公开(公告)号：US20210133612A1

公开(公告)日：2021-05-06

申请号：US16670543

申请日：2019-10-31

Applicant: Adobe Inc.

Inventor： Ritwik Sinha ,  Sunny Dhamnani

IPC: G06N7/00 ,  G06N20/00 ,  G06K9/62 ,  G06F30/20

Abstract: This disclosure involves generating graph data structures that model inter-feature dependencies for use with machine-learning models to predict end-user behavior. For example, a processing device receives an input dataset and a request to modify a first input feature of the input dataset. The processing device uses a graph data structure that models the inter-feature dependencies to modify the input dataset by propagating the modification of the first input feature to a second input feature dependent on the first input feature. The modification to the second input feature is a function of at least (a) the value of the first input feature and (b) a weight assigned to an edge linking the first input feature to the second input feature within the directed graph. The processing device then applies a trained machine-learning model to the modified input dataset to generate a prediction of an outcome.

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.

公开(公告)号：US20220004898A1

公开(公告)日：2022-01-06

申请号：US16921202

申请日：2020-07-06

Applicant: Adobe Inc.

Inventor： Atanu R Sinha ,  Tanay Asija ,  Sunny Dhamnani ,  Raja Kumar Dubey ,  Navita Goyal ,  Kaarthik Raja Meenakshi Viswanathan ,  Georgios Theocharous

IPC: G06N5/04 ,  G06F9/451 ,  G06N20/00

Abstract: The present disclosure relates to methods, systems, and non-transitory computer-readable media for determining a cognitive, action-selection bias of a user that influences how the user will select a sequence of digital actions for execution of a task. For example, the disclosed systems can identify, from a digital behavior log of a user, a set of digital action sequences that correspond to a set of sessions for a task previously executed by the user. The disclosed systems can utilize a machine learning model to analyze the set of sessions to generate session weights. The session weights can correspond to an action-selection bias that indicates an extent to which a future session for the task executed by the user is predicted to be influenced by the set of sessions. The disclosed systems can provide a visual indication of the action-selection bias of the user for display on a graphical user interface.

公开(公告)号：US20190356684A1

公开(公告)日：2019-11-21

申请号：US15982393

申请日：2018-05-17

Applicant: Adobe Inc.

Inventor： Ritwik Sinha ,  Vishwa Vinay ,  Sunny Dhamnani ,  Margarita Savova ,  Lilly Kumari ,  David Weinstein

IPC: H04L29/06 ,  H04L12/26 ,  H04L12/24

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for detecting robotic activity while monitoring Internet traffic across a plurality of domains. For example, the disclosed system identifies network session data for each domain of a plurality of domains, the network session data including network sessions comprising features that indicate human activity. In one or more embodiments, the disclosed system generates a classifier to output a probability that a network session at a domain includes human activity. In one or more embodiments, the disclosed system also generates a classifier to output a probability that a network session includes good robotic activity. Additionally, the disclosed system generates a domain-agnostic machine-learning model by combining models from a plurality of domains with network sessions including human activity.