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公开(公告)号：US20230094954A1
公开(公告)日：2023-03-30
申请号：US17485780
申请日：2021-09-27
申请人： Adobe Inc.
发明人： Ritwik Sinha , Sridhar Mahadevan , Moumita Sinha , Md Mehrab Tanjim , Krishna Kumar Singh , David Arbour
摘要： Methods and systems disclosed herein relate generally to systems and methods for generating simulated images for enhancing socio-demographic diversity. An image-generating application receives a request that includes a set of target socio-demographic attributes. The set of target socio-demographic attributes can define a gender, age, and/or race of a subject that are non-stereotypical for a particular occupation. The image-generating application applies the a machine-learning model to the set of target socio-demographic attributes. The machine-learning model generates a simulated image depicts a subject having visual characteristics that are defined by the set of target socio-demographic attributes.
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2.发明申请公开(公告)号：US20220283932A1
公开(公告)日：2022-09-08
申请号：US17192320
申请日：2021-03-04
申请人： Adobe Inc.
发明人： David Arbour , Ritwik Sinha , Ian Waudby-Smith , Aaditya Ramdas
IPC分类号： G06F11/36 , G06F16/958 , G06F8/77
摘要： A computer-implemented method includes instantiating a framework configured to optimize a metric of interest for a website based on interactions by participants with instances of a website in a controlled experiment. The instances of the website include one of two variants of digital content. Test data including an estimate of an effect on the metric of interest is generated based on the interactions. A sequence of confidence intervals is dynamically generated while the controlled experiment is ongoing. The true effect and the estimate effect on the metric of interest are both bounded by the sequence of confidence intervals throughout the controlled experiment. As such, an anytime analysis with anytime-valid test data is enabled while the controlled experiment is ongoing.
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公开(公告)号：US20240281836A1
公开(公告)日：2024-08-22
申请号：US18110620
申请日：2023-02-16
申请人： Adobe Inc.
发明人： Ritwik Sinha , Ziao Liu , Robert Sebastian Mares , Oana Catalina Persoiu-Focsa , Moumita Sinha , Ivan Andrus , David Arbour , Akash Maharaj , Prithvi Bhutani
IPC分类号： G06Q30/0203 , G06F17/18
CPC分类号： G06Q30/0203 , G06F17/18
摘要： Certain aspects and features of this disclosure relate to providing anytime-valid confidence sequences for multiple messaging treatments in an experiment. A process controls and/or corrects statistical error when multiple messaging treatments are being evaluated together. Messages can be stored, formatted, and transmitted from a communication server or other computing system. In one example, each test message from among multiple test messages is sent to an independent group of recipients over some period of time. An analytics application programmatically evaluates a metric related to message responses over time and determines a difference in the metric for each of several unique messages as compared to a baseline message. The analytics application also determines a confidence value and can display the changing confidence value in sequence over time along with the current difference, or lift, while maintaining the accuracy of the values.
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公开(公告)号：US11170048B2
公开(公告)日：2021-11-09
申请号：US16451956
申请日：2019-06-25
申请人： Adobe Inc.
发明人： Ryan Rossi , Aldo Gael Carranza , David Arbour , Anup Rao , Sungchul Kim , Eunyee Koh
IPC分类号： G06F16/00 , G06F16/901 , G06F17/18
摘要： A system is disclosed for identifying and counting typed graphlets in a heterogeneous network. A methodology implementing techniques for the disclosed system according to an embodiment includes identifying typed k-node graphlets occurring between any two selected nodes of a heterogeneous network, wherein the nodes are connected by one or more edges. The identification is based on combinatorial relationships between (k−1)-node typed graphlets occurring between the two selected nodes of the heterogeneous network. Identification of 3-node typed graphlets is based on computation of typed triangles, typed 3-node stars, and typed 3-paths associated with each edge connecting the selected nodes. The method further includes maintaining a count of the identified k-node typed graphlets and storing those graphlets with non-zero counts. The identified graphlets are employed for applications including visitor stitching, user profiling, outlier detection, and link prediction.
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公开(公告)号：US12001520B2
公开(公告)日：2024-06-04
申请号：US17485780
申请日：2021-09-27
申请人： Adobe Inc.
