Patents by Inventor Matvey Kapilevich
Matvey Kapilevich 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: 11914665Abstract: Multi-modal machine-learning model training techniques for search are described that overcome conventional challenges and inefficiencies to support real time output, which is not possible in conventional training techniques. In one example, a search system is configured to support multi-modal machine-learning model training. This includes use of a preview mode and an expanded mode. In the preview mode, a preview segment is generated as part of real time training of a machine learning model. In the expanded mode, the preview segment is persisted as an expanded segment that is used to train and utilize an expanded machine-learning model as part of search.Type: GrantFiled: February 18, 2022Date of Patent: February 27, 2024Assignee: Adobe Inc.Inventors: Matvey Kapilevich, Margarita R. Savova, Anup Bandigadi Rao, Tung Thanh Mai, Lakshmi Shivalingaiah, Liron Goren Snai, Charles Menguy, Vijeth Lomada, Moumita Sinha, Harleen Sahni
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Patent number: 11899693Abstract: A cluster generation system identifies data elements, from a first binary record, that each have a particular value and correspond to respective binary traits. A candidate description function describing the binary traits is generated, the candidate description function including a model factor that describes the data elements. Responsive to determining that a second record has additional data elements having the particular value and corresponding to the respective binary traits, the candidate description function is modified to indicate that the model factor describes the additional elements. The candidate description function is also modified to include a correction factor describing an additional binary trait excluded from the respective binary traits. Based on the modified candidate description function, the cluster generation system generates a data summary cluster, which includes a compact representation of the binary traits of the data elements and additional data elements.Type: GrantFiled: February 22, 2022Date of Patent: February 13, 2024Assignee: Adobe Inc.Inventors: Yeuk-yin Chan, Tung Mai, Ryan Rossi, Moumita Sinha, Matvey Kapilevich, Margarita Savova, Fan Du, Charles Menguy, Anup Rao
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Publication number: 20230267158Abstract: Multi-modal machine-learning model training techniques for search are described that overcome conventional challenges and inefficiencies to support real time output, which is not possible in conventional training techniques. In one example, a search system is configured to support multi-modal machine-learning model training. This includes use of a preview mode and an expanded mode. In the preview mode, a preview segment is generated as part of real time training of a machine learning model. In the expanded mode, the preview segment is persisted as an expanded segment that is used to train and utilize an expanded machine-learning model as part of search.Type: ApplicationFiled: February 18, 2022Publication date: August 24, 2023Applicant: Adobe Inc.Inventors: Matvey Kapilevich, Margarita R. Savova, Anup Bandigadi Rao, Tung Thanh Mai, Lakshmi Shivalingaiah, Liron Goren Snai, Charles Menguy, Vijeth Lomada, Moumita Sinha, Harleen Sahni
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Publication number: 20230267132Abstract: A cluster generation system identifies data elements, from a first binary record, that each have a particular value and correspond to respective binary traits. A candidate description function describing the binary traits is generated, the candidate description function including a model factor that describes the data elements. Responsive to determining that a second record has additional data elements having the particular value and corresponding to the respective binary traits, the candidate description function is modified to indicate that the model factor describes the additional elements. The candidate description function is also modified to include a correction factor describing an additional binary trait excluded from the respective binary traits. Based on the modified candidate description function, the cluster generation system generates a data summary cluster, which includes a compact representation of the binary traits of the data elements and additional data elements.Type: ApplicationFiled: February 22, 2022Publication date: August 24, 2023Inventors: Yeuk-yin Chan, Tung Mai, Ryan Rossi, Moumita Sinha, Matvey Kapilevich, Margarita Savova, Fan Du, Charles Menguy, Anup Rao
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Patent number: 11720592Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that estimate the overlap between sets of data samples. In particular, in one or more embodiments, the disclosed systems utilize a sketch-based sampling routine and a flexible, accurate estimator to determine the overlap (e.g., the intersection) between sets of data samples. For example, in some implementations, the disclosed systems generate a sketch vector—such as a one permutation hashing vector—for each set of data samples. The disclosed systems further compare the sketch vectors to determine an equal bin similarity estimator, a lesser bin similarity estimator, and a greater bin similarity estimator. The disclosed systems utilize one or more of the determined similarity estimators in generating an overlap estimation for the sets of data samples.Type: GrantFiled: August 10, 2022Date of Patent: August 8, 2023Assignee: Adobe Inc.Inventors: Anup Rao, Tung Mai, Matvey Kapilevich
