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公开(公告)号：US11947599B1
公开(公告)日：2024-04-02
申请号：US17974853
申请日：2022-10-27
Applicant: SAP SE
Inventor： Philipp Knuesel
IPC: G06F16/903 , G06F16/25 , G06Q10/0631
CPC classification number: G06F16/90335 , G06F16/252 , G06Q10/0631
Abstract: The present disclosure involves systems, software, and computer implemented methods for data confidentiality-preserving machine learning on remote datasets. An example method includes receiving connection information for connecting to a remote customer database and storing the connection information in a machine learning runtime. Workload schedule information for allowable time windows for machine learning pipeline execution on remote customer data of the customer is received from the customer. A determination is made that an execution queue includes a machine learning pipeline during an allowed time window. The connection information is used to connect to the remote customer database during the allowed time window. Execution is triggered by the machine learning runtime of the machine learning pipeline on the remote customer database. Aggregate evaluation data corresponding to the execution of the machine learning pipeline on the remote customer database is received and provided to a user.
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2.发明公开公开(公告)号：US20240095397A1
公开(公告)日：2024-03-21
申请号：US17974892
申请日：2022-10-27
Applicant: SAP SE
Inventor： Philipp Knuesel
CPC classification number: G06F21/6245 , G06N20/00
Abstract: The present disclosure involves systems, software, and computer implemented methods for evaluating machine learning on remote datasets using confidentiality-preserving evaluation data. In response to determining that data of the remote customer dataset is of sufficient quality and quantity, feature data corresponding to a machine learning pipeline is generated. The remote customer dataset into one or more data partitions and for each partition, one or more baseline models and one or more machine learning models are trained using a machine learning library included in the remote customer database. Aggregate evaluation data is generated for each baseline model and each machine learning model that includes model debrief data and customer data statistics. In response to determining that the customer has enabled sharing of the aggregate evaluation data with a software provider who provided the remote customer database, the aggregate evaluation data is provided to the software provider.
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3.发明授权公开(公告)号：US12197507B2
公开(公告)日：2025-01-14
申请号：US17974892
申请日：2022-10-27
Applicant: SAP SE
Inventor： Philipp Knuesel
IPC: G06F21/62 , G06F16/25 , G06F16/903 , G06N20/00 , G06Q10/0631
Abstract: The present disclosure involves systems, software, and computer implemented methods for evaluating machine learning on remote datasets using confidentiality-preserving evaluation data. In response to determining that data of the remote customer dataset is of sufficient quality and quantity, feature data corresponding to a machine learning pipeline is generated. The remote customer dataset into one or more data partitions and for each partition, one or more baseline models and one or more machine learning models are trained using a machine learning library included in the remote customer database. Aggregate evaluation data is generated for each baseline model and each machine learning model that includes model debrief data and customer data statistics. In response to determining that the customer has enabled sharing of the aggregate evaluation data with a software provider who provided the remote customer database, the aggregate evaluation data is provided to the software provider.
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Patent Agency Ranking
公开(公告)号：US20240095282A1
公开(公告)日：2024-03-21
申请号：US17974853
申请日：2022-10-27
Applicant: SAP SE
Inventor： Philipp Knuesel
IPC: G06F16/903 , G06Q10/06
CPC classification number: G06F16/90335 , G06Q10/0631
Abstract: The present disclosure involves systems, software, and computer implemented methods for data confidentiality-preserving machine learning on remote datasets. An example method includes receiving connection information for connecting to a remote customer database and storing the connection information in a machine learning runtime. Workload schedule information for allowable time windows for machine learning pipeline execution on remote customer data of the customer is received from the customer. A determination is made that an execution queue includes a machine learning pipeline during an allowed time window. The connection information is used to connect to the remote customer database during the allowed time window. Execution is triggered by the machine learning runtime of the machine learning pipeline on the remote customer database. Aggregate evaluation data corresponding to the execution of the machine learning pipeline on the remote customer database is received and provided to a user.
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公开(公告)号：US20230091954A1
公开(公告)日：2023-03-23
申请号：US17477807
申请日：2021-09-17
Applicant: SAP SE
Inventor： Roman Rommel , Philipp Knuesel , Janick Frasch , Santo Bianchino
IPC: H04L12/911 , H04L29/06 , G06F9/48 , H04L12/24
Abstract: Computer-readable media, methods, and systems are disclosed for scheduling a start time and a shutdown time of one or more online resources associated with a multi-cloud resource scheduler. A request from a first user is received to access a multi-cloud resource scheduler associated with one or more online resources. Responsive to the request from the first user, credentials of the first user are validated prior to providing access to the multi-cloud resource scheduler. Based upon validating the credentials of the first user, access to the multi-cloud resource scheduler is provided. Instructions are received from the first user to schedule a start time and a shutdown time of at least one online cloud resource connected to the multi-cloud resource scheduler. An availability of the at least one online cloud resource is established for access by a second user based on the instructions.
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公开(公告)号：US20240297853A1
公开(公告)日：2024-09-05
申请号：US18646316
申请日：2024-04-25
Applicant: SAP SE
Inventor： Roman Rommel , Philipp Knuesel , Janick Frasch , Santo Bianchino
CPC classification number: H04L47/827 , G06F9/4887 , H04L41/22 , H04L47/826 , H04L63/108
Abstract: Computer-readable media, methods, and systems are disclosed for scheduling a start time and a shutdown time of one or more online resources associated with a multi-cloud resource scheduler. A request from a first user is received to access a multi-cloud resource scheduler associated with one or more online resources. Responsive to the request from the first user, credentials of the first user are validated prior to providing access to the multi-cloud resource scheduler. Based upon validating the credentials of the first user, access to the multi-cloud resource scheduler is provided. Instructions are received from the first user to schedule a start time and a shutdown time of at least one online cloud resource connected to the multi-cloud resource scheduler. An availability of the at least one online cloud resource is established for access by a second user based on the instructions.
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公开(公告)号：US12003428B2
公开(公告)日：2024-06-04
申请号：US17477807
申请日：2021-09-17
Applicant: SAP SE
Inventor： Roman Rommel , Philipp Knuesel , Janick Frasch , Santo Bianchino
CPC classification number: H04L47/827 , G06F9/4887 , H04L41/22 , H04L47/826 , H04L63/108
Abstract: Computer-readable media, methods, and systems are disclosed for scheduling a start time and a shutdown time of one or more online resources associated with a multi-cloud resource scheduler. A request from a first user is received to access a multi-cloud resource scheduler associated with one or more online resources. Responsive to the request from the first user, credentials of the first user are validated prior to providing access to the multi-cloud resource scheduler. Based upon validating the credentials of the first user, access to the multi-cloud resource scheduler is provided. Instructions are received from the first user to schedule a start time and a shutdown time of at least one online cloud resource connected to the multi-cloud resource scheduler. An availability of the at least one online cloud resource is established for access by a second user based on the instructions.
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公开(公告)号：US20220405651A1
公开(公告)日：2022-12-22
申请号：US17835506
申请日：2022-06-08
Applicant: SAP SE
Inventor： Philipp Knuesel , Andre Sres , Mirko Hin , Roman Rommel , Janick Frasch , Santo Bianchino , Dominik Heere
Abstract: Some embodiments are directed to a federated machine learning, including the inference and training. Inference may be done by applying multiple machine learnable models to a mapped record. The mapped record may be obtained by applying a mapping rule to a local record. The mapping rule may generalize or extend data features in the local record.
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