ensemble.Type: GrantFiled: October 16, 2019Date of Patent: May 26, 2020Assignee: Sift Science, Inc.Inventors: Fred Sadaghiani, Alex Paino, Jacob Burnim, Janice Lan
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Patent number: 10572832Abstract: Systems and methods include: collecting digital threat scores of an incumbent digital threat machine learning model; identifying incumbent and successor digital threat score distributions; identifying quantiles data of the incumbent digital threat score distribution; collecting digital threat scores of a successor digital threat machine learning model; calibrating the digital threat scores of the successor digital threat score distribution based on the quantiles data of the incumbent digital threat score distribution and the incumbent digital threat score distribution; and in response to remapping the digital threat scores of the successor digital threat score distribution, publishing the successor digital scores in lieu of the incumbent digital threat scores based on requests for digital threat scores.Type: GrantFiled: May 14, 2019Date of Patent: February 25, 2020Assignee: Sift Science, Inc.Inventors: Fred Sadaghiani, Aaron Beppu, Jacob Burnim, Alex Paino
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Patent number: 10491617Abstract: A machine learning-based system and method for identifying digital threats that includes implementing a machine learning-based digital threat mitigation service over a distributed network of computers; constructing, by the machine learning-based digital threat mitigation service, a subscriber-specific machine learning ensemble that includes a plurality of distinct machine learning models, wherein each of the plurality of distinct machine learning models is configured to perform a distinct machine learning task for identifying a digital threat or digital fraud; constructing a corpus of subscriber-specific digital activity data for training the plurality of distinct machine learning models of the subscriber-specific ensemble; training the subscriber-specific ensemble using at least the corpus of subscriber-specific digital activity data; and deploying the subscriber-specific ensemble.Type: GrantFiled: May 31, 2019Date of Patent: November 26, 2019Assignee: Sift Science, Inc.Inventors: Fred Sadaghiani, Alex Paino, Jacob Burnim, Janice Lan
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Patent number: 10482395Abstract: Systems and methods include: collecting digital event data for the digital account; using a trained machine learning model to extract account takeover (ATO) risk features from the collected digital event data; evaluating the extracted ATO risk features of the collected digital event data of the digital account against a plurality of ATO risk heuristics; identifying one or more of the plurality of ATO risk heuristics that is triggered by the extracted ATO risk features, wherein one or more of the plurality of ATO risk heuristics may be triggered if at least a subset of the extracted ATO risk features matches requirements of the one or more ATO risk heuristics; and generating an ATO risk assessment for the digital account using the one or more triggered ATO risk heuristics.Type: GrantFiled: December 4, 2018Date of Patent: November 19, 2019Assignee: Sift Science, Inc.Inventors: Fred Sadaghiani, Keren Gu, Alex Paino, Jacob Burnim, Thomas Schiavone
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Patent number: 10462172Abstract: Systems and methods include implementing a remote machine learning service that collects digital event data; collecting incumbent digital threat scores generated by an incumbent machine learning model and successor digital threat scores generated by a successor digital threat machine learning (ML) model; implementing anomalous-shift-detection that detects whether the successor digital threat scores of the successor digital threat ML model produces an anomalous shift; if the anomalous shift is detected by the machine learning model validation system, blocking a deployment of the successor digital threat model to a live ensemble of digital threat scoring models; or if the anomalous shift is not detected by the machine learning model validation system, deploying the successor digital threat ML model by replacing the incumbent digital threat ML model in a live ensemble of digital threat scoring models with the successor digital threat ML model.Type: GrantFiled: May 16, 2019Date of Patent: October 29, 2019Assignee: Sift Science, Inc.Inventors: Fred Sadaghiani, Keren Gu, Vera Dadok, Alex Paino, Jacob Burnim
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Publication number: 20190272373Abstract: Systems and methods include: collecting digital threat scores of an incumbent digital threat machine learning model; identifying incumbent and successor digital threat score distributions; identifying quantiles data of the incumbent digital threat score distribution; collecting digital threat scores of a successor digital threat machine learning model; calibrating the digital threat scores of the successor digital threat score distribution based on the quantiles data of the incumbent digital threat score distribution and the incumbent digital threat score distribution; and in response to remapping the digital threat scores of the successor digital threat score distribution, publishing the successor digital scores in lieu of the incumbent digital threat scores based on requests for digital threat scores.Type: ApplicationFiled: May 14, 2019Publication date: September 5, 2019Inventors: Fred Sadaghiani, Aaron Beppu, Jacob Burnim, Alex Paino
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Patent number: 10402828Abstract: Systems and methods include: implementing a first machine learning model to generate an output of a global digital threat score for an online activity based on an input of the collected digital event data; implementing a second machine learning model that generates a category inference of a category of digital fraud or a category of digital abuse from a plurality of digital fraud or digital abuse categories; selecting a third machine learning model from an ensemble of digital fraud or digital abuse machine learning models based on the category inference generated by the second machine learning model, wherein the ensemble of digital fraud or digital abuse machine learning models comprise a plurality of disparate digital fraud or digital abuse category-specific machine learning models; and implementing the selected third machine learning model to generate a digital fraud or digital abuse category-specific threat score based on the digital event data.Type: GrantFiled: April 10, 2019Date of Patent: September 3, 2019Assignee: Sift Science, Inc.Inventors: Fred Sadaghiani, Alex Paino, Jacob Burnim, Keren Gu, Gary Lee, Noah Grant, Eugenia Ho, Doug Beeferman
