Patents by Inventor Paul Gribelyuk
Paul Gribelyuk 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: 11928211Abstract: Systems and methods are provided for implementing a machine learning approach to modeling entity behavior. Fixed information and periodically updated information may be utilized to predict the behavior of an entity. By incorporating periodically updated information, the system is able to maintain an up-to-date prediction of each entity's behavior, while also accounting for entity action with respect to ongoing obligations. The system may generate behavior scores for the set of entities. In some embodiments, the behavior scores that are generated may indicate the transactional risk associated with each entity. Using the behavior scores generated, a user may be able to assess the credit riskiness of individual entities and instruct one or more individuals assigned to the entities to take one or more actions based on the credit riskiness of the individual entities.Type: GrantFiled: November 21, 2022Date of Patent: March 12, 2024Assignee: Palantir Technologies Inc.Inventors: Paul Gribelyuk, Han Xu, Kelvin Lau, Pierre Cholet
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Patent number: 11921795Abstract: A data ingestion system normalizes ingested documents and extracts data based on a template that is applied to the documents. In an aspect, the system accesses a document of a document type and determines a template to apply to the document. The system normalizes the document, extracts data values from the document based at least in part on the template, and generates structured data based at least partly on the extracted data.Type: GrantFiled: April 5, 2022Date of Patent: March 5, 2024Assignee: Palantir Technologies Inc.Inventors: Casey Patton, Paul Gribelyuk, Kayo Teramoto, Aaron Rubin, Ankit Shankar
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Publication number: 20230333547Abstract: Systems and methods are provided for identifying relationships between defects. The system may obtain defect items and associated information. Defect items may be compared to one another based on their attributes to determine how related they are. According to the comparisons, defect items may be grouped together into issue items for further analysis by a user. The system may further update a defect comparison model according to user interaction with defect items.Type: ApplicationFiled: June 19, 2023Publication date: October 19, 2023Inventors: Andrew POH, Andre Frederico Cavalheiro MENCK, Arion SPRAGUE, Benjamin GRABHAM, Benjamin LEE, Bianca RAHILL-MARIER, Gregoire OMONT, Jim INOUE, Jonah SCHEINERMAN, Maciej ALBIN, Myles SCOLNICK, Paul GRIBELYUK, Steven FACKLER, Tam-Sanh NGUYEN, Thomas POWELL, William SEATON
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Patent number: 11681282Abstract: Systems and methods are provided for identifying relationships between defects. The system may obtain defect items and associated information. Defect items may be compared to one another based on their attributes to determine how related they are. According to the comparisons, defect items may be grouped together into issue items for further analysis by a user. The system may further update a defect comparison model according to user interaction with defect items.Type: GrantFiled: April 10, 2020Date of Patent: June 20, 2023Assignee: Palantir Technologies Inc.Inventors: Andrew Poh, Andre Frederico Cavalheiro Menck, Arion Sprague, Benjamin Grabham, Benjamin Lee, Bianca Rahill-Marier, Gregoire Omont, Jim Inoue, Jonah Scheinerman, Maciej Albin, Myles Scolnick, Paul Gribelyuk, Steven Fackler, Tam-Sanh Nguyen, Thomas Powell, William Seaton
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Publication number: 20230093712Abstract: Systems and methods are provided for implementing a machine learning approach to modeling entity behavior. Fixed information and periodically updated information may be utilized to predict the behavior of an entity. By incorporating periodically updated information, the system is able to maintain an up-to-date prediction of each entity's behavior, while also accounting for entity action with respect to ongoing obligations. The system may generate behavior scores for the set of entities. In some embodiments, the behavior scores that are generated may indicate the transactional risk associated with each entity. Using the behavior scores generated, a user may be able to assess the credit riskiness of individual entities and instruct one or more individuals assigned to the entities to take one or more actions based on the credit riskiness of the individual entities.Type: ApplicationFiled: November 21, 2022Publication date: March 23, 2023Inventors: Paul GRIBELYUK, Han XU, Kelvin LAU, Pierre CHOLET
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Patent number: 11507657Abstract: Systems and methods are provided for implementing a machine learning approach to modeling entity behavior. Fixed information and periodically updated information may be utilized to predict the behavior of an entity. By incorporating periodically updated information, the system is able to maintain an up-to-date prediction of each entity's behavior, while also accounting for entity action with respect to ongoing obligations. The system may generate behavior scores for the set of entities. In some embodiments, the behavior scores that are generated may indicate the transactional risk associated with each entity. Using the behavior scores generated, a user may be able to assess the credit riskiness of individual entities and instruct one or more individuals assigned to the entities to take one or more actions based on the credit riskiness of the individual entities.Type: GrantFiled: August 24, 2020Date of Patent: November 22, 2022Assignee: Palantir Technologies Inc.Inventors: Paul Gribelyuk, Han Xu, Kelvin Lau, Pierre Cholet
