Predicting leading indicators of an event

    公开(公告)号:US11262743B2

    公开(公告)日:2022-03-01

    申请号:US16276876

    申请日:2019-02-15

    Applicant: SAP SE

    Abstract: Provided is a system and method for predicting leading indicators for predicting occurrence of an event at a target asset. Rather than rely on traditional manufacturer-defined leading indicators for an asset, the examples herein predict leading indicators for a target asset based on actual operating conditions at the target asset. Accordingly, unanticipated operating conditions can be considered. In one example, the method may include receiving operating data of a target resource, the operating data being associated with previous occurrences of an event at the target resource, predicting one or more leading indicators of the event at the target resource based on the received operating data, each leading indicator comprising a variable and a threshold value for the variable, and outputting information about the one or more predicted leading indicators of the target resource for display via a user interface.

    Extraction and Distribution of Content Packages in a Digital Services Framework

    公开(公告)号:US20200186619A1

    公开(公告)日:2020-06-11

    申请号:US16211807

    申请日:2018-12-06

    Applicant: SAP SE

    Abstract: A data container for a content package comprising one or more semantics for populating the content package with selected types of information associated with a product or service is received by a computing device of a digital services framework. An organizational structure between and within networked tenants of the digital services framework is analyzed to identify one or more recipients for the content package. A data topology associated with the product or service is analyzed to generate announcements indicative of individualized content packages for the identified recipients for the content package. The announcements are sent to the identified recipients. Requests are received for subscriptions to the content package. Based on the analysis of the organizational structure and data topology and user-defined rules and semantics, instances of the container are selectively populating for tenants who have subscribed to the content package. The populated instances of the content package are sent to the subscribed users based on distribution data flows that are identified based at least in part on the analysis of the topological relationships and hierarchical structures.

    ORCHESTRATOR FOR MACHINE LEARNING PIPELINE
    3.
    发明公开

    公开(公告)号:US20230206137A1

    公开(公告)日:2023-06-29

    申请号:US18111839

    申请日:2023-02-20

    Applicant: SAP SE

    CPC classification number: G06N20/20 G06F16/355

    Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

    Extraction and distribution of content packages in a digital services framework

    公开(公告)号:US11496584B2

    公开(公告)日:2022-11-08

    申请号:US16211807

    申请日:2018-12-06

    Applicant: SAP SE

    Abstract: A data container for a content package comprising one or more semantics for populating the content package with selected types of information associated with a product or service is received by a computing device of a digital services framework. An organizational structure between and within networked tenants of the digital services framework is analyzed to identify one or more recipients for the content package. A data topology associated with the product or service is analyzed to generate announcements indicative of individualized content packages for the identified recipients for the content package. The announcements are sent to the identified recipients. Requests are received for subscriptions to the content package. Based on the analysis of the organizational structure and data topology and user-defined rules and semantics, instances of the container are selectively populating for tenants who have subscribed to the content package. The populated instances of the content package are sent to the subscribed users based on distribution data flows that are identified based at least in part on the analysis of the topological relationships and hierarchical structures.

    ORCHESTRATOR FOR MACHINE LEARNING PIPELINE
    5.
    发明申请

    公开(公告)号:US20200272947A1

    公开(公告)日:2020-08-27

    申请号:US16284291

    申请日:2019-02-25

    Applicant: SAP SE

    Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

    FAILURE MODE ANALYTICS
    6.
    发明申请

    公开(公告)号:US20200272112A1

    公开(公告)日:2020-08-27

    申请号:US16284369

    申请日:2019-02-25

    Applicant: SAP SE

    Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.

    Orchestrator for machine learning pipeline

    公开(公告)号:US11586986B2

    公开(公告)日:2023-02-21

    申请号:US16284291

    申请日:2019-02-25

    Applicant: SAP SE

    Abstract: Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving an identification of a machine learning model, executing a machine learning pipeline comprising a plurality of services which train the machine learning model via at least one of an unsupervised learning process and a supervised learning process, the machine learning pipeline being controlled by an orchestration module that triggers ordered execution of the services, and storing the trained machine learning model output from the machine learning pipeline in a database associated with the machine learning pipeline.

    Microservice generation system
    9.
    发明授权

    公开(公告)号:US11444852B2

    公开(公告)日:2022-09-13

    申请号:US17335262

    申请日:2021-06-01

    Applicant: SAP SE

    Abstract: Systems and methods are provided for receiving, from a computing device, a selection of a template for a custom microservice and configuration parameters for the custom microservice, generating the template for the custom microservice using the configuration parameters, the template for the custom microservice comprising defined interfaces for accessing core microservices, defined integration points for integration with a system providing the core microservices, and stubs for custom components for the custom microservice, and providing the template for the custom microservice to the computing device, wherein custom components for the custom microservice are added to the template via the computing device using the stubs for the custom components. The systems and methods further provide for registering the custom microservice to be exposed to and accessed by a tenant with authorization to access the custom microservice along with the core microservices.

    Microservice Generation System
    10.
    发明申请

    公开(公告)号:US20210288891A1

    公开(公告)日:2021-09-16

    申请号:US17335262

    申请日:2021-06-01

    Applicant: SAP SE

    Abstract: Systems and methods are provided for receiving, from a computing device, a selection of a template for a custom microservice and configuration parameters for the custom microservice, generating the template for the custom microservice using the configuration parameters, the template for the custom microservice comprising defined interfaces for accessing core microservices, defined integration points for integration with a system providing the core microservices, and stubs for custom components for the custom microservice, and providing the template for the custom microservice to the computing device, wherein custom components for the custom microservice are added to the template via the computing device using the stubs for the custom components. The systems and methods further provide for registering the custom microservice to be exposed to and accessed by a tenant with authorization to access the custom microservice along with the core microservices.

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