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    Temporal impact analysis of cascading events on metaverse-based organization avatar entities
    1.
    发明授权
    Temporal impact analysis of cascading events on metaverse-based organization avatar entities 有权

    公开(公告)号：US12229902B2

    公开(公告)日：2025-02-18

    申请号：US17987281

    申请日：2022-11-15

    Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITED

    Inventor： Janardan Misra ,  Sanjay Podder

    IPC: G06T19/00 ,  G06T13/40

    Abstract: In some examples, temporal impact analysis of cascading events on metaverse-based organization avatar entities may include determining a temporal impact of a metaverse event on a specified organization avatar entity. With respect to the specified organization avatar entity, a similarity of the metaverse event may be determined in a current temporal context to past events. A reaction plan of a plurality of reaction plans may be selected from an event database and based on the determined similarity. Based on an analysis of the temporal impact with respect to the selected reaction plan, instructions may be generated to execute the selected reaction plan by a metaverse operating environment.

    Machine learning based semantic structural hole identification
    2.
    发明授权
    Machine learning based semantic structural hole identification 有权

    公开(公告)号：US11893503B2

    公开(公告)日：2024-02-06

    申请号：US16594899

    申请日：2019-10-07

    Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITED

    Inventor： Janardan Misra ,  Sanjay Podder

    IPC: G06N5/022 ,  G06F40/30 ,  G06N5/04

    CPC classification number: G06N5/022 ,  G06F40/30 ,  G06N5/042

    Abstract: In some examples, machine learning based semantic structural hole identification may include mapping each text element of a plurality of text elements of a corpus into an embedding space that includes embeddings that are represented as vectors. A semantic network may be generated based on semantic relatedness between each pair of vectors. A boundary enclosure of the embedding space may be determined, and points to fill the boundary enclosure may be generated. Based on an analysis of voidness for each point within the boundary enclosure, a set of void points and void regions may be identified. Semantic holes may be identified for each void region, and utilized to determine semantic porosity of the corpus. A performance impact may be determined between utilization of the corpus to generate an application by using the text elements without filling the semantic holes and the text elements with the semantic holes filled.

    Location aware learning system for content dispensation for resource-constrained edge devices
    3.
    发明授权
    Location aware learning system for content dispensation for resource-constrained edge devices 有权

    公开(公告)号：US11652896B1

    公开(公告)日：2023-05-16

    申请号：US17744325

    申请日：2022-05-13

    Applicant: Accenture Global Solutions Limited

    Inventor： Sanjay Podder ,  Nataraj Kuntagod ,  Venkatesh Subramanian ,  Satya Sai Srinivas ,  Yogesh Mallaiah

    IPC: H04L67/51

    CPC classification number: H04L67/51

    Abstract: A learning system for automatically transmitting files according to user capabilities is provided. The learning system may include non-transitory memory storing instructions executable to transmit a file to a user device, and a processor circuitry configured to execute the instructions to determine a home base location of a user from at least one of a database storing user information of the user and the user device of the user. The processor circuitry further configured to calculate a travel distance from the home base location of the user to a hub circuitry of the learning system, determine a type of the file and an amount of content of the file based on the travel distance, and transmit, to the user device, the file according to the type of the file and the amount of content of the file.

    Machine learning based quantification of performance impact of data veracity
    6.
    发明授权
    Machine learning based quantification of performance impact of data veracity 有权

    公开(公告)号：US11210471B2

    公开(公告)日：2021-12-28

    申请号：US16526471

    申请日：2019-07-30

    Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITED

    Inventor： Janardan Misra ,  Sanjay Podder ,  Narendranath Sukhavasi

    IPC: G06F40/20 ,  G06F40/30 ,  G06N20/00 ,  G06N5/04 ,  G06F40/205 ,  G06F40/268 ,  G06F40/284

    Abstract: In some examples, machine learning based quantification of performance impact of data irregularities may include generating an irregularity feature vector for each text analytics application of a plurality of text analytics applications. Normalized data associated with a corresponding text analytics application may be generated for each text analytics application and based on minimization of irregularities present in un-normalized data associated with the corresponding text analytics application. An un-normalized data machine learning model may be generated for each text analytics application and based on the un-normalized data associated with the corresponding text analytics application. A normalized data machine learning model may be generated for each text analytics application and based on the normalized data associated with the corresponding text analytics application. A difference in performances may be determined with respect to the un-normalized data machine learning model and the normalized data machine learning model.

