Chat-based proactive nudging system and method

    公开(公告)号:US12238061B2

    公开(公告)日:2025-02-25

    申请号:US17880540

    申请日:2022-08-03

    Applicant: SAP SE

    Abstract: A method includes receiving by a chat server a client request from a client device communicating with the chat server. The chat server generates a server response that is transmitted to the client device. A nudge repository is searched for a nudge action based on a set of tokens generated from at least a portion of the client request. In response to finding the nudge action, a user cohort to receive the nudge action is determined. A nudge request including the nudge action and the user cohort is generated and transmitted to the chat server. The nudge action is deployed from the chat server to one or more client devices associated with one or more user identifiers in the user cohort. The nudge action is rendered as a nudge at the one or more client devices.

    Knowledge base for predicting success of cluster scaling

    公开(公告)号:US12175229B2

    公开(公告)日:2024-12-24

    申请号:US17545332

    申请日:2021-12-08

    Abstract: An information handling system may receive a request from a particular remote cluster regarding a cluster scaling event; receive first information from a plurality of other remote clusters indicative of a success or a failure of a corresponding cluster expansion event that was performed at such other remote clusters; receive second information from the plurality of other remote clusters indicative of scores for such other remote clusters in a plurality of metrics; determine, based on the first and second information, a ranking of the metrics based on their criticality to the cluster scaling event; receive third information from the particular remote cluster indicative of scores for the particular remote cluster in the plurality of metrics; and determine a likelihood of success for the cluster scaling event based on the determined ranking of the metrics and the scores for the particular remote cluster in the plurality of metrics.

    Trade platform with reinforcement learning network and matching engine

    公开(公告)号:US12099874B2

    公开(公告)日:2024-09-24

    申请号:US18227079

    申请日:2023-07-27

    Abstract: A system for reinforcement learning in a dynamic resource environment includes at least one memory and at least one processor configured to provide an electronic resource environment comprising: a matching engine and the resource generating agent configured for: obtaining from a historical data processing task database a plurality of historical data processing tasks, each historical data processing task including respective task resource requirement data; for a historical data processing task of the plurality of historical data processing tasks, generating layers of data processing tasks wherein a first layer data processing task has an incremental variant in its resource requirement data relative to resource requirement data for a second layer data processing task; and providing the layers of data processing tasks for matching by the machine engine.

    Targeted training of inductive multi-organization recommendation models for enterprise applications

    公开(公告)号:US11983649B2

    公开(公告)日:2024-05-14

    申请号:US17510523

    申请日:2021-10-26

    CPC classification number: G06Q10/063 G06N5/022 G06N5/04

    Abstract: An enterprise system server, a computer-readable storage medium, and a method for targeted training of inductive multi-organization recommendation models for enterprise applications are described herein. The method includes receiving enterprise application data from remote organization computing systems executing the enterprise application, training per-organization recommendation models for a subset of the organizations, and validating each per-organization recommendation model on enterprise application data corresponding to one or more other organizations. The method also includes calculating a transferability metric for each per-organization recommendation model based on results obtained during validation, determining a specified number of organizations including the best-transferring per-organization recommendation models based on the calculated transferability metrics, and training an inductive multi-organization recommendation model using the enterprise application data from the specified number of organizations. The method further includes utilizing the trained inductive multi-organization recommendation model to provide user recommendations to the remote organization computing systems during execution of the enterprise application.

    MANAGED SOLVER EXECUTION USING DIFFERENT SOLVER TYPES

    公开(公告)号:US20240112067A1

    公开(公告)日:2024-04-04

    申请号:US17936793

    申请日:2022-09-29

    CPC classification number: G06N20/00 G06N5/003

    Abstract: A multitenant solver execution service provides managed infrastructure for defining and solving large-scale optimization problems. In embodiments, the service executes solver jobs on managed compute resources such as virtual machines or containers. The compute resources can be automatically scaled up or down based on client demand and are assigned to solver jobs in a serverless manner. Solver jobs can be initiated based on configured triggers. In embodiments, the service allows users to select from different types of solvers, mix different solvers in a solver job, and translate a model from one solver to another solver. In embodiments, the service provides developer interfaces to, for example, run solver experiments, recommend solver types or solver settings, and suggest model templates. The solver execution service relieves developers from having to manage infrastructure for running optimization solvers and allows developers to easily work with different types of solvers via a unified interface.

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