FEATURE-SPECIFIC ADAPTIVE MODELS FOR SUPPORT TOOLS

    公开(公告)号:US20180314958A1

    公开(公告)日:2018-11-01

    申请号:US15582559

    申请日:2017-04-28

    IPC分类号: G06N5/04 G06N99/00

    摘要: In one embodiment, a machine learning server in a computer network determines a plurality of computing features shared across a given set of computing products, and collects, from each computing product of the given set, problem-solution data for each computing feature of the plurality of computing features. Problem-solution data is indicative of problems related to a respective computing feature, attempted solution actions for the problems, and outcomes of the attempted solutions on the problem. The machine learning server updates a machine learning model of suggested solutions for computing-feature-specific problems based on the collected problem-solution data, and provides, based on the machine learning model, a particular suggested solution for a particular computing-feature-specific problem to a particular computing product. An outcome of the particular suggested solution for the particular computing-feature-specific problem on the particular computing product may then be fed back to the machine learning server as collected problem-solution data.

    Feature-specific adaptive models for support tools

    公开(公告)号:US11216744B2

    公开(公告)日:2022-01-04

    申请号:US15582559

    申请日:2017-04-28

    摘要: In one embodiment, a machine learning server in a computer network determines a plurality of computing features shared across a given set of computing products, and collects, from each computing product of the given set, problem-solution data for each computing feature of the plurality of computing features. Problem-solution data is indicative of problems related to a respective computing feature, attempted solution actions for the problems, and outcomes of the attempted solutions on the problem. The machine learning server updates a machine learning model of suggested solutions for computing-feature-specific problems based on the collected problem-solution data, and provides, based on the machine learning model, a particular suggested solution for a particular computing-feature-specific problem to a particular computing product. An outcome of the particular suggested solution for the particular computing-feature-specific problem on the particular computing product may then be fed back to the machine learning server as collected problem-solution data.