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Patent Agency Ranking
公开(公告)号：US20220058069A1
公开(公告)日：2022-02-24
申请号：US17000043
申请日：2020-08-21
Applicant: SAP SE
Inventor： Anubhav Bhatia , Patrick Brose , Lukas Carullo , Martin Weiss , Leonard Brzezinski
Abstract: Techniques and solutions are provided for processing data in conjunction with one or more hyperscale computing systems. An interface is provided for translating calls from an application into a format used by a hyperscale computing system. The calls can be to read data from, or write data to, a hyperscale computing system. In particular examples, data to be read or written is data from a plurality of IOT devices, where each IOT device has one or more hardware sensors. An interface can also be used to configure how the hyperscale computing system processes data, such as determining how IOT data is stored or how aggregates are generated from IOT data.
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公开(公告)号：US12223408B2
公开(公告)日：2025-02-11
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
IPC: G06F15/173 , G06N20/20
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：US12055902B2
公开(公告)日：2024-08-06
申请号：US18096080
申请日：2023-01-12
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
CPC classification number: G05B13/0265 , G06F9/542 , G06N7/01 , G06N20/20
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：US20230168639A1
公开(公告)日：2023-06-01
申请号：US18096080
申请日：2023-01-12
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
CPC classification number: G05B13/0265 , G06F9/542 , G06N20/20 , G06N7/01
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：US20220058177A1
公开(公告)日：2022-02-24
申请号：US17000017
申请日：2020-08-21
Applicant: SAP SE
Inventor： Anubhav Bhatia , Patrick Brose , Lukas Carullo , Martin Weiss , Leonard Brzezinski
Abstract: Techniques for processing sensor data are provided. Sensor data, such as individual messages or data points from devices having one or more hardware sensors, can be annotated with one or more metadata elements to facilitate sensor data processing. An annotation rule for sensor data can be determined and sensor data annotated according to the annotation rule. Sensor data can be written to a relational database table, where the table has a schema that provides columns for storing data for particular indicators of an indicator group having a plurality of indicators.
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