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Publication number: 20220229977Abstract: A data ingestion system normalizes ingested documents and extracts data based on a template that is applied to the documents. In an aspect, the system accesses a document of a document type and determines a template to apply to the document. The system normalizes the document, extracts data values from the document based at least in part on the template, and generates structured data based at least partly on the extracted data.Type: ApplicationFiled: April 5, 2022Publication date: July 21, 2022Inventors: Casey Patton, Paul Gribelyuk, Kayo Teramoto, Aaron Rubin, Ankit Shankar
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Patent number: 11341325Abstract: A data ingestion system normalizes ingested documents and extracts data based on a template that is applied to the documents. In an aspect, the system accesses a document of a document type and determines a template to apply to the document. The system normalizes the document, extracts data values from the document based at least in part on the template, and generates structured data based at least partly on the extracted data.Type: GrantFiled: November 26, 2019Date of Patent: May 24, 2022Assignee: PALANTIR TECHNOLOGIES INC.Inventors: Casey Patton, Paul Gribelyuk, Kayo Teramoto, Aaron Rubin, Ankit Shankar
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Publication number: 20210089712Abstract: A data ingestion system normalizes ingested documents and extracts data based on a template that is applied to the documents. In an aspect, the system accesses a document of a document type and determines a template to apply to the document. The system normalizes the document, extracts data values from the document based at least in part on the template, and generates structured data based at least partly on the extracted data.Type: ApplicationFiled: November 26, 2019Publication date: March 25, 2021Inventors: Casey Patton, Paul Gribelyuk, Kayo Teramoto, Aaron Rubin, Ankit Shankar
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Publication number: 20200387606Abstract: Systems and methods are provided for implementing a machine learning approach to modeling entity behavior. Fixed information and periodically updated information may be utilized to predict the behavior of an entity. By incorporating periodically updated information, the system is able to maintain an up-to-date prediction of each entity's behavior, while also accounting for entity action with respect to ongoing obligations. The system may generate behavior scores for the set of entities. In some embodiments, the behavior scores that are generated may indicate the transactional risk associated with each entity. Using the behavior scores generated, a user may be able to assess the credit riskiness of individual entities and instruct one or more individuals assigned to the entities to take one or more actions based on the credit riskiness of the individual entities.Type: ApplicationFiled: August 24, 2020Publication date: December 10, 2020Inventors: Paul Gribelyuk, Han Xu, Kelvin Lau, Pierre Cholet
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Patent number: 10754946Abstract: Systems and methods are provided for implementing a machine learning approach to modeling entity behavior. Fixed information and periodically updated information may be utilized to predict the behavior of an entity. By incorporating periodically updated information, the system is able to maintain an up-to-date prediction of each entity's behavior, while also accounting for entity action with respect to ongoing obligations. The system may generate behavior scores for the set of entities. In some embodiments, the behavior scores that are generated may indicate the transactional risk associated with each entity. Using the behavior scores generated, a user may be able to assess the credit riskiness of individual entities and instruct one or more individuals assigned to the entities to take one or more actions based on the credit riskiness of the individual entities.Type: GrantFiled: July 5, 2018Date of Patent: August 25, 2020Assignee: Palantir Technologies Inc.Inventors: Paul Gribelyuk, Han Xu, Kelvin Lau, Pierre Cholet
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Publication number: 20200241518Abstract: Systems and methods are provided for identifying relationships between defects. The system may obtain defect items and associated information. Defect items may be compared to one another based on their attributes to determine how related they are. According to the comparisons, defect items may be grouped together into issue items for further analysis by a user. The system may further update a defect comparison model according to user interaction with defect items.Type: ApplicationFiled: April 10, 2020Publication date: July 30, 2020Inventors: Andrew Poh, Andre Frederico Cavalheiro Menck, Arion Sprague, Benjamin Grabham, Benjamin Lee, Bianca Rahill-Marier, Gregoire Omont, Jim Inoue, Jonah Scheinerman, Maciej Albin, Myles Scolnick, Paul Gribelyuk, Steven Fackler, Tam-Sanh Nguyen, Thomas Powell, William Seaton
