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    GENERATING SIMULATED IMAGES THAT ENHANCE SOCIO-DEMOGRAPHIC DIVERSITY
    21.
    发明申请
    GENERATING SIMULATED IMAGES THAT ENHANCE SOCIO-DEMOGRAPHIC DIVERSITY 有权

    公开(公告)号：US20230094954A1

    公开(公告)日：2023-03-30

    申请号：US17485780

    申请日：2021-09-27

    Applicant: Adobe Inc.

    Inventor： Ritwik Sinha ,  Sridhar Mahadevan ,  Moumita Sinha ,  Md Mehrab Tanjim ,  Krishna Kumar Singh ,  David Arbour

    IPC: G06K9/62 ,  G06N3/04

    Abstract: 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.

    Utilizing logical-form dialogue generation for multi-turn construction of paired natural language queries and query-language representations
    22.
    发明授权
    Utilizing logical-form dialogue generation for multi-turn construction of paired natural language queries and query-language representations 有权

    公开(公告)号：US11561969B2

    公开(公告)日：2023-01-24

    申请号：US16834850

    申请日：2020-03-30

    Applicant: Adobe Inc.

    Inventor： Doo Soon Kim ,  Anthony M Colas ,  Franck Dernoncourt ,  Moumita Sinha ,  Trung Bui

    IPC: G06F16/00 ,  G06F16/242 ,  G06F16/28

    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media for generating pairs of natural language queries and corresponding query-language representations. For example, the disclosed systems can generate a contextual representation of a prior-generated dialogue sequence to compare with logical-form rules. In some implementations, the logical-form rules comprise trigger conditions and corresponding logical-form actions for constructing a logical-form representation of a subsequent dialogue sequence. Based on the comparison to logical-form rules indicating satisfaction of one or more trigger conditions, the disclosed systems can perform logical-form actions to generate a logical-form representation of a subsequent dialogue sequence. In turn, the disclosed systems can apply a natural-language-to-query-language (NL2QL) template to the logical-form representation to generate a natural language query and a corresponding query-language representation for the subsequent dialogue sequence.

    Predicting unsubscription of subscribing users
    23.
    发明授权
    Predicting unsubscription of subscribing users 有权

    公开(公告)号：US11170407B2

    公开(公告)日：2021-11-09

    申请号：US16192517

    申请日：2018-11-15

    Applicant: Adobe Inc.

    Inventor： Moumita Sinha ,  Kandarp Sunil Khandwala ,  Harvineet Singh ,  Dharwar Prasanna Kumar Tejas

    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.

    METHODS AND SYSTEMS FOR DETECTION AND ISOLATION OF BIAS IN PREDICTIVE MODELS
    24.
    发明申请
    METHODS AND SYSTEMS FOR DETECTION AND ISOLATION OF BIAS IN PREDICTIVE MODELS 有权

    公开(公告)号：US20210319333A1

    公开(公告)日：2021-10-14

    申请号：US16844006

    申请日：2020-04-09

    Applicant: Adobe Inc.

    Inventor： Sana Lee ,  Po Ming Law ,  Moumita Sinha ,  Fan Du

    IPC: G06N5/04 ,  G06N20/00

    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.

    MODELING TIME TO OPEN OF ELECTRONIC COMMUNICATIONS
    25.
    发明申请
    MODELING TIME TO OPEN OF ELECTRONIC COMMUNICATIONS 审中-公开

    公开(公告)号：US20190138944A1

    公开(公告)日：2019-05-09

    申请号：US15808171

    申请日：2017-11-09

    Applicant: Adobe Inc.

    Inventor： Moumita Sinha ,  Vishwa Vinay ,  Harvineet Singh ,  Frederic Mary

    IPC: G06N99/00

    Abstract: The present disclosure relates applying a survival analysis to model when a particular recipient will view an electronic message. For example, one or more embodiments train a survivor function to model the time that will elapse, on a continuous scale, before a recipient will open an electronic message. For example, one or more embodiments involve accessing analytics training data and extracting a first set of features affecting the time that elapsed before past recipients opened an electronic message and a second set of features affecting whether the recipients opened the electronic message at all. The system then generates a mixture model modified survivor function and determines the effect of each feature set on its corresponding outcome to learn parameters for the mixture model modified survivor function.

    Trait Expansion Techniques in Binary Matrix Datasets
    28.
    发明公开
    Trait Expansion Techniques in Binary Matrix Datasets 审中-公开

    公开(公告)号：US20230267132A1

    公开(公告)日：2023-08-24

    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

    IPC: G06F16/28

    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.

    FACILITATING ONLINE RESOURCE ACCESS WITH BIAS CORRECTED TRAINING DATA GENERATED FOR FAIRNESS-AWARE PREDICTIVE MODELS
    30.
    发明申请
    FACILITATING ONLINE RESOURCE ACCESS WITH BIAS CORRECTED TRAINING DATA GENERATED FOR FAIRNESS-AWARE PREDICTIVE MODELS 审中-公开

    公开(公告)号：US20200226489A1

    公开(公告)日：2020-07-16

    申请号：US16247297

    申请日：2019-01-14

    Applicant: Adobe Inc.

    Inventor： Yancheng Li ,  Moumita Sinha ,  Haichun Chen

    IPC: G06N20/00 ,  G06N5/02

    Abstract: In some embodiments, a computing system generates de-biased training data for fairness-aware predictive models to facilitate online resource access. The computing system extracts latent features from training data of a first machine learning model for predicting an access flag for a user indicating the ability of the user to access an online environment. Based on the latent features, the computing system trains a second machine learning model to generate de-biased training data by applying a loss function that includes loss terms associated with an individual bias and a group bias of the training data. The de-biased training data are utilized to train the first machine learning model and to update the access flag for the user by applying the first machine learning model to attributes of the user. A user device associated with the user can be provided with access to the online environment according to the updated access flag.

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