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公开(公告)号：US12205127B2
公开(公告)日：2025-01-21
申请号：US17232591
申请日：2021-04-16
Applicant: ADOBE INC.
Inventor： Sukriti Verma , Shripad Deshmukh , Jayakumar Subramanian , Piyush Gupta , Nikaash Puri
IPC: G06Q30/0201 , G06N3/047 , G06N3/08
Abstract: Interactions between a user and an e-commerce platform are automatically guided to increase the chances of a conversion. Previous sequences of interactions (e.g., conversion journeys and non-conversion journeys) with the e-commerce platform are collected, an artificial neural network (ANN) learns how to estimate a safety value a current user state by learning from previous user interactions (e.g., conversion and non-conversion journeys), a software agent of the e-commerce platform applies a current user state of the user to the ANN to determine a current safety value, and the software agent provides content to the user based on the current safety value and the current user state.
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公开(公告)号：US20240355020A1
公开(公告)日：2024-10-24
申请号：US18304534
申请日：2023-04-21
Applicant: Adobe Inc.
Inventor： Yaman Kumar , Somesh Singh , Seoyoung Park , Pranjal Prasoon , Nithyakala Sainath , Nisarg Shailesh Joshi , Nikitha Srikanth , Nikaash Puri , Milan Aggarwal , Jayakumar Subramanian , Ganesh Palwe , Balaji Krishnamurthy , Matthew William Rozen , Mihir Naware , Hyman Chung
Abstract: In implementations of systems for digital content analysis, a computing device implements an analysis system to extract a first content component and a second content component from digital content to be analyzed based on content metrics. The analysis system generates first embeddings using a first machine learning model and second embedding using a second machine learning model. The first embeddings and the second embeddings are combined as concatenated embeddings. The analysis system generates an indication of a content metric for display in a user interface using a third machine learning model based on the concatenated embeddings.
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公开(公告)号：US11861636B2
公开(公告)日：2024-01-02
申请号：US16910357
申请日：2020-06-24
Applicant: ADOBE INC.
Inventor： Pankhri Singhai , Piyush Gupta , Balaji Krishnamurthy , Jayakumar Subramanian , Nikaash Puri
IPC: G06Q30/02 , G06Q30/0204 , G06N20/00 , G06Q30/0201 , G06Q10/0633
CPC classification number: G06Q30/0205 , G06N20/00 , G06Q10/0633 , G06Q30/0201
Abstract: Methods and systems are provided for generating and providing insights associated with a journey. In embodiments described herein, journey data associated with a journey is obtained. A journey can include journey paths indicating workflows through which audience members can traverse. The journey data can include audience member attributes (e.g., demographics) and labels indicating journey paths traversed by audience members. A set of audience segments are determined that describe a set of audience members traversing a particular journey path. The set of audience segments can be determined using the journey data to train a segmentation model and, thereafter, analyzing the segmentation model to identify patterns that indicate audience segments associated with the particular journey path. An indication of the set of audience segments that describe the set of audience members traversing the particular journey path can be provided for display.
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Patent Agency Ranking
公开(公告)号：US20220335508A1
公开(公告)日：2022-10-20
申请号：US17232591
申请日：2021-04-16
Applicant: ADOBE INC.
Inventor： Sukriti Verma , Shripad Deshmukh , Jayakumar Subramanian , Piyush Gupta , Nikaash Puri
Abstract: Interactions between a user and an e-commerce platform are automatically guided to increase the chances of a conversion. Previous sequences of interactions (e.g., conversion journeys and non-conversion journeys) with the e-commerce platform are collected, an artificial neural network (ANN) learns how to estimate a safety value a current user state by learning from previous user interactions (e.g., conversion and non-conversion journeys), a software agent of the e-commerce platform applies a current user state of the user to the ANN to determine a current safety value, and the software agent provides content to the user based on the current safety value and the current user state.
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公开(公告)号：US20240403651A1
公开(公告)日：2024-12-05
申请号：US18328174
申请日：2023-06-02
Applicant: Adobe Inc.
Inventor： Shripad Vilasrao Deshmukh , Arpan Dasgupta , Balaji Krishnamurthy , Chirag Agarwal , Georgios Theocharous , Jayakumar Subramanian
IPC: G06N3/092
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that provide a trajectory-based explainability framework for reinforcement learning models. For example, the disclosed systems generate trajectory clusters from trajectories utilized to train a reinforcement learning agent. In some embodiments, the disclosed system generates a complementary target data set by removing a target trajectory cluster from the trajectory clusters. In some cases, the disclosed system trains a test reinforcement learning agent utilizing the complementary target data set and generates a cluster attribution by comparing the result of the test reinforcement learning agent with the result of the reinforcement learning agent.
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公开(公告)号：US12111884B2
公开(公告)日：2024-10-08
申请号：US17659983
申请日：2022-04-20
Applicant: ADOBE INC.
Inventor： Tanay Anand , Pinkesh Badjatiya , Sriyash Poddar , Jayakumar Subramanian , Georgios Theocharous , Balaji Krishnamurthy
IPC: G06F18/2137 , G06N3/088
CPC classification number: G06F18/2137 , G06N3/088
Abstract: Systems and methods for machine learning are described. Embodiments of the present disclosure receive state information that describes a state of a decision making agent in an environment; compute an action vector from an action embedding space based on the state information using a policy neural network of the decision making agent, wherein the policy neural network is trained using reinforcement learning based on a topology loss that constrains changes in a mapping between an action set and the action embedding space; and perform an action that modifies the state of the decision making agent in the environment based on the action vector, wherein the action is selected based on the mapping.
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公开(公告)号：US20240005146A1
公开(公告)日：2024-01-04
申请号：US17855085
申请日：2022-06-30
Applicant: Adobe Inc. , Delhi Technological University
Inventor： Tanay Anand , Piyush Gupta , Pinkesh Badjatiya , Nikaash Puri , Jayakumar Subramanian , Balaji Krishnamurthy , Chirag Singla , Rachit Bansal , Anil Singh Parihar
CPC classification number: G06N3/08 , G06N3/0445
Abstract: In some embodiments, techniques for extracting high-value sequential patterns are provided. For example, a process may involve training a machine learning model to learn a state-action map that contains high-utility sequential patterns; extracting at least one high-utility sequential pattern from the trained machine learning model; and causing a user interface of a computing environment to be modified based on information from the at least one high-utility sequential pattern.
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公开(公告)号：US20230342425A1
公开(公告)日：2023-10-26
申请号：US17659983
申请日：2022-04-20
Applicant: ADOBE INC.
Inventor： Tanay Anand , Pinkesh Badjatiya , Sriyash Poddar , Jayakumar Subramanian , Georgios Theocharous , Balaji Krishnamurthy
CPC classification number: G06K9/6251 , G06N3/088
Abstract: Systems and methods for machine learning are described. Embodiments of the present disclosure receive state information that describes a state of a decision making agent in an environment; compute an action vector from an action embedding space based on the state information using a policy neural network of the decision making agent, wherein the policy neural network is trained using reinforcement learning based on a topology loss that constrains changes in a mapping between an action set and the action embedding space; and perform an action that modifies the state of the decision making agent in the environment based on the action vector, wherein the action is selected based on the mapping.
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