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Publication number: 20220391407Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that estimate the overlap between sets of data samples. In particular, in one or more embodiments, the disclosed systems utilize a sketch-based sampling routine and a flexible, accurate estimator to determine the overlap (e.g., the intersection) between sets of data samples. For example, in some implementations, the disclosed systems generate a sketch vector—such as a one permutation hashing vector—for each set of data samples. The disclosed systems further compare the sketch vectors to determine an equal bin similarity estimator, a lesser bin similarity estimator, and a greater bin similarity estimator. The disclosed systems utilize one or more of the determined similarity estimators in generating an overlap estimation for the sets of data samples.Type: ApplicationFiled: August 10, 2022Publication date: December 8, 2022Inventors: Anup Rao, Tung Mai, Matvey Kapilevich
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Patent number: 11449523Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that estimate the overlap between sets of data samples. In particular, in one or more embodiments, the disclosed systems utilize a sketch-based sampling routine and a flexible, accurate estimator to determine the overlap (e.g., the intersection) between sets of data samples. For example, in some implementations, the disclosed systems generate a sketch vector—such as a one permutation hashing vector—for each set of data samples. The disclosed systems further compare the sketch vectors to determine an equal bin similarity estimator, a lesser bin similarity estimator, and a greater bin similarity estimator. The disclosed systems utilize one or more of the determined similarity estimators in generating an overlap estimation for the sets of data samples.Type: GrantFiled: November 5, 2020Date of Patent: September 20, 2022Assignee: Adobe Inc.Inventors: Anup Rao, Tung Mai, Matvey Kapilevich
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Publication number: 20220138218Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that estimate the overlap between sets of data samples. In particular, in one or more embodiments, the disclosed systems utilize a sketch-based sampling routine and a flexible, accurate estimator to determine the overlap (e.g., the intersection) between sets of data samples. For example, in some implementations, the disclosed systems generate a sketch vector—such as a one permutation hashing vector—for each set of data samples. The disclosed systems further compare the sketch vectors to determine an equal bin similarity estimator, a lesser bin similarity estimator, and a greater bin similarity estimator. The disclosed systems utilize one or more of the determined similarity estimators in generating an overlap estimation for the sets of data samples.Type: ApplicationFiled: November 5, 2020Publication date: May 5, 2022Inventors: Anup Rao, Tung Mai, Matvey Kapilevich
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Patent number: 11109085Abstract: The present disclosure relates to training a recommendation model to generate trait recommendations using one permutation hashing and populated-value-slot-based densification. In particular, the disclosed systems can train the recommendation model by computing sketch vectors corresponding to traits using one permutation hashing. The disclosed systems can then fill in unpopulated value slots of the sketch vectors using populated-value-slot-based densification. The disclosed systems can combine the resulting densified sketches to generate the trained recommendation model. For example, in some embodiments, the disclosed systems can combine the sketches by generating a plurality of locality sensitive hashing tables based on the sketches. In some embodiments, the disclosed systems generate a count sketch matrix based on the sketches and generate trait embeddings based on the count sketch matrix using spectral embedding.Type: GrantFiled: March 28, 2019Date of Patent: August 31, 2021Assignee: ADOBE INC.Inventors: Anup Rao, Yasin Abbasi Yadkori, Tung Mai, Ryan Rossi, Ritwik Sinha, Matvey Kapilevich, Alexandru Ionut Hodorogea
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Patent number: 11093565Abstract: Systems and methods are disclosed for clustering multiple devices that are associated with particular users by utilizing both probabilistic and deterministic data derived from analytics information on the users. An analytics computing system generates at least one deterministic device cluster that groups a first set of devices associated with a first user. The first set of devices share deterministic user identifiers specific to the first user. The analytics computing system also identifies a probabilistic link between a device in the first set of devices and additional devices. The probabilistic link indicates common usage patterns between two devices. Based on the probabilistic link, the analytics computing system generates a data structure that includes the deterministic device cluster and the additional devices.Type: GrantFiled: February 27, 2019Date of Patent: August 17, 2021Assignee: ADOBE INC.Inventors: Karthik Raman, Nedim Lipka, Matvey Kapilevich
