Patents by Inventor Pradipkumar Dineshbhai Gajjar
Pradipkumar Dineshbhai Gajjar 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: 11361017Abstract: Techniques are described that exclude use of “stop-fingerprints” from media database formation and search query to an automatic content recognition (ACR) systems based on media content fingerprints updated by stop-fingerprint analysis. A classification process is presented which takes in fingerprints from reference media files as an input and produces a modified set of fingerprints as an output by applying a novel stop-fingerprint classification algorithm. Architecture for the distributed stop-fingerprint generation is presented. Various cases, as stop-fingerprints generation for the entire reference database, stop-fingerprints generation for the individual reference fingerprint files, and temporal fingerprint classification obtained through intermediate steps of the temporal fingerprint classification algorithm are presented. A hash-based signature classification algorithm is also described.Type: GrantFiled: July 15, 2020Date of Patent: June 14, 2022Assignee: Roku, Inc.Inventors: Sunil Suresh Kulkarni, Pradipkumar Dineshbhai Gajjar, Jose Pio Pereira, Preshant Ramanathan, Mihailo M. Stojancic, Shashank Merchant
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Patent number: 11188587Abstract: The overall architecture and details of a scalable video fingerprinting and identification system that is robust with respect to many classes of video distortions is described. In this system, a fingerprint for a piece of multimedia content is composed of a number of compact signatures, along with traversal hash signatures and associated metadata. Numerical descriptors are generated for features found in a multimedia clip, signatures are generated from these descriptors, and a reference signature database is constructed from these signatures. Query signatures are also generated for a query multimedia clip. These query signatures are searched against the reference database using a fast similarity search procedure, to produce a candidate list of matching signatures. This candidate list is further analyzed to find the most likely reference matches. Signature correlation is performed between the likely reference matches and the query clip to improve detection accuracy.Type: GrantFiled: April 17, 2019Date of Patent: November 30, 2021Assignee: Roku, Inc.Inventors: Jose Pio Pereira, Sunil Suresh Kulkarni, Shashank Merchant, Prashant Ramanathan, Pradipkumar Dineshbhai Gajjar
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Patent number: 11163818Abstract: The overall architecture and details of a scalable video fingerprinting and identification system that is robust with respect to many classes of video distortions is described. In this system, a fingerprint for a piece of multimedia content is composed of a number of compact signatures, along with traversal hash signatures and associated metadata. Numerical descriptors are generated for features found in a multimedia clip, signatures are generated from these descriptors, and a reference signature database is constructed from these signatures. Query signatures are also generated for a query multimedia clip. These query signatures are searched against the reference database using a fast similarity search procedure, to produce a candidate list of matching signatures. This candidate list is further analyzed to find the most likely reference matches. Signature correlation is performed between the likely reference matches and the query clip to improve detection accuracy.Type: GrantFiled: April 17, 2019Date of Patent: November 2, 2021Assignee: Roku, Inc.Inventors: Jose Pio Pereira, Sunil Suresh Kulkarni, Shashank Merchant, Prashant Ramanathan, Pradipkumar Dineshbhai Gajjar
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Patent number: 11120068Abstract: The overall architecture and details of a scalable video fingerprinting and identification system that is robust with respect to many classes of video distortions is described. In this system, a fingerprint for a piece of multimedia content is composed of a number of compact signatures, along with traversal hash signatures and associated metadata. Numerical descriptors are generated for features found in a multimedia clip, signatures are generated from these descriptors, and a reference signature database is constructed from these signatures. Query signatures are also generated for a query multimedia clip. These query signatures are searched against the reference database using a fast similarity search procedure, to produce a candidate list of matching signatures. This candidate list is further analyzed to find the most likely reference matches. Signature correlation is performed between the likely reference matches and the query clip to improve detection accuracy.Type: GrantFiled: April 17, 2019Date of Patent: September 14, 2021Assignee: Roku, Inc.Inventors: Jose Pio Pereira, Sunil Suresh Kulkarni, Shashank Merchant, Prashant Ramanathan, Pradipkumar Dineshbhai Gajjar