    Test automation using multiple programming languages
    8.
    发明授权
    Test automation using multiple programming languages 有权

    公开(公告)号：US10339036B2

    公开(公告)日：2019-07-02

    申请号：US15395436

    申请日：2016-12-30

    Applicant: Accenture Global Solutions Limited

    Inventor： Anurag Dwarakanath ,  Dipin Era ,  Subani Basha Nure ,  Neville Dubash ,  Sanjay Podder ,  Aditya Priyadarshi ,  Bargav Jayaraman

    IPC: G06F11/00 ,  G06F11/36

    Abstract: A device may receive information identifying a first set of instructions. The first set of instructions may identify an action to perform to test a first program. The device may identify a second set of instructions, related to testing a second program, that can be used in association with the first set of instructions. The first test may be similar to the second test. The device may identify multiple steps, of the first set of instructions, that can be combined to form a third set of instructions. The third set of instructions may be used to test the first program or a third program. The device may generate program code in a first programming language to perform the action. The first programming language may be different than a second programming language used to write the first set of instructions. The device may perform the action.

    INTELLIGENT SCHEDULING AND WORK ITEM ALLOCATION
    9.
    发明申请
    INTELLIGENT SCHEDULING AND WORK ITEM ALLOCATION 审中-公开

    公开(公告)号：US20170213171A1

    公开(公告)日：2017-07-27

    申请号：US15003411

    申请日：2016-01-21

    Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITED

    Inventor： Neville Dubash ,  David Edward Ingram ,  Sanjay Podder

    IPC: G06Q10/06

    CPC classification number: G06Q10/06313 ,  G06Q10/063 ,  G06Q10/0631 ,  G06Q10/06311 ,  G06Q10/06314 ,  G06Q10/06316

    Abstract: According to examples, intelligent scheduling and work item allocation may include ascertaining work items, and classifying the work items by using classification rules to map each of the work items to a corresponding type of work item based on attributes associated with the work items to generate classified work items. Intelligent scheduling and work item allocation may include prioritizing the classified work items by using prioritization rules to determine a sequence of the classified work items based on the attributes and classification of the work items to generate prioritized work items. Intelligent scheduling and work item allocation may include scheduling the classified and prioritized work items by using scheduling rules to determine times of processing of the classified and prioritized work items, and allocating the classified and prioritized work items by using allocation rules to determine resources that are to process the classified and prioritized work items.

    SUSTAINABLE RETRAINING FOR PRODUCTION MACHINE LEARNING
    10.
    发明公开
    SUSTAINABLE RETRAINING FOR PRODUCTION MACHINE LEARNING 审中-公开

    公开(公告)号：US20240232698A1

    公开(公告)日：2024-07-11

    申请号：US18095632

    申请日：2023-01-11

    Applicant: ACCENTURE GLOBAL SOLUTIONS LIMITED

    Inventor： Sankar Narayan Das ,  Kuntal Dey ,  Kapil Singi ,  Vikrant Kaulgud ,  Sanjay Podder ,  Andrew Francis Hickl

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: A retraining monitoring system maintains the sustainability of a production machine learning (ML) model system that includes a production ML model retraining platform. The retraining monitoring system collects contextual data from the production ML model system and determines if one or more of a currently-selected architectural options has to be changed for sustainability. An architectural option of the production ML model retraining platform, such as, a processing location is selected from a cloud retraining platform or an on-premises retraining platform by a selection process based on a multi-armed bandit problem. An evaluation of the retraining architecture is dealt with as a reinforcement learning problem to implement one of a periodic retraining architecture or a reactive retraining architecture.

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