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公开(公告)号：US11899693B2
公开(公告)日：2024-02-13
申请号：US17677323
申请日：2022-02-22
Applicant: Adobe Inc.
Inventor： Yeuk-yin Chan , Tung Mai , Ryan Rossi , Moumita Sinha , Matvey Kapilevich , Margarita Savova , Fan Du , Charles Menguy , Anup Rao
CPC classification number: G06F16/285
Abstract: A cluster generation system identifies data elements, from a first binary record, that each have a particular value and correspond to respective binary traits. A candidate description function describing the binary traits is generated, the candidate description function including a model factor that describes the data elements. Responsive to determining that a second record has additional data elements having the particular value and corresponding to the respective binary traits, the candidate description function is modified to indicate that the model factor describes the additional elements. The candidate description function is also modified to include a correction factor describing an additional binary trait excluded from the respective binary traits. Based on the modified candidate description function, the cluster generation system generates a data summary cluster, which includes a compact representation of the binary traits of the data elements and additional data elements.
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公开(公告)号：US20230267764A1
公开(公告)日：2023-08-24
申请号：US17652026
申请日：2022-02-22
Applicant: ADOBE INC.
Inventor： Md Mehrab Tanjim , Ritwik Sinha , Moumita Sinha , David Thomas Arbour , Sridhar Mahadevan
IPC: G06V40/16
CPC classification number: G06V40/172
Abstract: Systems and methods for diversity auditing are described. The systems and methods include identifying a plurality of images; detecting a face in each of the plurality of images using a face detection network; classifying the face in each of the plurality of images based on a sensitive attribute using an image classification network; generating a distribution of the sensitive attribute in the plurality of images based on the classification; and computing a diversity score for the plurality of images based on the distribution.
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公开(公告)号：US11593648B2
公开(公告)日：2023-02-28
申请号：US16844006
申请日：2020-04-09
Applicant: Adobe Inc.
Inventor： Sana Lee , Po Ming Law , Moumita Sinha , Fan Du
Abstract: This disclosure involves detecting biases in predictive models and the root cause of those biases. For example, a processing device receives test data and training data from a client device. The processing device identifies feature groups from the training data and the test data generates performance metrics and baseline metrics for a feature group. The processing device detects biases through a comparison of the performance metrics and the baseline metrics the feature group. The processing device then isolates a portion of the training data that corresponds to the detected bias. The processing device generates a model correction usable to remove the bias from the predictive model.
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公开(公告)号：US10783549B2
公开(公告)日：2020-09-22
申请号：US15355673
申请日：2016-11-18
Applicant: Adobe Inc.
Inventor： Moumita Sinha , Varun Gupta , Tathagata Sengupta , Niloy Ganguly , Faran Ahmad
IPC: G06Q30/02
Abstract: The present disclosure is directed towards methods and systems for determining a persuasiveness of a content item. The systems and methods receive a content item from a client device and analyze the content item. Analyzing the content item includes analyzing at least one textual element, at least one image element, and at least one layout element of the content item to determine a first persuasion score, a second persuasion score, and a third persuasion score of the elements the content item. The systems and methods also generate a persuasion score of the content item and provide the persuasion score of the content item to the client device.
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公开(公告)号：US20190087861A1
公开(公告)日：2019-03-21
申请号：US16192517
申请日：2018-11-15
Applicant: Adobe Inc.
IPC: G06Q30/02
Abstract: The present disclosure is directed toward systems and methods for generating an un-subscription model and predicting whether a potential customer will un-subscribe from receiving electronic marketing content from a marketing source. For example, systems and methods described herein involve generating a prediction un-subscription model that predicts whether a potential customer is prone to un-subscribe from receiving future communications about a product or merchant in response to receiving a communication for the product or merchant. The systems and methods further involve determining an appropriate action to take with regard to a potential customer based on whether the potential customer is prone to un-subscribe from receiving future communications.
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Patent Agency Ranking
公开(公告)号：US20240281836A1
公开(公告)日：2024-08-22
申请号：US18110620
申请日：2023-02-16
Applicant: Adobe Inc.
Inventor： Ritwik Sinha , Ziao Liu , Robert Sebastian Mares , Oana Catalina Persoiu-Focsa , Moumita Sinha , Ivan Andrus , David Arbour , Akash Maharaj , Prithvi Bhutani
IPC: G06Q30/0203 , G06F17/18
CPC classification number: G06Q30/0203 , G06F17/18
Abstract: 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：US11995520B2
公开(公告)日：2024-05-28
申请号：US16520645
申请日：2019-07-24
Applicant: Adobe Inc.
Inventor： Ritwik Sinha , Sunny Dhamnani , Moumita Sinha
IPC: G06N20/00 , G06F16/904 , G06N5/045 , G06N20/20
CPC classification number: G06N20/00 , G06F16/904 , G06N5/045 , G06N20/20
Abstract: The present disclosure relates to a feature contribution system that accurately and efficiently provides the influence of features utilized in machine-learning models with respect to observed model results. In particular, the feature contribution system can utilize an observed model result, initial contribution values, and historical feature values to determine a contribution value correction factor. Further, the feature contribution system can apply the correction factor to the initial contribution values to determine correction-factor adjusted contribution values of each feature of the model with respect to the observed model result.
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公开(公告)号：US20210027191A1
公开(公告)日：2021-01-28
申请号：US16520645
申请日：2019-07-24
Applicant: Adobe Inc.
Inventor： Ritwik Sinha , Sunny Dhamnani , Moumita Sinha
IPC: G06N20/00 , G06F16/904
Abstract: The present disclosure relates to a feature contribution system that accurately and efficiently provides the influence of features utilized in machine-learning models with respect to observed model results. In particular, the feature contribution system can utilize an observed model result, initial contribution values, and historical feature values to determine a contribution value correction factor. Further, the feature contribution system can apply the correction factor to the initial contribution values to determine correction-factor adjusted contribution values of each feature of the model with respect to the observed model result.
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公开(公告)号：US10755088B2
公开(公告)日：2020-08-25
申请号：US15868531
申请日：2018-01-11
Applicant: Adobe Inc.
Inventor： Kushal Chawla , Vaishnav Pawan Madandas , Moumita Sinha , Gaurush Hiranandani , Aditya Jain
Abstract: Systems and methods are disclosed herein for determining user behavior in an augmented reality environment. An augmented reality application executing on a computing system receives a video depicting a face of a person. The video includes a video frame. The augmented reality application augments the video frame with an image of an item selected via input from a user device associated with a user. The augmented reality application determines, from the video frame, a score representing an action unit. The action unit represents a muscle on the face of the person depicted by the video frame and the score represents an intensity of the action unit. The augmented reality application calculates, from a predictive model and based on the score, an indicator of intent of the person depicted by the video.
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10.发明申请公开(公告)号：US20190213476A1
公开(公告)日：2019-07-11
申请号：US15867169
申请日：2018-01-10
Applicant: Adobe Inc.
Inventor： Harvineet Singh , Sahil Garg , Neha Banerjee , Moumita Sinha , Atanu Sinha
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for determining and applying digital content transmission times using machine-learning. For example, in one or more embodiments, the disclosed system trains a recurrent neural network based on past electronic messages for a user that have been partitioned into a plurality of time bins. Additionally, in one or more embodiments, the system utilizes the trained recurrent neural network to generate predictions of engagement metrics (e.g., a hazard metric based on survival analysis or interaction probability metric) for sending a new electronic message within the plurality of time bins. The system then executes the digital content campaign by selecting a time bin based on the predicted engagement metrics and then sending the new electronic message at a send time corresponding to the selected time bin.
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