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Patent number: 10970328Abstract: Techniques are described that exclude use of “stop-fingerprints” from media database formation and search query to an automatic content recognition (ACR) systems based on media content fingerprints updated by stop-fingerprint analysis. A classification process is presented which takes in fingerprints from reference media files as an input and produces a modified set of fingerprints as an output by applying a novel stop-fingerprint classification algorithm. Architecture for the distributed stop-fingerprint generation is presented. Various cases, as stop-fingerprints generation for the entire reference database, stop-fingerprints generation for the individual reference fingerprint files, and temporal fingerprint classification obtained through intermediate steps of the temporal fingerprint classification algorithm are presented. A hash-based signature classification algorithm is also described.Type: GrantFiled: September 24, 2018Date of Patent: April 6, 2021Assignee: Gracenote, Inc.Inventors: Sunil Suresh Kulkarni, Pradipkumar Dineshbhai Gajjar, Jose Pio Pereira, Prashant Ramanathan, Mihailo M. Stojancic, Shashank Merchant
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Patent number: 10956484Abstract: Techniques are described that exclude use of “stop-fingerprints” from media database formation and search query to an automatic content recognition (ACR) systems based on media content fingerprints updated by stop-fingerprint analysis. A classification process is presented which takes in fingerprints from reference media files as an input and produces a modified set of fingerprints as an output by applying a novel stop-fingerprint classification algorithm. Architecture for the distributed stop-fingerprint generation is presented. Various cases, as stop-fingerprints generation for the entire reference database, stop-fingerprints generation for the individual reference fingerprint files, and temporal fingerprint classification obtained through intermediate steps of the temporal fingerprint classification algorithm are presented. A hash-based signature classification algorithm is also described.Type: GrantFiled: March 13, 2017Date of Patent: March 23, 2021Assignee: Gracenote, Inc.Inventors: Sunil Suresh Kulkarni, Pradipkumar Dineshbhai Gajjar, Jose Pio Pereira, Prashant Ramanathan, Mihailo M. Stojancic, Shashank Merchant
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Publication number: 20190251114Abstract: The overall architecture and details of a scalable video fingerprinting and identification system that is robust with respect to many classes of video distortions is described. In this system, a fingerprint for a piece of multimedia content is composed of a number of compact signatures, along with traversal hash signatures and associated metadata. Numerical descriptors are generated for features found in a multimedia clip, signatures are generated from these descriptors, and a reference signature database is constructed from these signatures. Query signatures are also generated for a query multimedia clip. These query signatures are searched against the reference database using a fast similarity search procedure, to produce a candidate list of matching signatures. This candidate list is further analyzed to find the most likely reference matches. Signature correlation is performed between the likely reference matches and the query clip to improve detection accuracy.Type: ApplicationFiled: April 17, 2019Publication date: August 15, 2019Inventors: Jose Pio Pereira, Sunil Suresh Kulkarni, Shashank Merchant, Prashant Ramanathan, Pradipkumar Dineshbhai Gajjar
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Publication number: 20190251113Abstract: The overall architecture and details of a scalable video fingerprinting and identification system that is robust with respect to many classes of video distortions is described. In this system, a fingerprint for a piece of multimedia content is composed of a number of compact signatures, along with traversal hash signatures and associated metadata. Numerical descriptors are generated for features found in a multimedia clip, signatures are generated from these descriptors, and a reference signature database is constructed from these signatures. Query signatures are also generated for a query multimedia clip. These query signatures are searched against the reference database using a fast similarity search procedure, to produce a candidate list of matching signatures. This candidate list is further analyzed to find the most likely reference matches. Signature correlation is performed between the likely reference matches and the query clip to improve detection accuracy.Type: ApplicationFiled: April 17, 2019Publication date: August 15, 2019Inventors: Jose Pio Pereira, Sunil Suresh Kulkarni, Shashank Merchant, Prashant Ramanathan, Pradipkumar Dineshbhai Gajjar