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Publication number: 20190236610Abstract: Systems and methods include: implementing a first machine learning model to generate an output of a global digital threat score for an online activity based on an input of the collected digital event data; implementing a second machine learning model that generates a category inference of a category of digital fraud or a category of digital abuse from a plurality of digital fraud or digital abuse categories; selecting a third machine learning model from an ensemble of digital fraud or digital abuse machine learning models based on the category inference generated by the second machine learning model, wherein the ensemble of digital fraud or digital abuse machine learning models comprise a plurality of disparate digital fraud or digital abuse category-specific machine learning models; and implementing the selected third machine learning model to generate a digital fraud or digital abuse category-specific threat score based on the digital event data.Type: ApplicationFiled: April 10, 2019Publication date: August 1, 2019Inventors: Fred Sadaghiani, Alex Paino, Jacob Burnim, Keren Gu, Gary Lee, Noah Grant, Eugenia Ho, Doug Beeferman
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Patent number: 10341374Abstract: Systems and methods include implementing a remote machine learning service that collects digital event data; collecting incumbent digital threat scores generated by an incumbent machine learning model and successor digital threat scores generated by a successor digital threat machine learning (ML) model; implementing anomalous-shift-detection that detects whether the successor digital threat scores of the successor digital threat ML model produces an anomalous shift; if the anomalous shift is detected by the machine learning model validation system, blocking a deployment of the successor digital threat model to a live ensemble of digital threat scoring models; or if the anomalous shift is not detected by the machine learning model validation system, deploying the successor digital threat ML model by replacing the incumbent digital threat ML model in a live ensemble of digital threat scoring models with the successor digital threat ML model.Type: GrantFiled: November 20, 2018Date of Patent: July 2, 2019Assignee: Sift Science, Inc.Inventors: Fred Sadaghiani, Keren Gu, Vera Dadok, Alex Paino, Jacob Burnim
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Patent number: 10339472Abstract: Systems and methods include: collecting digital threat scores of an incumbent digital threat machine learning model; identifying incumbent and successor digital threat score distributions; identifying quantiles data of the incumbent digital threat score distribution; collecting digital threat scores of a successor digital threat machine learning model; calibrating the digital threat scores of the successor digital threat score distribution based on the quantiles data of the incumbent digital threat score distribution and the incumbent digital threat score distribution; and in response to remapping the digital threat scores of the successor digital threat score distribution, publishing the successor digital scores in lieu of the incumbent digital threat scores based on requests for digital threat scores.Type: GrantFiled: March 30, 2018Date of Patent: July 2, 2019Assignee: Sift Science, Inc.Inventors: Fred Sadaghiani, Aaron Beppu, Jacob Burnim, Alex Paino
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Patent number: 10296912Abstract: Systems and methods include: implementing a first machine learning model to generate an output of a global digital threat score for an online activity based on an input of the collected digital event data; implementing a second machine learning model that generates a category inference of a category of digital fraud or a category of digital abuse from a plurality of digital fraud or digital abuse categories; selecting a third machine learning model from an ensemble of digital fraud or digital abuse machine learning models based on the category inference generated by the second machine learning model, wherein the ensemble of digital fraud or digital abuse machine learning models comprise a plurality of disparate digital fraud or digital abuse category-specific machine learning models; and implementing the selected third machine learning model to generate a digital fraud or digital abuse category-specific threat score based on the digital event data.Type: GrantFiled: September 21, 2018Date of Patent: May 21, 2019Assignee: Sift Science, Inc.Inventors: Fred Sadaghiani, Alex Paino, Jacob Burnim, Keren Gu, Gary Lee, Noah Grant, Eugenia Ho, Doug Beeferman
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Publication number: 20190108334Abstract: Systems and methods include: collecting digital event data for the digital account; using a trained machine learning model to extract account takeover (ATO) risk features from the collected digital event data; evaluating the extracted ATO risk features of the collected digital event data of the digital account against a plurality of ATO risk heuristics; identifying one or more of the plurality of ATO risk heuristics that is triggered by the extracted ATO risk features, wherein one or more of the plurality of ATO risk heuristics may be triggered if at least a subset of the extracted ATO risk features matches requirements of the one or more ATO risk heuristics; and generating an ATO risk assessment for the digital account using the one or more triggered ATO risk heuristics.Type: ApplicationFiled: December 4, 2018Publication date: April 11, 2019Inventors: Fred Sadaghiani, Keren Gu, Alex Paino, Jacob Burnim, Thomas Schiavone
