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    KEYPRESS EVENT SMOOTHENER AND PREDICTOR
    1.
    发明申请
    KEYPRESS EVENT SMOOTHENER AND PREDICTOR 有权

    公开(公告)号：US20220121459A1

    公开(公告)日：2022-04-21

    申请号：US17075804

    申请日：2020-10-21

    Applicant: Adobe Inc.

    Inventor： Reetesh Mukul ,  Mayuri Jain

    IPC: G06F9/451 ,  G06F9/54 ,  G06F3/0488 ,  G06F3/023

    Abstract: In some embodiments, a key smoothener and predictor module of a software application executing on a computing device receives a sequence of key events from an input device of the computing device and through a user interface of the software application. The key smoothener and predictor module stores the sequence of key events in a key event queue and predicts the total number of key events for processing in a current processing cycle of the application based on the sequence of key events. A processing component of the software application processes an aggregated key event that indicates multiple keypresses. The number of the multiple keypresses is the same as the predicted total number of key events for the current processing cycle. The software application further causes the user interface of the software application to be updated based on processing the aggregated key event.

    Markov decision process for efficient data transfer
    3.
    发明授权
    Markov decision process for efficient data transfer 有权

    公开(公告)号：US12135741B2

    公开(公告)日：2024-11-05

    申请号：US16943331

    申请日：2020-07-30

    Applicant: Adobe Inc.

    Inventor： Reetesh Mukul ,  Mayuri Jain

    IPC: G06N20/00 ,  G06F16/58

    Abstract: Techniques are disclosed for improving transfer speed for a plurality of files (e.g., image files) by using a Markov decision process to determine an optimal number of parallel instances of transfer stages and optimal file batch sizes for each instance. The transfer (e.g., import or export) operation involves different stages that are each optimized using the algorithm. The stages include a file fetch operation, a file processing operation, and a database update operation. Each of the stages may have multiple parallel instances to process many files at the same time. The Markov decision process uses a reward structure to determine the optimal number of parallel instances for each stage and the number of files operated on at each instance at any given moment in time. The process is dynamic and adaptable to any system environment since it does not rely on any particular hardware or operating system configuration.

    Keypress event smoothener and predictor
    8.
    发明授权
    Keypress event smoothener and predictor 有权

    公开(公告)号：US11409548B2

    公开(公告)日：2022-08-09

    申请号：US17075804

    申请日：2020-10-21

    Applicant: Adobe Inc.

    Inventor： Reetesh Mukul ,  Mayuri Jain

    IPC: G06F9/451 ,  G06F9/54 ,  G06F3/023 ,  G06F3/04883 ,  G06F3/04886

    Abstract: In some embodiments, a key smoothener and predictor module of a software application executing on a computing device receives a sequence of key events from an input device of the computing device and through a user interface of the software application. The key smoothener and predictor module stores the sequence of key events in a key event queue and predicts the total number of key events for processing in a current processing cycle of the application based on the sequence of key events. A processing component of the software application processes an aggregated key event that indicates multiple keypresses. The number of the multiple keypresses is the same as the predicted total number of key events for the current processing cycle. The software application further causes the user interface of the software application to be updated based on processing the aggregated key event.

    MARKOV DECISION PROCESS FOR EFFICIENT DATA TRANSFER
    9.
    发明申请
    MARKOV DECISION PROCESS FOR EFFICIENT DATA TRANSFER 有权

    公开(公告)号：US20220035855A1

    公开(公告)日：2022-02-03

    申请号：US16943331

    申请日：2020-07-30

    Applicant: Adobe Inc.

    Inventor： Reetesh Mukul ,  Mayuri Jain

    IPC: G06F16/58 ,  G06F16/23 ,  G06F16/18 ,  G06N7/00

    Abstract: Techniques are disclosed for improving transfer speed for a plurality of files (e.g., image files) by using a Markov decision process to determine an optimal number of parallel instances of transfer stages and optimal file batch sizes for each instance. The transfer (e.g., import or export) operation involves different stages that are each optimized using the algorithm. The stages include a file fetch operation, a file processing operation, and a database update operation. Each of the stages may have multiple parallel instances to process many files at the same time. The Markov decision process uses a reward structure to determine the optimal number of parallel instances for each stage and the number of files operated on at each instance at any given moment in time. The process is dynamic and adaptable to any system environment since it does not rely on any particular hardware or operating system configuration.

    Context-based Recommendation System for Feature Search
    10.
    发明申请
    Context-based Recommendation System for Feature Search 有权

    公开(公告)号：US20210357440A1

    公开(公告)日：2021-11-18

    申请号：US16876624

    申请日：2020-05-18

    Applicant: Adobe Inc.

    Inventor： Sudhir Tubegere Shankaranarayana ,  Sreenivas Ramaswamy ,  Sachin Tripathi ,  Reetesh Mukul ,  Mayuri Jain ,  Bhakti Ramnani

    IPC: G06F16/332 ,  G06N20/00 ,  G06F16/33 ,  G06F40/284

    Abstract: A context-based recommendation system for feature search automatically identifies features of a feature-rich system (e.g., an application) based on the program code of the feature-rich system and additional data corresponding to the feature-rich system. A code workflow graph describing workflows in the program code is generated. Various data corresponding to the feature-rich system, such as help data, analytics data, social media data, and so forth is obtained. The code workflow graph and the data are analyzed to identify sentences in the workflow. These sentences are used to a train machine learning system to generate one or more recommendations. In response to a user query, the machine learning system generates and outputs as recommendations workflows identified based on the user query.

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