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Publication number: 20190243851Abstract: The overall architecture and details of a scalable video fingerprinting and identification system that is robust with respect to many classes of video distortions is described. In this system, a fingerprint for a piece of multimedia content is composed of a number of compact signatures, along with traversal hash signatures and associated metadata. Numerical descriptors are generated for features found in a multimedia clip, signatures are generated from these descriptors, and a reference signature database is constructed from these signatures. Query signatures are also generated for a query multimedia clip. These query signatures are searched against the reference database using a fast similarity search procedure, to produce a candidate list of matching signatures. This candidate list is further analyzed to find the most likely reference matches. Signature correlation is performed between the likely reference matches and the query clip to improve detection accuracy.Type: ApplicationFiled: April 17, 2019Publication date: August 8, 2019Inventors: Jose Pio Pereira, Sunil Suresh Kulkarni, Shashank Merchant, Prashant Ramanathan, Pradipkumar Dineshbhai Gajjar
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Patent number: 9785708Abstract: An architecture for a multimedia search system is described. To perform similarity matching of multimedia query frames against reference content, reference database comprising of a cluster index using cluster keys to perform similarity matching and a multimedia index to perform sequence matching is built. Methods to update and maintain the reference database that enables addition and removal of the multimedia contents, including portions of multimedia content, from the reference database in a running system are described. Hierarchical multi-level partitioning methods to organize the reference database are presented. Smart partitioning of the reference multimedia content according to the nature of the multimedia content, and according to the popularity among the social media, that supports scalable fast multimedia identification is also presented.Type: GrantFiled: May 21, 2015Date of Patent: October 10, 2017Assignee: GRACENOTE, INC.Inventors: Sunil Suresh Kulkarni, Jose Pio Pereira, Pradipkumar Dineshbhai Gajjar, Shashank Merchant, Prashant Ramanathan, Mihailo M. Stojancic
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Publication number: 20150254344Abstract: An architecture for a multimedia search system is described. To perform similarity matching of multimedia query frames against reference content, reference database comprising of a cluster index using cluster keys to perform similarity matching and a multimedia index to perform sequence matching is built. Methods to update and maintain the reference database that enables addition and removal of the multimedia contents, including portions of multimedia content, from the reference database in a running system are described. Hierarchical multi-level partitioning methods to organize the reference database are presented. Smart partitioning of the reference multimedia content according to the nature of the multimedia content, and according to the popularity among the social media, that supports scalable fast multimedia identification is also presented.Type: ApplicationFiled: May 21, 2015Publication date: September 10, 2015Applicant: ZEITERA, LLCInventors: Sunil Suresh Kulkarni, Jose Pio Pereira, Pradipkumar Dineshbhai Gajjar, Shashank Merchant, Prashant Ramanathan, Mihailo M. Stojancic
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Patent number: 9058355Abstract: An architecture for a multimedia search system is described. To perform similarity matching of multimedia query frames against reference content, reference database comprising of a cluster index using cluster keys to perform similarity matching and a multimedia index to perform sequence matching is built. Methods to update and maintain the reference database that enables addition and removal of the multimedia contents, including portions of multimedia content, from the reference database in a running system are described. Hierarchical multi-level partitioning methods to organize the reference database are presented. Smart partitioning of the reference multimedia content according to the nature of the multimedia content, and according to the popularity among the social media, that supports scalable fast multimedia identification is also presented.Type: GrantFiled: January 9, 2014Date of Patent: June 16, 2015Assignee: Zeitera, LLCInventors: Sunil Suresh Kulkarni, Jose Pio Pereira, Pradipkumar Dineshbhai Gajjar, Shashank Merchant, Prashant Ramanathan, Mihailo M. Stojancic
