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公开(公告)号：US20170017649A1
公开(公告)日：2017-01-19
申请号：US14799206
申请日：2015-07-14
Applicant: Verizon Patent and Licensing Inc.
Inventor： Haripriya Srinivasaraghavan
CPC classification number: G06F17/30029 , G06N3/0472 , G06N7/005 , G06Q30/00
Abstract: Techniques described herein may be used to improve recommendations that are provided to a user regarding content (e.g., images, music, and videos). A content recommendations server may provide a user with recommended content and the reasons for which the content is being recommended, such as genres, directors, and actors that the content recommendations server believes the user enjoys. The user may provide feedback to the content recommendations server regarding the recommendations themselves and also regarding the reasons for which the content was recommended. The content recommendations server may use the feedback to improve subsequent recommendations to the user.
Abstract translation: 本文描述的技术可以用于改进向用户提供关于内容（例如，图像，音乐和视频）的建议。 内容推荐服务器可以向用户提供建议的内容以及推荐内容的原因，例如内容推荐服务器认为用户所喜欢的流派，导演和演员。 用户可以向内容推荐服务器提供关于建议本身的反馈，并且还关于推荐内容的原因。 内容推荐服务器可以使用反馈来改进对用户的后续建议。
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公开(公告)号：US11659247B2
公开(公告)日：2023-05-23
申请号：US17805889
申请日：2022-06-08
Applicant: Verizon Patent and Licensing Inc.
Inventor： Haripriya Srinivasaraghavan , Rajeshwar Makam , Frolov Volodymyr , Pratik Sarkar , Ankit Naidu , Yevhen Rutskyi
IPC: H04N21/25 , H04N21/466 , H04N21/84
CPC classification number: H04N21/4668 , H04N21/251 , H04N21/4662 , H04N21/84
Abstract: A device may receive content data, a first model, and a second model. The first model may be trained on different types of metadata than the second model. The content data may include a first identifier of a first content item and a first set of metadata associated with the first content item. The device may process the first set of metadata to generate first recommendations from the first model and second recommendations from the second model. The device may provide the first identifier and a combination of the first recommendations and the second recommendations to client devices. The device may receive, from the client devices, user-generated target recommendations based on the combination. The device may process the user-generated target recommendations, the first recommendations, and the second recommendations, to provide feedback to update the first model and the second model.
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公开(公告)号：US11615158B2
公开(公告)日：2023-03-28
申请号：US17406199
申请日：2021-08-19
Applicant: VERIZON PATENT AND LICENSING INC.
Inventor： Haripriya Srinivasaraghavan
IPC: G06F16/9535 , G06F3/0484 , G06N20/00 , G06F16/9532 , G06F3/0482
Abstract: Disclosed are systems and methods for an electronic framework that enables un-biasing of personalizations for users. The disclosed framework provides controls that can enable users to selectively escape from previously conceived notions of a user's preferred tastes and/or interests. Upon a user requesting content, the disclosed framework can analyze the type of request as well as the modeled behavior and preferences of the user, and automatically un-bias or depersonalize content for the user, thereby availing the user to a broader range of content from a larger pool of content then previously made available to the user.
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公开(公告)号：US11321610B2
公开(公告)日：2022-05-03
申请号：US16108242
申请日：2018-08-22
Applicant: Verizon Patent and Licensing Inc.
Inventor： Haripriya Srinivasaraghavan
Abstract: A method, a device, and a non-transitory storage medium are described in which a rehearsal network service is provided that enables generalized learning for all types of input patterns ranging from one-shot inputs to a large set of inputs. The rehearsal network service includes using biological memory indicator data relating to a user and the input data. The rehearsal network service includes calculating a normalized effective salience for each input data, and generating a new set of input data in which the inclusion of input data is proportional to its normalization effective salience. The rehearsal network service further includes augmenting the new set of input data using perturbation values. The rehearsal network service provides the new set of input data to a learning network, such as a neural network or a deep learning network that can learn the user's taste or preference.
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35.发明授权公开(公告)号：US11243957B2
公开(公告)日：2022-02-08
申请号：US16031703
申请日：2018-07-10
Applicant: Verizon Patent and Licensing Inc.
Inventor： Haripriya Srinivasaraghavan
IPC: G06F16/2457 , G06Q30/06 , G06F16/9535
Abstract: A computing device may include a memory configured to store instructions and a processor configured to execute the instructions to receive a selection of a content catalog item or a search query from a user; generate an input vector based on the selected content catalog item or the search query; and map the generated input vector onto one or more points on a self-organizing map associated with the user. The processor may be further configured to select a set of points within a particular distance of the one or more points; reverse map the selected set of points to a plurality of content catalog items using the self-organizing map associated with the user; and present one or more of the plurality of content catalog items to the user as recommended content catalog items for the user.
