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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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公开(公告)号：US20200159203A1
公开(公告)日：2020-05-21
申请号：US16276876
申请日：2019-02-15
Applicant: SAP SE
Inventor： Rashmi Shetty B , Leonard Brzezinski , Lauren McMullen , Harpreet Singh , Karthik Mohan Mokashi , Simon Lee , Lukas Carullo , Martin Weiss , Patrick Brose , Anubhav Bhatia
IPC: G05B23/02
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.
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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
Inventor： Rashmi Shetty B , Leonard Brzezinski , Lauren McMullen , Harpreet Singh , Karthik Mohan Mokashi , Simon Lee , Lukas Carullo , Martin Weiss , Patrick Brose , Anubhav Bhatia
IPC: G05B23/02
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.
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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
Inventor： Daniel Huber , Srikanth Grandhe , Emese Borbala Baliko , Sri Vidah A N , Yogesh Beria , Anubhav Bhatia , Lukas Carullo , Martin Weiss , Patrick Brose , Markus Krabel
IPC: H04L29/08
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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Patent Agency Ranking
公开(公告)号：US20170178025A1
公开(公告)日：2017-06-22
申请号：US14978963
申请日：2015-12-22
Applicant: SAP SE
Inventor： Susan Marie Thomas , Hartwig Seifert , Harish Mehta , Thomas Kunz , Omar Alexander Al-Hujaj , Eugen Pritzkau , Lukas Carullo , Rita Merkel , Marco Rodeck
CPC classification number: G06N20/00 , G06F16/2465 , G06F21/552 , G06N5/022 , G06N5/025 , H04L63/1408 , H04L63/1425
Abstract: A log file including a plurality of log entries is accessed. Each log entry of the plurality of log entries is analyzed to identify components of each log entry. The components of the particular log entry indicate an event. The event is associated with roles. Each role is associated with one or more attributes. Semantic meaning of the event associated with the particular log entry is determined. A mapping is performed by applying contextual information from one or more semantic meaning models stored in a knowledgebase to the identified components of each log entry to derive semantic meaning for the particular log entry. The derived semantic meaning is modeled for the particular log entry. The modeled semantic meaning is recorded in the knowledgebase as a new semantic meaning model for future use.
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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：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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