METHOD AND SYSTEM OF CONTROLLING VEHICLE OPERATIONS BASED ON UNSTATED AND STATED REQUIREMENTS OF OCCUPANTS

    公开(公告)号:US20240246551A1

    公开(公告)日:2024-07-25

    申请号:US18186255

    申请日:2023-03-20

    申请人: Wipro Limited

    IPC分类号: B60W50/10

    摘要: Embodiments of present disclosure relates to method and system of controlling operations of the vehicle based on the requirements of the occupants in the vehicle. The occupant support system receives data from a plurality of sources installed in the vehicle. The occupant support system identifies one or more unstated requirements of an occupant in the vehicle based on the data. The occupant support system determines one or more changes associated with one or more operations of the vehicle, based on the one or more unstated requirements. The occupant support system determines feasibility of implementing the one or more changes for controlling the one or more operations of the vehicle. Thereafter, the occupant support system controls the one or more operations of the vehicle, based on the feasibility.

    Method and system for effective service fulfillment in communication networks

    公开(公告)号:US11996991B2

    公开(公告)日:2024-05-28

    申请号:US17658084

    申请日:2022-04-05

    申请人: Wipro Limited

    摘要: A method and system for effective service fulfillment in a communication network is disclosed. The method includes determining utilization of each of a plurality of resources for each of a plurality of network slice instances. The method further includes determining a service allocation schedule and a service resource utilization across one or more service classes and one or more resource classes for each of the plurality of resources; determining at least one of a set of possible actions required to be performed in at least one of the plurality of resources, a plurality of services, and the plurality of network slice instances based on a consolidation plan; assessing an impact of each of the set of possible actions based on a plurality of factors; and triggering a relevant network function(s) to perform at least one of the set of determined possible actions based on the assessed impact.

    METHOD AND SYSTEM FOR IDENTIFYING AN OPTIMIZED SET OF CODE COMMITS TO PERFORM VULNERABILITY REMEDIATION

    公开(公告)号:US20240169069A1

    公开(公告)日:2024-05-23

    申请号:US18157849

    申请日:2023-01-23

    申请人: Wipro Limited

    IPC分类号: G06F21/57

    CPC分类号: G06F21/577 G06F2221/033

    摘要: Embodiments of present disclosure relates to method and remediation system of performing remediation for managing vulnerabilities in application. The remediation system receives data related to source code associated with plurality of vulnerabilities and target code of application from one or more data sources. The remediation system identifies commit-log comprising plurality of code commits by extracting features, code commits and test cases from one or more data sources. The remediation system determines lower bound limit and upper bound limit to identify optimal code commits log from commit-log. Thereafter, the remediation system performs remediation by generating security patches for optimal code commits log. Thus, the present disclosure automatically identifies optimal code commits log for which security patches needs to be generated without any manual intervention.

    METHOD AND SYSTEM FOR PREDICTING DEMAND FOR SUPPLY CHAIN

    公开(公告)号:US20230342796A1

    公开(公告)日:2023-10-26

    申请号:US18193330

    申请日:2023-03-30

    申请人: Wipro Limited

    IPC分类号: G06Q30/0202

    CPC分类号: G06Q30/0202

    摘要: A method and a system for predicting demand for a supply chain is disclosed. The method includes feeding input vectors to a trained Machine Learning (ML) model, for a future time-period. The input vectors include an intensity vector corresponding to an intensity of a possible disruption-event at each point of time within the future time-period and a duration vector corresponding to the duration of the possible disruption-event, and one or more extrinsic data vectors. The method further includes obtaining a demand for a target product in the future time-period from the trained ML model based on the input vectors.