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Publication number: 20210056458Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for intelligently predicting a persona class of a client device and/or target user utilizing an overlap-agnostic machine learning model and distributing persona-based digital content to the client device. In particular, in one or more embodiments, the persona classification system can learn overlap-agnostic machine learning model parameters to apply to user traits in real-time or in offline batches. For example, the persona classification system can train and utilize an overlap-agnostic machine learning model that includes an overlap-agnostic embedding model, a trained user-embedding generation model, and a trained persona prediction model. By applying the learned overlap-agnostic machine learning model parameters to the target user traits, the persona classification system can predict a persona class for sending digital content based on the predicted persona class.Type: ApplicationFiled: August 20, 2019Publication date: February 25, 2021Inventors: Margarita Savova, Matvey Kapilevich, Lakshmi Shivalingaiah, Anup Rao, Alexandru Ionut Hodorogea, Harleen Singh Sahni
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Patent number: 10803471Abstract: Selection of a trait may be received. A complex segment rule may be created that is usable to evaluate one or more qualification events. For example, the segment rule may be usable to evaluate a combined recency and frequency of the one or more qualification events. The qualification events may be based on collected network data associated with the plurality of visitors with each qualification event corresponding to a separate qualification of visitor according to the trait. The qualification events may be evaluated together according to the segment rule. For example, the combined recency and frequency of the one or more qualification events may be evaluated according to the segment rule. Evaluating the segment rule may include estimating a segment population size in real-time.Type: GrantFiled: September 27, 2012Date of Patent: October 13, 2020Assignee: Adobe Inc.Inventors: David M. Weinstein, Matvey Kapilevich, Harleen S. Sahni, Margarita R. Savova, Nicholas M. Jordan, David A. Jared
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Publication number: 20200314472Abstract: The present disclosure relates to training a recommendation model to generate trait recommendations using one permutation hashing and populated-value-slot-based densification. In particular, the disclosed systems can train the recommendation model by computing sketch vectors corresponding to traits using one permutation hashing. The disclosed systems can then fill in unpopulated value slots of the sketch vectors using populated-value-slot-based densification. The disclosed systems can combine the resulting densified sketches to generate the trained recommendation model. For example, in some embodiments, the disclosed systems can combine the sketches by generating a plurality of locality sensitive hashing tables based on the sketches. In some embodiments, the disclosed systems generate a count sketch matrix based on the sketches and generate trait embeddings based on the count sketch matrix using spectral embedding.Type: ApplicationFiled: March 28, 2019Publication date: October 1, 2020Inventors: Anup Rao, Yasin Abbasi Yadkori, Tung Mai, Ryan Rossi, Ritwik Sinha, Matvey Kapilevich, Alexandru Ionut Hodorogea
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Patent number: 10477382Abstract: Certain embodiments involve associating a device with an identifier of a router that is assigned an IP address based on DHCP. For example, connection data that includes the IP address is analyzed to associate devices with the identifier of the router. A determination is made as to whether any usage of the IP address by the devices overlap in time. Devices with overlapping usage are determined to be connecting to online resources via a same router, while devices with non-overlapping usage are determined to be connecting from other routers. A single router identifier is associated with the devices using the same router. This identifier is then used to track the online activity of the associated devices.Type: GrantFiled: April 18, 2018Date of Patent: November 12, 2019Assignee: Adobe Inc.Inventor: Matvey Kapilevich
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Publication number: 20190327600Abstract: Certain embodiments involve associating a device with an identifier of a router that is assigned an IP address based on DHCP. For example, connection data that includes the IP address is analyzed to associate devices with the identifier of the router. A determination is made as to whether any usage of the IP address by the devices overlap in time. Devices with overlapping usage are determined to be connecting to online resources via a same router, while devices with non-overlapping usage are determined to be connecting from other routers. A single router identifier is associated with the devices using the same router. This identifier is then used to track the online activity of the associated devices.Type: ApplicationFiled: April 18, 2018Publication date: October 24, 2019Inventor: Matvey Kapilevich
