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公开(公告)号：US20230164403A1
公开(公告)日：2023-05-25
申请号：US17535113
申请日：2021-11-24
发明人： Alexander Niedt , Mara Idai Lucien , Juli Logemann , Miquel Angel Farre Guiu , Monica Alfaro Vendrell , Marc Junyent Martin
IPC分类号： H04N21/81 , H04N21/475 , H04N21/466 , G06F16/74 , H04N21/45 , H04N21/84 , H04N21/431
CPC分类号： H04N21/8153 , H04N21/4755 , H04N21/4667 , G06F16/743 , H04N21/4532 , H04N21/84 , H04N21/4312
摘要： A system includes a computing platform including processing hardware and a memory storing software code, a trained machine learning (ML) model, and a content thumbnail generator. The processing hardware executes the software code to receive interaction data describing interactions by a user with content thumbnails, identify, using the interaction data, an affinity by the user for at least one content thumbnail feature, and determine, using the interaction data, a predetermined business rule, or both, content for promotion to the user. The software code further provides a prediction, using the trained ML model and based on the affinity by the user, of the desirability of each of multiple candidate thumbnails for the content to the user, generates, using the content thumbnail generator and based on the prediction, a thumbnail having features of one or more of the candidate thumbnails, and displays the thumbnail to promote the content to the user.
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公开(公告)号：US20230068502A1
公开(公告)日：2023-03-02
申请号：US17460910
申请日：2021-08-30
发明人： Pablo Pernias , Monica Alfaro Vendrell , Francesc Josep Guitart Bravo , Marc Junyent Martin , Miquel Angel Farre Guiu
摘要： A system includes a computing platform having processing hardware, and a memory storing software code and a machine learning (ML) model-based feature classifier. When executed, the software code receives media content including a first media component corresponding to a first media mode and a second media component corresponding to a second media mode, encodes the first media component using a first encoder to generate multiple first embedding vectors, and encodes the second media component using a second encoder to generate multiple second embedding vectors. The software code further combines the first embedding vectors and the second embedding vectors to provide an input data structure for a neural network mixer, process, using the neural network mixer, the input data structure to provide feature data corresponding to a feature of the media content, and predict, using the ML model-based feature classifier and the feature data, a classification of the feature.
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公开(公告)号：US11314706B2
公开(公告)日：2022-04-26
申请号：US16999767
申请日：2020-08-21
发明人： Christopher C. Stoafer , Jordi Badia Pujol , Francesc Josep Guitart Bravo , Marc Junyent Martin , Miquel Angel Farre Guiu , Calvin Lawson , Erick L. Luerken
IPC分类号： G06F16/00 , G06F16/215 , G06F16/2453 , G06F9/48 , G06F11/34 , G06F16/21 , G06F16/178 , G06F16/2458 , G06F16/383 , G06F17/11 , G06F30/27 , G06F16/33
摘要： A metadata aggregation system includes a computing platform having a hardware processor and a memory storing a software code including a trained entity matching predictive model trained using training data obtained from a reference database. The hardware processor executes the software code to obtain metadata inputs from multiple sources, conform the metadata inputs to a common format, match, using the trained entity matching predictive model, at least some of the conformed metadata inputs to the same entity, and determine, using the trained entity matching predictive model, a confidence score for each match. The software code further sends a request to one or more human editor(s) for confirmation of each match having a confidence score greater than a first threshold and less than a second threshold, and updates the reference database, in response to receiving a confirmation that at least one match is a confirmed match, to include the confirmed match.
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公开(公告)号：US11157777B2
公开(公告)日：2021-10-26
申请号：US16512223
申请日：2019-07-15
发明人： Miquel Angel Farre Guiu , Matthew C. Petrillo , Marc Junyent Martin , Anthony M. Accardo , Avner Swerdlow , Monica Alfaro Vendrell
摘要： According to one implementation, a quality control (QC) system for annotated content includes a computing platform having a hardware processor and a system memory storing an annotation culling software code. The hardware processor executes the annotation culling software code to receive multiple content sets annotated by an automated content classification engine, and obtain evaluations of the annotations applied by the automated content classification engine to the content sets. The hardware processor further executes the annotation culling software code to identify a sample size of the content sets for automated QC analysis of the annotations applied by the automated content classification engine, and cull the annotations applied by the automated content classification engine based on the evaluations when the number of annotated content sets equals the identified sample size.
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公开(公告)号：US20210117678A1
公开(公告)日：2021-04-22
申请号：US16655117
申请日：2019-10-16
发明人： Miquel Angel Farre Guiu , Matthew C. Petrillo , Monica Alfaro Vendrell , Daniel Fojo , Albert Aparicio , Francese Josep Guitart Bravo , Jordi Badia Pujol , Marc Junyent Martin , Anthony M. Accardo
摘要： According to one implementation, a system for automating inferential content annotation includes a computing platform having a hardware processor and a system memory storing a software code including a set of rules trained to annotate content inferentially. The hardware processor executes the software code to utilize one or more feature analyzer(s) to apply labels to features detected in the content, access one or more knowledge base(s) to validate at least one of the applied labels, and to obtain, from the knowledge base(s), descriptive data linked to the validated label(s). The software code then infers, using the set of rules, one or more label(s) for the content based on the validated label(s) and the descriptive data, and outputs tags for annotating the content, where the tags include the validated label(s) and the inferred label(s).
