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公开(公告)号：US10762513B2
公开(公告)日：2020-09-01
申请号：US15369354
申请日：2016-12-05
Applicant: SAP SE
Inventor： Alan Southall , Anubhav Bhatia , Hermann Lueckhoff , Olaf Meincke , Reghu Ram Thanumalayan , Thomas Hettel
Abstract: Methods, systems, and computer-readable storage media for providing an insight provider including a logic component and a configuration component, the logic component including a domain-specific model, the configuration component including one or more parameter values for processing data using the domain-specific model, receiving a set of assets including data indicative of one or more assets, retrieving asset data associated with at least one asset of the first set of assets, the asset data including OT data and IT data, the OT data being provided from one or more networked devices, the IT data being provided from one or more enterprise systems, and processing the OT data and the IT data using the domain-specific model of the logic component to provide a result set, the result set including one or more of a second set of assets and enriched data.
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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
Inventor： Lukas Carullo , Patrick Brose , Kun Bao , Anubhav Bhatia , Leonard Brzezinski , Lauren McMullen , Simon Lee
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.
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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
Inventor： Daniel Huber , Srikanth Grandhe , Emese Borbala Baliko , Sri Vidah A N , Yogesh Beria , Anubhav Bhatia , Lukas Carullo , Martin Weiss , Patrick Brose , Markus Krabel
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.
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公开(公告)号：US20210342409A1
公开(公告)日：2021-11-04
申请号：US16864816
申请日：2020-05-01
Applicant: SAP SE
Inventor： Anubhav Bhatia , Martin Weiss , Oliver Mainka , Ankit Shivhare , Rajarshi Ghosh , Lauren McMullen
IPC: G06F16/9535 , G06F16/901 , G06F16/9538
Abstract: A global filter allows data filtering using attributes across multiple Analysis Tools (ATs), by broadcasting semantic filter context objects. Upon selecting object attribute values, the filter context object is created with attribute names and values. A processing engine resolves the filter context object to a data object, and then subsequently to target data. A lateral filter finds related entities in a relational database, without having to maintain and/or duplicate all of the data into a graph database. The processing engine resolves lateral filters using an entity graph path calculation conducted in conjunction with the generation of a bootstrapped graph structure. That graph structure is constructed (bootstrapped) utilizing available database schematic information—e.g., pre-calculated (key) relations and metadata read from the relational database. From that information, relationships in the bootstrapped graph structure are determined. Possible paths between entities are used to generate an optimized SQL query to reach target data.
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公开(公告)号：US20210342336A1
公开(公告)日：2021-11-04
申请号：US16864785
申请日：2020-05-01
Applicant: SAP SE
Inventor： Anubhav Bhatia , Martin Weiss , Oliver Mainka , Ankit Shivhare , Rajarshi Ghosh , Lauren McMullen
IPC: G06F16/248 , G06F16/21 , G06F16/901 , G06F16/28
Abstract: A global filter allows data filtering using attributes across multiple Analysis Tools (ATs), by broadcasting semantic filter context objects. Upon selecting object attribute values, the filter context object is created with attribute names and values. A processing engine resolves the filter context object to a data object, and then subsequently to target data. A lateral filter finds related entities in a relational database, without having to maintain and/or duplicate all of the data into a graph database. The processing engine resolves lateral filters using an entity graph path calculation conducted in conjunction with the generation of a bootstrapped graph structure. That graph structure is constructed (bootstrapped) utilizing available database schematic information—e.g., pre-calculated (key) relations and metadata read from the relational database. From that information, relationships in the bootstrapped graph structure are determined. Possible paths between entities are used to generate an optimized SQL query to reach target data.
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Patent Agency Ranking
公开(公告)号：US20200349589A1
公开(公告)日：2020-11-05
申请号：US16935324
申请日：2020-07-22
Applicant: SAP SE
Inventor： Alan Southall , Anubhav Bhatia , Hermann Lueckhoff , Olaf Meincke , Reghu Ram Thanumalayan , Thomas Hettel
Abstract: Methods, systems, and computer-readable storage media for providing an insight provider including a logic component and a configuration component, the logic component including a domain-specific model, the configuration component including one or more parameter values for processing data using the domain-specific model, receiving a set of assets including data indicative of one or more assets, retrieving asset data associated with at least one asset of the first set of assets, the asset data including OT data and IT data, the OT data being provided from one or more networked devices, the IT data being provided from one or more enterprise systems, and processing the OT data and the IT data using the domain-specific model of the logic component to provide a result set, the result set including one or more of a second set of assets and enriched data.
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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
Inventor： Lukas Carullo , Patrick Brose , Kun Bao , Anubhav Bhatia , Leonard Brzezinski , Lauren McMullen , Simon Lee
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.
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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
Inventor： Lukas Carullo , Patrick Brose , Kun Bao , Anubhav Bhatia , Rashmi Shetty B , Leonard Brzezinski , Lauren McMullen , Harpreet Singh , Karthik Mohan Mokashi , Simon Lee
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.
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公开(公告)号：US11645247B2
公开(公告)日：2023-05-09
申请号：US17000057
申请日：2020-08-21
Applicant: SAP SE
Inventor： Anubhav Bhatia , Patrick Brose , Lukas Carullo , Martin Weiss , Leonard Brzezinski
IPC: G06F16/21 , G06F16/22 , G06F16/2455 , G06F16/25 , G06F16/178
CPC classification number: G06F16/213 , G06F16/1794 , G06F16/2282 , G06F16/2456 , G06F16/258
Abstract: Techniques and solutions are provided for integrating master data from multiple applications. Master data from multiple applications can be integrated for use in processing data associated with internet of things (IOT) devices, such as by joining master data with timeseries data (including aggregated values). Integrating master data from multiple applications can include converting master data from a schema used by an application into an analytics schema. New or updated master data can be indicated in a message sent by an application. In processing the message, additional master data, or data used to determine how master data should be processed, can be retrieved.
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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
Inventor： Lukas Carullo , Patrick Brose , Kun Bao , Anubhav Bhatia , Leonard Brzezinski , Lauren McMullen , Simon Lee
IPC: G06F15/173 , G06N20/20 , G06F16/35
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.
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