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Patent number: 8965863Abstract: An architecture for a multimedia search system is described. To perform similarity matching of multimedia query frames against reference content, reference database comprising of a cluster index using cluster keys to perform similarity matching and a multimedia index to perform sequence matching is built. Methods to update and maintain the reference database that enables addition and removal of the multimedia contents, including portions of multimedia content, from the reference database in a running system are described. Hierarchical multi-level partitioning methods to organize the reference database are presented. Smart partitioning of the reference multimedia content according to the nature of the multimedia content, and according to the popularity among the social media, that supports scalable fast multimedia identification is also presented.Type: GrantFiled: January 9, 2014Date of Patent: February 24, 2015Assignee: Zeitera, LLCInventors: Sunil Suresh Kulkarni, Jose Pio Pereira, Pradipkumar Dineshbhai Gajjar, Shashank Merchant, Prashant Ramanathan, Mihailo M. Stojancic
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Patent number: 8655878Abstract: An architecture for a multimedia search system is described. To perform similarity matching of multimedia query frames against reference content, reference database comprising of a cluster index using cluster keys to perform similarity matching and a multimedia index to perform sequence matching is built. Methods to update and maintain the reference database that enables addition and removal of the multimedia contents, including portions of multimedia content, from the reference database in a running system are described. Hierarchical multi-level partitioning methods to organize the reference database are presented. Smart partitioning of the reference multimedia content according to the nature of the multimedia content, and according to the popularity among the social media, that supports scalable fast multimedia identification is also presented.Type: GrantFiled: May 6, 2011Date of Patent: February 18, 2014Assignee: Zeitera, LLCInventors: Sunil Suresh Kulkarni, Jose Pio Pereira, Pradipkumar Dineshbhai Gajjar, Shashank Merchant, Prashant Ramanathan, Mihailo Stojancic
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Patent number: 8335786Abstract: A method is presented for large media data base query and media entry identification based on multi-level similarity search and reference-query entry correlation. Media content fingerprinting detects unique features and generates discriminative descriptors and signatures used to form preliminary reference data base. The preliminary reference data base is processed and a subset-set of it is selected to form a final reference data base. To identify a media query a fast similarity search is performed first on the reference database resulting in a preliminary set of likely matching videos. For each preliminary likely matching video a further multi-level correlation is performed which includes iterative refinement, sub-sequence merging, and final result classification.Type: GrantFiled: May 27, 2010Date of Patent: December 18, 2012Assignee: Zeitera, LLCInventors: Jose Pio Pereira, Sunil Suresh Kulkarni, Shashank Merchant, Prashant Ramanathan, Pradipkumar Dineshbhai Gajjar
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Publication number: 20100306193Abstract: A method is presented for large media data base query and media entry identification based on multi-level similarity search and reference-query entry correlation. Media content fingerprinting detects unique features and generates discriminative descriptors and signatures used to form preliminary reference data base. The preliminary reference data base is processed and a subset-set of it is selected to form a final reference data base. To identify a media query a fast similarity search is performed first on the reference database resulting in a preliminary set of likely matching videos. For each preliminary likely matching video a further multi-level correlation is performed which includes iterative refinement, sub-sequence merging, and final result classification.Type: ApplicationFiled: May 27, 2010Publication date: December 2, 2010Applicant: Zeitera, LLCInventors: Jose Pio Pereira, Sunil Suresh Kulkarni, Shashank Merchant, Prashant Ramanathan, Pradipkumar Dineshbhai Gajjar
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Patent number: RE48791Abstract: An architecture for a multimedia search system is described. To perform similarity matching of multimedia query frames against reference content, reference database comprising of a cluster index using cluster keys to perform similarity matching and a multimedia index to perform sequence matching is built. Methods to update and maintain the reference database that enables addition and removal of the multimedia contents, including portions of multimedia content, from the reference database in a running system are described. Hierarchical multi-level partitioning methods to organize the reference database are presented. Smart partitioning of the reference multimedia content according to the nature of the multimedia content, and according to the popularity among the social media, that supports scalable fast multimedia identification is also presented.Type: GrantFiled: October 9, 2019Date of Patent: October 26, 2021Assignee: Roku, Inc.Inventors: Sunil Suresh Kulkarni, Jose Pio Pereira, Pradipkumar Dineshbhai Gajjar, Shashank Merchant, Prashant Ramanathan, Mihailo M. Stojancic