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Publication number: 20190034932Abstract: Systems and methods include: implementing a first machine learning model to generate an output of a global digital threat score for an online activity based on an input of the collected digital event data; implementing a second machine learning model that generates a category inference of a category of digital fraud or a category of digital abuse from a plurality of digital fraud or digital abuse categories; selecting a third machine learning model from an ensemble of digital fraud or digital abuse machine learning models based on the category inference generated by the second machine learning model, wherein the ensemble of digital fraud or digital abuse machine learning models comprise a plurality of disparate digital fraud or digital abuse category-specific machine learning models; and implementing the selected third machine learning model to generate a digital fraud or digital abuse category-specific threat score based on the digital event data.Type: ApplicationFiled: September 21, 2018Publication date: January 31, 2019Inventors: Fred Sadaghiani, Alex Paino, Jacob Burnim, Keren Gu, Gary Lee, Noah Grant, Eugenia Ho, Doug Beeferman
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Publication number: 20190018956Abstract: Systems and methods include: collecting digital event data for the digital account; using a trained machine learning model to extract account takeover (ATO) risk features from the collected digital event data; evaluating the extracted ATO risk features of the collected digital event data of the digital account against a plurality of ATO risk heuristics; identifying one or more of the plurality of ATO risk heuristics that is triggered by the extracted ATO risk features, wherein one or more of the plurality of ATO risk heuristics may be triggered if at least a subset of the extracted ATO risk features matches requirements of the one or more ATO risk heuristics; and generating an ATO risk assessment for the digital account using the one or more triggered ATO risk heuristics.Type: ApplicationFiled: December 14, 2017Publication date: January 17, 2019Inventors: Fred Sadaghiani, Keren Gu, Alex Paino, Jacob Burnim, Thomas Schiavone
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Publication number: 20190019109Abstract: Systems and methods include: collecting digital threat scores of an incumbent digital threat machine learning model; identifying incumbent and successor digital threat score distributions; identifying quantiles data of the incumbent digital threat score distribution; collecting digital threat scores of a successor digital threat machine learning model; calibrating the digital threat scores of the successor digital threat score distribution based on the quantiles data of the incumbent digital threat score distribution and the incumbent digital threat score distribution; and in response to remapping the digital threat scores of the successor digital threat score distribution, publishing the successor digital scores in lieu of the incumbent digital threat scores based on requests for digital threat scores.Type: ApplicationFiled: March 30, 2018Publication date: January 17, 2019Inventors: Fred Sadaghiani, Aaron Beppu, Jacob Burnim, Alex Paino
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Patent number: 10181032Abstract: Systems and methods include: collecting digital event data for the digital account; using a trained machine learning model to extract account takeover (ATO) risk features from the collected digital event data; evaluating the extracted ATO risk features of the collected digital event data of the digital account against a plurality of ATO risk heuristics; identifying one or more of the plurality of ATO risk heuristics that is triggered by the extracted ATO risk features, wherein one or more of the plurality of ATO risk heuristics may be triggered if at least a subset of the extracted ATO risk features matches requirements of the one or more ATO risk heuristics; and generating an ATO risk assessment for the digital account using the one or more triggered ATO risk heuristics.Type: GrantFiled: December 14, 2017Date of Patent: January 15, 2019Assignee: Sift Science, Inc.Inventors: Fred Sadaghiani, Keren Gu, Alex Paino, Jacob Burnim, Thomas Schiavone
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Patent number: 10108962Abstract: Systems and methods include: implementing a first machine learning model to generate an output of a global digital threat score for an online activity based on an input of the collected digital event data; implementing a second machine learning model that generates a category inference of a category of digital fraud or a category of digital abuse from a plurality of digital fraud or digital abuse categories; selecting a third machine learning model from an ensemble of digital fraud or digital abuse machine learning models based on the category inference generated by the second machine learning model, wherein the ensemble of digital fraud or digital abuse machine learning models comprise a plurality of disparate digital fraud or digital abuse category-specific machine learning models; and implementing the selected third machine learning model to generate a digital fraud or digital abuse category-specific threat score based on the digital event data.Type: GrantFiled: April 19, 2018Date of Patent: October 23, 2018Assignee: Sift Science, Inc.Inventors: Fred Sadaghiani, Alex Paino, Jacob Burnim, Keren Gu, Gary Lee, Noah Grant, Eugenia Ho, Doug Beeferman
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Patent number: 9978067Abstract: Systems and methods include: collecting digital event data from at least one remote source of digital event data; using the collected digital event data as input into primary machine learning ensemble that predicts the likelihood of digital fraud and/or digital abuse; generating by the machine learning system the global digital threat score; identifying a sub-request for a specific digital threat score for a digital abuse type; in response to identifying the sub-request, providing the input of the collected digital event data to a secondary machine learning model ensemble of the machine learning system that predicts a likelihood of the identified digital abuse type; generating by the secondary machine learning ensemble the specific digital threat score for the digital abuse type based on the input of the collected digital event data; and transmitting the global digital threat score and the specific digital threat score for the identified digital abuse type.Type: GrantFiled: July 18, 2017Date of Patent: May 22, 2018Assignee: Sift Science, Inc.Inventors: Fred Sadaghiani, Alex Paino, Jacob Burnim, Keren Gu, Gary Lee, Noah Grant, Eugenia Ho, Doug Beeferman