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Patent number: 10373197Abstract: Tunable algorithmic segment techniques are described. In one or more implementations, a target audience definition is obtained that is input to initiate creation of a look-alike model. The target audience definition indicates traits associated with a baseline group of consumers who have interacted with online resources in a designated manner, such as by buying a product, visiting a website, using a service, and so forth. Tuning parameters designated for the look-alike model are ascertained and the look-alike model is built based on the target audience definition and the tuning parameters. The tuning parameters may include at least a setting selectable to control reach versus accuracy for the look-alike model. Segment data indicative of market segments generated according to the look-alike model may then be exposed for manipulation by a client. The manipulation may include selectable control over the tuning parameters to generate different look-alike groups from the segment data.Type: GrantFiled: December 24, 2012Date of Patent: August 6, 2019Assignee: Adobe Inc.Inventors: Nicholas M. Jordon, Margarita R. Savova, Matvey Kapilevich, Paul Mackles, David M. Weinstein
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Publication number: 20190197072Abstract: Systems and methods are disclosed for clustering multiple devices that are associated with particular users by utilizing both probabilistic and deterministic data derived from analytics information on the users. An analytics computing system generates at least one deterministic device cluster that groups a first set of devices associated with a first user. The first set of devices share deterministic user identifiers specific to the first user. The analytics computing system also identifies a probabilistic link between a device in the first set of devices and additional devices. The probabilistic link indicates common usage patterns between two devices. Based on the probabilistic link, the analytics computing system generates a data structure that includes the deterministic device cluster and the additional devices.Type: ApplicationFiled: February 27, 2019Publication date: June 27, 2019Inventors: Karthik Raman, Nedim Lipka, Matvey Kapilevich
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Patent number: 10255371Abstract: Systems and methods are disclosed for clustering multiple devices that are associated with particular users by utilizing both probabilistic and deterministic data derived from analytics information on the users. An analytics computing system generates at least one deterministic device cluster that groups a first set of devices associated with a first user. The first set of devices share deterministic user identifiers specific to the first user. The analytics computing system also identifies a probabilistic link between a device in the first set of devices and additional devices. The probabilistic link indicates common usage patterns between two devices. Based on the probabilistic link, the analytics computing system generates a data structure that includes the deterministic device cluster and the additional devices.Type: GrantFiled: September 19, 2016Date of Patent: April 9, 2019Assignee: Adobe Systems IncorporatedInventors: Karthik Raman, Nedim Lipka, Matvey Kapilevich
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Publication number: 20180081960Abstract: Systems and methods are disclosed for clustering multiple devices that are associated with particular users by utilizing both probabilistic and deterministic data derived from analytics information on the users. An analytics computing system generates at least one deterministic device cluster that groups a first set of devices associated with a first user. The first set of devices share deterministic user identifiers specific to the first user. The analytics computing system also identifies a probabilistic link between a device in the first set of devices and additional devices. The probabilistic link indicates common usage patterns between two devices. Based on the probabilistic link, the analytics computing system generates a data structure that includes the deterministic device cluster and the additional devices.Type: ApplicationFiled: September 19, 2016Publication date: March 22, 2018Inventors: Karthik Raman, Nedim Lipka, Matvey Kapilevich
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Publication number: 20140180804Abstract: Tunable algorithmic segment techniques are described. In one or more implementations, a target audience definition is obtained that is input to initiate creation of a look-alike model. The target audience definition indicates traits associated with a baseline group of consumers who have interacted with online resources in a designated manner, such as by buying a product, visiting a website, using a service, and so forth. Tuning parameters designated for the look-alike model are ascertained and the look-alike model is built based on the target audience definition and the tuning parameters. The tuning parameters may include at least a setting selectable to control reach versus accuracy for the look-alike model. Segment data indicative of market segments generated according to the look-alike model may then be exposed for manipulation by a client. The manipulation may include selectable control over the tuning parameters to generate different look-alike groups from the segment data.Type: ApplicationFiled: December 24, 2012Publication date: June 26, 2014Applicant: ADOBE SYSTEMS INCORPORATEDInventors: Nicholas M. Jordan, Margarita R. Savova, Matvey Kapilevich, Paul Mackles, David M. Weinstein