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Patent number: 10620618Abstract: Systems and methods are provided for identifying relationships between defects. The system may obtain defect items and associated information. Defect items may be compared to one another based on their attributes to determine how related they are. According to the comparisons, defect items may be grouped together into issue items for further analysis by a user. The system may further update a defect comparison model according to user interaction with defect items.Type: GrantFiled: December 20, 2016Date of Patent: April 14, 2020Assignee: Palantir Technologies Inc.Inventors: Andrew Poh, Andre Frederico Cavalheiro Menck, Arion Sprague, Benjamin Grabham, Benjamin Lee, Bianca Rahill-Marier, Gregoire Omont, Jim Inoue, Jonah Scheinerman, Maciej Albin, Myles Scolnick, Paul Gribelyuk, Steven Fackler, Tam-Sanh Nguyen, Thomas Powell, William Seaton
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Publication number: 20200110590Abstract: Techniques for configuring and validating a data pipeline system deployment are described. In an embodiment, a template is a file or data object that describes a package of related jobs. For example, a template may describe a set of jobs necessary for deduplication of data records or a set of jobs performing machine learning on a set of data records. The template can be defined in a file, such as a JSON blob or XML file. For each job specified in the template, the template may identify a set of dataset dependencies that are needed as input for the processing of that job. For each job specified in the template, the template may further identify a set of configuration parameters needed for deployment of the job. In an embodiment, a server uses the template and the configuration parameter values collected via the GUI to generate code for the package of jobs. The code may be stored in a version control system. In an embodiment, the code may be compiled, executed, and deployed to a server for processing the data.Type: ApplicationFiled: December 6, 2019Publication date: April 9, 2020Inventors: David Lisuk, Paul Gribelyuk
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Patent number: 10534595Abstract: Techniques for configuring and validating a data pipeline system deployment are described. In an embodiment, a template is a file or data object that describes a package of related jobs. For example, a template may describe a set of jobs necessary for deduplication of data records or a set of jobs performing machine learning on a set of data records. The template can be defined in a file, such as a JSON blob or XML file. For each job specified in the template, the template may identify a set of dataset dependencies that are needed as input for the processing of that job. For each job specified in the template, the template may further identify a set of configuration parameters needed for deployment of the job. In an embodiment, a server uses the template and the configuration parameter values collected via the GUI to generate code for the package of jobs. The code may be stored in a version control system. In an embodiment, the code may be compiled, executed, and deployed to a server for processing the data.Type: GrantFiled: May 11, 2018Date of Patent: January 14, 2020Assignee: Palantir Technologies Inc.Inventors: David Lisuk, Paul Gribelyuk
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Publication number: 20190243897Abstract: In various example embodiments, a textual identification system is configured to receive a set of search terms and identify a set of textual data based on the search terms. The textual identification system retrieves a data structure including textual identifications for the set of textual data and processes the data structure to generate a modified data structure. The textual identification system sums rows within the modified data structure and identifies one or more elements of interest. The textual identification system then causes presentation of the elements of interest in a first portion of a graphical user interface and the textual identifications for the set of textual data in a second portion of the graphical user interface.Type: ApplicationFiled: March 15, 2019Publication date: August 8, 2019Inventors: Maxim Kesin, Paul Gribelyuk
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Patent number: 10318630Abstract: In various example embodiments, a textual identification system is configured to receive a set of search terms and identify a set of textual data based on the search terms. The textual identification system retrieves a data structure including textual identifications for the set of textual data and processes the data structure to generate a modified data structure. The textual identification system sums rows within the modified data structure and identifies one or more elements of interest. The textual identification system then causes presentation of the elements of interest in a first portion of a graphical user interface and the textual identifications for the set of textual data in a second portion of the graphical user interface.Type: GrantFiled: August 16, 2017Date of Patent: June 11, 2019Assignee: Palantir Technologies Inc.Inventors: Maxim Kesin, Paul Gribelyuk
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Publication number: 20180173212Abstract: Systems and methods are provided for identifying relationships between defects. The system may obtain defect items and associated information. Defect items may be compared to one another based on their attributes to determine how related they are. According to the comparisons, defect items may be grouped together into issue items for further analysis by a user. The system may further update a defect comparison model according to user interaction with defect items.Type: ApplicationFiled: December 20, 2016Publication date: June 21, 2018Inventors: Andrew Poh, Andre Frederico Cavalheiro Menck, Arion Sprague, Benjamin Grabham, Benjamin Lee, Bianca Rahill-Marier, Gregoire Omont, Jim Inoue, Jonah Scheinerman, Maciej Albin, Myles Scolnick, Paul Gribelyuk, Steven Fackler, Tam-Sanh Nguyen, Thomas Powell, William Seaton