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公开(公告)号：US20200151459A1
公开(公告)日：2020-05-14
申请号：US16352601
申请日：2019-03-13
发明人： Miquel Angel Farre Guiu , Matthew Petrillo , Monica Alfaro Vendrell , Marc Junyent Martin , Daniel Fojo , Anthony M. Accardo , Avner Swerdlow , Katharine Navarre
摘要： According to one implementation, a system for automating content annotation includes a computing platform having a hardware processor and a system memory storing an automation training software code. The hardware processor executes the automation training software code to initially train a content annotation engine using labeled content, test the content annotation engine using a first test set of content obtained from a training database, and receive corrections to a first automatically annotated content set resulting from the test. The hardware processor further executes the automation training software code to further train the content annotation engine based on the corrections, determine one or more prioritization criteria for selecting a second test set of content for testing the content annotation engine based on the statistics relating to the first automatically annotated content, and select the second test set of content from the training database based on the prioritization criteria.
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公开(公告)号：US11354894B2
公开(公告)日：2022-06-07
申请号：US16655117
申请日：2019-10-16
发明人： Miquel Angel Farre Guiu , Matthew C. Petrillo , Monica Alfaro Vendrell , Daniel Fojo , Albert Aparicio Isarn , Francesc Josep Guitart Bravo , Jordi Badia Pujol , Marc Junyent Martin , Anthony M. Accardo
摘要： According to one implementation, a system for automating inferential content annotation includes a computing platform having a hardware processor and a system memory storing a software code including a set of rules trained to annotate content inferentially. The hardware processor executes the software code to utilize one or more feature analyzer(s) to apply labels to features detected in the content, access one or more knowledge base(s) to validate at least one of the applied labels, and to obtain, from the knowledge base(s), descriptive data linked to the validated label(s). The software code then infers, using the set of rules, one or more label(s) for the content based on the validated label(s) and the descriptive data, and outputs tags for annotating the content, where the tags include the validated label(s) and the inferred label(s).
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公开(公告)号：US20220058196A1
公开(公告)日：2022-02-24
申请号：US16999767
申请日：2020-08-21
发明人： Christopher C. Stoafer , Jordi Badia Pujol , Francesc Josep Guitart Bravo , Marc Junyent Martin , Miquel Angel Farre Guiu , Calvin Lawson , Erick L. Luerken
IPC分类号： G06F16/2453 , G06F9/48 , G06F16/21 , G06F11/34
摘要： A metadata aggregation system includes a computing platform having a hardware processor and a memory storing a software code including a trained entity matching predictive model trained using training data obtained from a reference database. The hardware processor executes the software code to obtain metadata inputs from multiple sources, conform the metadata inputs to a common format, match, using the trained entity matching predictive model, at least some of the conformed metadata inputs to the same entity, and determine, using the trained entity matching predictive model, a confidence score for each match. The software code further sends a request to one or more human editor(s) for confirmation of each match having a confidence score greater than a first threshold and less than a second threshold, and updates the reference database, in response to receiving a confirmation that at least one match is a confirmed match, to include the confirmed match.
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公开(公告)号：US11010398B2
公开(公告)日：2021-05-18
申请号：US15985433
申请日：2018-05-21
发明人： Miquel Angel Farre Guiu , Marc Junyent Martin , Jordi Pont-Tuset , Pablo Beltran , Nimesh Narayan , Leonid Sigal , Aljoscha Smolic , Anthony M. Accardo
IPC分类号： G06F16/25 , G06F3/0484 , G06F3/0481 , G06F16/955 , G06F16/23 , G06F16/78 , G06F16/783 , G06F40/166
摘要： There is provided a system including a computing platform having a hardware processor and a memory, and a metadata extraction and management unit stored in the memory. The hardware processor is configured to execute the metadata extraction and management unit to extract a plurality of metadata types from a media asset sequentially and in accordance with a prioritized order of extraction based on metadata type, aggregate the plurality of metadata types to produce an aggregated metadata describing the media asset, use the aggregated metadata to include at least one database entry in a graphical database, wherein the at least one database entry describes the media asset, display a user interface for a user to view tags of metadata associated with the media asset, and correcting presence of one of the tags of metadata associated with the media asset, in response to an input from the user via the user interface.
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公开(公告)号：US10924823B1
公开(公告)日：2021-02-16
申请号：US16551467
申请日：2019-08-26
发明人： Evan A. Binder , Marc Junyent Martin , Jordi Badia Pujol , Avner Swerdlow , Miquel Angel Farre Guiu
IPC分类号： H04N21/8545 , H04L29/08 , G06T15/00 , H04N21/858 , H04L29/06 , H04N21/845
摘要： According to one implementation, a cloud-based system for performing cloud-based image rendering for video stream enrichment includes a video forwarding unit and a video enrichment unit. The video forwarding unit is configured to detect one or more non-interactive video player(s) linked to the video forwarding unit over a communication network, forward a video stream to the non-interactive video player(s), and forward the video stream to the video enrichment unit. The video enrichment unit is configured to receive the video stream, detect one or more interactive video player(s) linked to the video enrichment unit over the communication network, identify a video enhancement corresponding to one or more customizable video segment(s) in the video stream, insert a rendered video enhancement into the one or more customizable video segment(s) to produce an enriched video stream, and distribute the enriched video stream to one or more of the interactive video player(s).
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