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Patent Agency Ranking
公开(公告)号：US10747803B2
公开(公告)日：2020-08-18
申请号：US16052255
申请日：2018-08-01
Applicant: Verizon Patent and Licensing Inc.
IPC: G06F16/438 , G06F16/435 , G06F16/28 , G06Q30/06 , G06F3/0482
Abstract: A method, a device, and a non-transitory storage medium are described in which a personalized content recommendation system determines a content-offering value (COV) for each title of content identified in a content catalog, wherein the COVs indicate terms of offerings to a user for consuming each title of content; calculate, for each title of content, a content-relevance value (CRV), wherein the CRV indicates respective relevancies of each title of content to the user; calculate, for each title of content, a cost-content sensitivity index (CCSI) value indicative of the user's relative cost and content sensitivities, wherein the CCSI value is calculated for a time-of-day parameter or a content-genre parameter for consuming each title of content; calculate, for each title of content, a cost-content tradeoff score (CCTS) based on the COV, CRV, and CCSI value; and identify k number of titles of content having the highest CCTS.
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公开(公告)号：US20200065662A1
公开(公告)日：2020-02-27
申请号：US16108242
申请日：2018-08-22
Applicant: Verizon Patent and Licensing Inc.
Inventor： Haripriya Srinivasaraghavan
Abstract: A method, a device, and a non-transitory storage medium are described in which a rehearsal network service is provided that enables generalized learning for all types of input patterns ranging from one-shot inputs to a large set of inputs. The rehearsal network service includes using biological memory indicator data relating to a user and the input data. The rehearsal network service includes calculating a normalized effective salience for each input data, and generating a new set of input data in which the inclusion of input data is proportional to its normalization effective salience. The rehearsal network service further includes augmenting the new set of input data using perturbation values. The rehearsal network service provides the new set of input data to a learning network, such as a neural network or a deep learning network that can learn the user's taste or preference.
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公开(公告)号：US20200042605A1
公开(公告)日：2020-02-06
申请号：US16052255
申请日：2018-08-01
Applicant: Verizon Patent and Licensing Inc.
IPC: G06F17/30 , G06Q30/06 , G06F3/0482
Abstract: A method, a device, and a non-transitory storage medium are described in which a personalized content recommendation system determines a content-offering value (COV) for each title of content identified in a content catalog, wherein the COVs indicate terms of offerings to a user for consuming each title of content; calculate, for each title of content, a content-relevance value (CRV), wherein the CRV indicates respective relevancies of each title of content to the user; calculate, for each title of content, a cost-content sensitivity index (CCSI) value indicative of the user's relative cost and content sensitivities, wherein the CCSI value is calculated for a time-of-day parameter or a content-genre parameter for consuming each title of content; calculate, for each title of content, a cost-content tradeoff score (CCTS) based on the COV, CRV, and CCSI value; and identify k number of titles of content having the highest CCTS.
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39.发明申请公开(公告)号：US20200019636A1
公开(公告)日：2020-01-16
申请号：US16031703
申请日：2018-07-10
Applicant: Verizon Patent and Licensing Inc.
Inventor： Haripriya Srinivasaraghavan
Abstract: A computing device may include a memory configured to store instructions and a processor configured to execute the instructions to receive a selection of a content catalog item or a search query from a user; generate an input vector based on the selected content catalog item or the search query; and map the generated input vector onto one or more points on a self-organizing map associated with the user. The processor may be further configured to select a set of points within a particular distance of the one or more points; reverse map the selected set of points to a plurality of content catalog items using the self-organizing map associated with the user; and present one or more of the plurality of content catalog items to the user as recommended content catalog items for the user.
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40.发明申请公开(公告)号：US20170185598A1
公开(公告)日：2017-06-29
申请号：US14980881
申请日：2015-12-28
Applicant: Verizon Patent and Licensing Inc.
Inventor： Haripriya Srinivasaraghavan
IPC: G06F17/30
CPC classification number: G06F16/24578 , G06F16/40
Abstract: Systems and methods for scoring and using popularity of entities in a media-content-based social network to provide a media service are disclosed herein. An exemplary system assigns popularity scores to a plurality of entities included in a media-content-based social network, the popularity scores including a first popularity score assigned to a first entity included in the plurality of entities and a second popularity score assigned to a second entity included in the plurality of entities, detects an operation in the media-content-based social network, adjusts, in response to the detection of the operation in the media-content-based social network, the second popularity score of the second entity by an amount proportional to the first popularity score of the first entity at a time of the operation, and customizes a media service based at least in part on the popularity scores of the plurality of entities included in the media-content-based social network.
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