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    Determining feature contributions to data metrics utilizing a causal dependency model
    11.
    发明授权
    Determining feature contributions to data metrics utilizing a causal dependency model 有权

    公开(公告)号：US11797515B2

    公开(公告)日：2023-10-24

    申请号：US16813424

    申请日：2020-03-09

    Applicant: Adobe Inc.

    Inventor： Pulkit Goel ,  Naman Poddar ,  Gaurav Sinha ,  Ayush Chauhan ,  Aurghya Maiti

    IPC: G06F16/23

    CPC classification number: G06F16/2365

    Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining causal contributions of dimension values to anomalous data based on causal effects of such dimension values on the occurrence of other dimension values from interventions performed in a causal graph. For example, the disclosed systems can identify an anomalous dimension value that reflects a threshold change in value between an anomalous time period and a reference time period. The disclosed systems can determine causal effects by traversing a causal network representing dependencies between different dimensions associated with the dimension values. Based on the causal effects, the disclosed systems can determine causal contributions of particular dimension values on the anomalous dimension value. Further, the disclosed systems can generate a causal-contribution ranking of the particular dimension values based on the determined causal contributions.

    TREATMENT EFFECT ESTIMATION USING OBSERVATIONAL AND INTERVENTIONAL SAMPLES
    12.
    发明公开
    TREATMENT EFFECT ESTIMATION USING OBSERVATIONAL AND INTERVENTIONAL SAMPLES 审中-公开

    公开(公告)号：US20230144357A1

    公开(公告)日：2023-05-11

    申请号：US17520403

    申请日：2021-11-05

    Applicant: ADOBE INC.

    Inventor： Gaurav Sinha ,  Aurghya Maiti

    IPC: G06Q10/06

    CPC classification number: G06Q10/0637

    Abstract: A treatment effect system estimates treatment effects by trading off between observational samples and interventional samples to maintain within a budget while providing high confidence. The treatment effect system determines whether to perform interventions by comparing the cost of interventional samples with metrics regarding the joint probability distribution of treatments and their parents in a first set of observational samples. If it is determined to not perform interventions, the treatment effect for each treatment is determined using an estimator that uses the first set of observational samples independent of a second set of observational samples. If it is determined to perform interventions, each treatment is identified as a reliable or unreliable treatment. The treatment effect for reliable treatments is estimated using an estimator that uses the first set of observational samples split into two portions. The treatment effect for unreliable treatments is estimated using interventional samples generated from interventions.

    Systems for Predicting a Terminal Event
    13.
    发明申请
    Systems for Predicting a Terminal Event 有权

    公开(公告)号：US20210342649A1

    公开(公告)日：2021-11-04

    申请号：US16866261

    申请日：2020-05-04

    Applicant: Adobe Inc.

    Inventor： 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: G06K9/62 ,  G06F17/18 ,  G06N20/00 ,  G06F3/0481

    Abstract: 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.

    JOINTLY PREDICTING MULTIPLE INDIVIDUAL-LEVEL FEATURES FROM AGGREGATE DATA
    16.
    发明公开
    JOINTLY PREDICTING MULTIPLE INDIVIDUAL-LEVEL FEATURES FROM AGGREGATE DATA 审中-公开

    公开(公告)号：US20230274310A1

    公开(公告)日：2023-08-31

    申请号：US17680932

    申请日：2022-02-25

    Applicant: ADOBE INC.

    Inventor： Gaurav Sinha ,  Vibhor Porwal ,  Isha Chaudhary ,  Simarpreet Singh Saluja ,  Rashul Chutani ,  Shaurya Goel

    IPC: G06Q30/02 ,  G06K9/62

    CPC classification number: G06Q30/0246 ,  G06K9/6232 ,  G06K9/6256

    Abstract: An analytics system jointly predicts values for multiple unobserved individual-level features using aggregate data for those features. Given a dataset, a transformation is applied to individual-level information for the dataset to generate transformed data in a higher dimensional space. Bag-wise mean embeddings are generated using the transformed data. The bag-wise mean embeddings and aggregate data for unobserved individual-level features for the dataset are used to train a model to jointly predict values for the unobserved individual-features for data instances. In particular, a given data instance can be transformed to a representation in a higher dimensional space. Given this representation, the trained model predicts values for the unobserved individual-level features for the data instance, and the data instance can be augmented with the predicted values.

    INDIVIDUAL TREATMENT ASSIGNMENT FROM MIXTURE OF INTERVENTIONS
    17.
    发明公开
    INDIVIDUAL TREATMENT ASSIGNMENT FROM MIXTURE OF INTERVENTIONS 审中-公开

    公开(公告)号：US20230259963A1

    公开(公告)日：2023-08-17

    申请号：US17671082

    申请日：2022-02-14

    Applicant: ADOBE INC.

    Inventor： Gaurav Sinha ,  Abhinav Kumar

    IPC: G06Q30/02

    CPC classification number: G06Q30/0242

    Abstract: An analytics system identifies interventions for individual samples from a set of samples with a mixture of interventions. Given a causal graph, a set of baseline samples, and a set of samples with interventions, a set of intervention tuples is determined that represents the mixture of interventions for the set of samples with interventions. Each intervention tuple in the set of intervention tuples identifies an intervention and a mixing coefficient representing a percentage of samples with the intervention. An iterative process is used in which a set of intervention tuples is determined for N variables and then lifted to a set of intervention tuples for N+1 variables until all variables from the causal graph have been considered, providing a final set of intervention tuples. The final set of intervention tuples is used to match individual samples from the set of samples with interventions to interventions.

    CAUSAL MULTI-TOUCH ATTRIBUTION
    18.
    发明申请
    CAUSAL MULTI-TOUCH ATTRIBUTION 有权

    公开(公告)号：US20230127453A1

    公开(公告)日：2023-04-27

    申请号：US17452519

    申请日：2021-10-27

    Applicant: ADOBE INC.

    Inventor： Aniket Agrawal ,  Nikhil Sheoran ,  Gaurav Sinha

    IPC: G06N3/04 ,  G06N3/08

    Abstract: An apparatus and method for causal multi-touch attribution are described. One or more aspects of the apparatus and method include a time series component configured to generate an ordered series representing a plurality of precursor events corresponding to a result event, wherein each of the precursor events is associated with an event category from a set of event categories; a temporal convolution network configured to generate a series of predictive values corresponding to the plurality of precursor events by computing a plurality of hidden vector representations for at least one of the precursor events; and an attribution component configured to compute an attribution value for each of the event categories based on the series of predictive values.

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