发明人： Ritwik Sinha , Sridhar Mahadevan , Moumita Sinha , Md Mehrab Tanjim , Krishna Kumar Singh , David Arbour
IPC分类号： G06K9/00 , G06F18/214 , G06F18/28 , G06N3/045
CPC分类号： G06F18/28 , G06F18/2148 , G06N3/045
摘要： Methods and systems disclosed herein relate generally to systems and methods for generating simulated images for enhancing socio-demographic diversity. An image-generating application receives a request that includes a set of target socio-demographic attributes. The set of target socio-demographic attributes can define a gender, age, and/or race of a subject that are non-stereotypical for a particular occupation. The image-generating application applies the a machine-learning model to the set of target socio-demographic attributes. The machine-learning model generates a simulated image depicts a subject having visual characteristics that are defined by the set of target socio-demographic attributes.
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公开(公告)号：US20230394332A1
公开(公告)日：2023-12-07
申请号：US17804991
申请日：2022-06-01
申请人： Adobe Inc.
CPC分类号： G06N5/04 , G06F11/3409 , G06F11/3006 , G06F11/3476
摘要： The present disclosure describes methods, systems, and non-transitory computer-readable media for generating a projected value metric that projects a performance of a target policy within a digital action space. For instance, in one or more embodiments, the disclosed systems identify a target policy for performing digital actions represented within a digital action space. The disclosed systems further determine a set of sampled digital actions performed according to a logging policy and represented within the digital action space. Utilizing an embedding model, the disclosed systems generate a set of action embedding vectors representing the set of sampled digital actions within an embedding space. Further, utilizing the set of action embedding vectors, the disclosed systems generate a projected value metric indicating a projected performance of the target policy.
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公开(公告)号：US20220261683A1
公开(公告)日：2022-08-18
申请号：US17174944
申请日：2021-02-12
申请人： ADOBE INC.
发明人： Tong Mu , Georgios Theocharous , David Arbour
摘要： Systems and methods for sequential recommendation are described. Embodiments receive a user interaction history including interactions of a user with a plurality of items, select a constraint from a plurality of candidate constraints based on lifetime values observed for the candidate constraints, wherein the lifetime values are based on items predicted for other users using a recommendation network subject to the candidate constraints, and predict a next item for the user based on the user interaction history using the recommendation network subject to the selected constraint.
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公开(公告)号：US20240153598A1
公开(公告)日：2024-05-09
申请号：US18051736
申请日：2022-11-01
申请人： ADOBE INC.
IPC分类号： G16H10/20
CPC分类号： G16H10/20
摘要： Systems and methods for content customization are described. According to one aspect, a content customization apparatus is provided. The apparatus includes a processor; a memory storing instructions executable by the processor; a user feature component configured to generate user feature vectors representing user features for a plurality of users, respectively; a group selection component configured to select a treatment group and a control group based on the user feature vectors; a machine learning model configured to train a treatment effect estimator based on the user feature vectors and outcome data for the treatment group and the control group; and a content component configured to provide customized content based on the treatment effect estimator.
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公开(公告)号：US11790379B2
公开(公告)日：2023-10-17
申请号：US17004377
申请日：2020-08-27
申请人： ADOBE INC.
发明人： Shiv Kumar Saini , Ritwik Sinha , Moumita Sinha , David Arbour
IPC分类号： G06Q30/0201 , G06Q30/0204 , H04N21/81 , G06Q30/0242 , H04L67/50 , G06N7/01
CPC分类号： G06Q30/0201 , G06N7/01 , G06Q30/0205 , G06Q30/0242 , H04L67/535 , H04N21/812
摘要： A method, apparatus, and non-transitory computer readable medium for data analytics are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include monitoring online activity corresponding to a plurality of users; receiving aggregate marketing data for a marketing activity; identifying online activity data for a time period corresponding to the marketing activity based on the monitoring; generating a regression model based on the aggregate marketing data and the online activity data using Bayesian regression, wherein the regression model represents a relationship between the marketing activity and the online activity, comprises a time effect coefficient, and is based on a prior distribution of the time effect coefficient that decays to zero as time increases; and estimating a treatment effect for the marketing activity on the online activity based on the regression model, wherein the treatment effect comprises a rate of effect decay.
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公开(公告)号：US20230139824A1
公开(公告)日：2023-05-04
申请号：US17519311
申请日：2021-11-04
申请人： ADOBE INC.
发明人： Trisha Mittal , Viswanathan Swaminathan , Ritwik Sinha , Saayan Mitra , David Arbour , Somdeb Sarkhel
摘要： Various disclosed embodiments are directed to using one or more algorithms or models to select a suitable or optimal variation, among multiple variations, of a given content item based on feedback. Such feedback guides the algorithm or model to arrive at suitable variation result such that the variation result is produced as the output for consumption by users. Further, various embodiments resolve tedious manual user input requirements and reduce computing resource consumption, among other things, as described in more detail below.
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