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公开(公告)号：US20230281642A1
公开(公告)日：2023-09-07
申请号：US17653157
申请日：2022-03-02
申请人： ADOBE INC.
发明人： Shiv Shankar , Sridhar Mahadevan , Moumita Sinha , Ritwik Sinha , Saayan Mitra , Viswanathan Swaminathan , Erin Davis
IPC分类号： G06Q30/02
CPC分类号： G06Q30/0201
摘要： A system and method for content distribution without tracking is described. The system and method includes determining that device identifiers are not available for a first digital content channel; identifying a first cluster of users and a second cluster of users based on the determination that device identifiers are not available; providing first content and second content via the first digital content channel; monitoring user interactions on the first digital content channel to obtain a first conversion rate for users in the first cluster that receive the first content and a second conversion rate for users in the second cluster that receive the second content; computing a cross-cluster treatment effect based on the first conversion rate and the second conversion rate; computing a treatment effect for the first content based on the cross-cluster treatment effect; and providing the first content to a subsequent user based on the treatment effect.
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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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3.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：US11200592B2
公开(公告)日：2021-12-14
申请号：US16514576
申请日：2019-07-17
申请人： Adobe Inc.
摘要： This disclosure involves allocating content-delivery resources to electronic content-delivery channels based on attribution models accuracy. For instance, a simulation is executed that involves simulating user exposures, times between user exposures, and user responses. The simulation is performed based on parameters associated with simulating user exposures to electronic content-delivery channels and user responses to the user exposures. An accuracy of a channel attribution model when estimating an attribution of an electronic content-delivery channel to a user response is evaluated based on the simulation. A channel attribution model is selected based on the evaluation. An attribution of the electronic content-delivery channel is determined by applying the selected channel attribution model to actual user exposures and actual user responses. This attribution can be used to allocate content-delivery resources to the electronic content-delivery channel in accordance with the selected channel attribution model, and thereby provide interactive content via the electronic content-delivery channel.
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公开(公告)号：US20210342649A1
公开(公告)日：2021-11-04
申请号：US16866261
申请日：2020-05-04
申请人： Adobe Inc.
发明人： Amit Doda , Gaurav Sinha , Kai Yeung Lau , Akangsha Sunil Bedmutha , Shiv Kumar Saini , Ritwik Sinha , Vaidyanathan Venkatraman , Niranjan Shivanand Kumbi , Omar Rahman , Atanu R. Sinha
IPC分类号： G06K9/62 , G06F17/18 , G06N20/00 , G06F3/0481
摘要： In implementations of systems for predicting a terminal event, a computing device implements a termination system to receive input data defining a period of time and a maximum event threshold. This system uses a classification model to generate event scores for a plurality of entity devices. Each of the event scores indicates a probability of an event occurrence for a corresponding entity device within a period of time. The plurality of entity devices are segmented into a first segment and a second segment based on an event score threshold. Entity devices included in the first segment have event scores greater than the event score threshold and entity devices included in the second segment have event scores below the event score threshold. The termination system generates an indication of a probability that a number of event occurrences for the entity devices included in the second segment exceeds the maximum even threshold within the period of time.
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公开(公告)号：US10785318B2
公开(公告)日：2020-09-22
申请号：US15793001
申请日：2017-10-25
申请人： Adobe Inc.
发明人： Sunny Dhamnani , Vishwa Vinay , Lilly Kumari , Ritwik Sinha
IPC分类号： G06F15/173 , H04L29/08 , H04L29/06 , G06K9/62
摘要： A session identification system classifies network sessions with a network application as either human-generated or generated by a non-human, such as by a bot. In an embodiment, the session identification system receives a set of unlabeled network sessions, and determines a label for a single class of the unlabeled network sessions. Based on the one-class labeling information, the session identification system determines multiple subsets of the unlabeled network sessions. Multiple classifiers included in the session identification system generate probabilities describing each of the unlabeled network sessions. The session identification system classifies each of the unlabeled network sessions based on a combination of the generated probabilities.
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公开(公告)号：US20200293836A1
公开(公告)日：2020-09-17
申请号：US16353076
申请日：2019-03-14
申请人： Adobe Inc.
发明人： Sunny Dhamnani , Dhruv Singal , Ritwik Sinha
IPC分类号： G06K9/62 , G06N5/02 , G06N20/00 , G06F16/901
摘要： An IDS generator determines multiple classes for electronic data items. The IDS generator determines, for each class, a class-specific candidate ruleset. The IDS generator performs a differential analysis of each class-specific candidate ruleset. The differential analysis is based on differences between result values of a scoring objective function. In some cases, the differential analysis determines at least one of the differences based on additional data structures, such as an augmented frequent-pattern tree. A probability function based on the differences is compared to a threshold probability At least one testing ruleset is modified based on the comparison. The IDS generator determines, for each class, a class-specific optimized ruleset based on the differential analysis of each class-specific candidate ruleset. The IDS generator creates an optimized interpretable decision set based on combined class-specific optimized rulesets for the multiple classes.
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公开(公告)号：US20190340641A1
公开(公告)日：2019-11-07
申请号：US16514576
申请日：2019-07-17
申请人： Adobe Inc.
摘要： This disclosure involves allocating content-delivery resources to electronic content-delivery channels based on attribution models accuracy. For instance, a simulation is executed that involves simulating user exposures, times between user exposures, and user responses. The simulation is performed based on parameters associated with simulating user exposures to electronic content-delivery channels and user responses to the user exposures. An accuracy of a channel attribution model when estimating an attribution of an electronic content-delivery channel to a user response is evaluated based on the simulation. A channel attribution model is selected based on the evaluation. An attribution of the electronic content-delivery channel is determined by applying the selected channel attribution model to actual user exposures and actual user responses. This attribution can be used to allocate content-delivery resources to the electronic content-delivery channel in accordance with the selected channel attribution model, and thereby provide interactive content via the electronic content-delivery channel.
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公开(公告)号：US10346861B2
公开(公告)日：2019-07-09
申请号：US14933254
申请日：2015-11-05
申请人： ADOBE INC.
发明人： Wei Zhang , Said Kobeissi , Anandhavelu Natarajan , Shiv Kumar Saini , Ritwik Sinha , Scott Allen Tomko
摘要： Embodiments of the present invention relate to providing business customers with predictive capabilities, such as identifying valuable customers or estimating the likelihood that a product will be purchased. An adaptive sampling scheme is utilized, which helps generate sample data points from large scale data that is imbalanced (for example, digital website traffic with hundreds of millions of visitors but only a small portion of them are of interest). In embodiments, a stream of sample data points is received. Positive samples are added to a positive list until the desired number of positives is reached and negative samples are added to a negative list until the desired number of negative samples is reached. The positive list and the negative list can then be combined, shuffled, and fed into a prediction model.
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公开(公告)号：US20240296519A1
公开(公告)日：2024-09-05
申请号：US18178018
申请日：2023-03-03
申请人： ADOBE INC.
发明人： Pooja Guhan , Saayan Mitra , Somdeb Sarkhel , Ritwik Sinha , Stefano Petrangeli , Viswanathan Swaminathan
摘要： Systems and methods for media generation are provided. According to one aspect, a method for media generation includes obtaining a media object and context data describing a context of the media object, wherein the media object comprises one or more modification parameters; generating a modified media object by adjusting the one or more modification parameters using a reinforcement learning model based on the context data; and providing the modified media object within the context.
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