公开(公告)号：US11556393B2

公开(公告)日：2023-01-17

申请号：US16736476

申请日：2020-01-07

Applicant: Adobe Inc.

Inventor： Bhakti Ramnani ,  Sachin Tripathi ,  Reetesh Mukul ,  Prabal Kumar Ghosh

IPC: G06F9/50

Abstract: A resource management system of an application takes various actions to improve or maintain the health of the application (e.g., keep the application from becoming sluggish). The resource management system maintains a reinforcement learning model indicating which actions the resource management system is to take for various different states of the application. The resource management system performs multiple iterations of a process of identifying a current state of the application, determining an action to take to manage resources for the application, and taking the determined action. In each iteration, the resource management system determines the result of the action taken in the previous iteration and updates the reinforcement learning model so that the reinforcement learning model learns which actions improve the health of the application and which actions do not improve the health of the application.

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.

公开(公告)号：US12032607B2

公开(公告)日：2024-07-09

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

CPC classification number: G06F16/3322 ,  G06F16/3334 ,  G06F40/284 ,  G06N20/00

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.

公开(公告)号：US20210209419A1

公开(公告)日：2021-07-08

申请号：US16736476

申请日：2020-01-07

Applicant: Adobe Inc.

Inventor： Bhakti Ramnani ,  Sachin Tripathi ,  Reetesh Mukul ,  Prabal Kumar Ghosh

IPC: G06K9/62 ,  G06F9/50

Abstract: A resource management system of an application takes various actions to improve or maintain the health of the application (e.g., keep the application from becoming sluggish). The resource management system maintains a reinforcement learning model indicating which actions the resource management system is to take for various different states of the application. The resource management system performs multiple iterations of a process of identifying a current state of the application, determining an action to take to manage resources for the application, and taking the determined action. In each iteration, the resource management system determines the result of the action taken in the previous iteration and updates the reinforcement learning model so that the reinforcement learning model learns which actions improve the health of the application and which actions do not improve the health of the application.