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公开(公告)号：US12033094B2
公开(公告)日：2024-07-09
申请号：US16574064
申请日：2019-09-17
发明人： Jonathan F. Brunn , Rachael Marie Huston Dickens , Rui Zhang , Ami Herrman Dewar , Heiko H. Ludwig
IPC分类号： G06Q10/10 , G06N20/00 , G06Q10/0631 , G06Q10/1093
CPC分类号： G06Q10/063112 , G06N20/00 , G06Q10/1095
摘要： Provided are a computer program product, system, and method for generation of tasks and retraining machine learning modules to generate tasks based on feedback for the generated tasks. A machine learning module processes an input text message sent in the communication channel to output task information including an intended action and a set of associated users. A task message is generated including the output task information of a task to perform. The task message is sent to a user interface panel in a user computer. Feedback is received from the user computer on the output task information in the task message. The machine learning module is retrained to output task information from the input text message based on the feedback to reinforce likelihood correct task information is outputted and reinforce lower likelihood incorrect task information is outputted.
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公开(公告)号：US20230017500A1
公开(公告)日：2023-01-19
申请号：US17373611
申请日：2021-07-12
发明人： Ali Anwar , Syed Amer Zawad , Yi Zhou , Nathalie Baracaldo Angel , Kamala Micaela Noelle Varma , Annie Abay , Ebube Chuba , Yuya Jeremy Ong , Heiko H. Ludwig
IPC分类号： G06N20/00
摘要： One embodiment of the invention provides a method for federated learning (FL) comprising training a machine learning (ML) model collaboratively by initiating a round of FL across data parties. Each data party is allocated tokens to utilize during the training. The method further comprises maintaining, for each data party, a corresponding data usage profile indicative of an amount of data the data party consumed during the training and a corresponding participation profile indicative of an amount of data the data party provided during the training. The method further comprises selectively allocating new tokens to the data parties based on each participation profile maintained, selectively allocating additional new tokens to the data parties based on each data usage profile maintained, and reimbursing one or more tokens utilized during the training to the data parties based on one or more measurements of accuracy of the ML model.
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公开(公告)号：US11165664B2
公开(公告)日：2021-11-02
申请号：US16541648
申请日：2019-08-15
IPC分类号： H04L12/24
摘要： A method, computer system, and computer program product are provided. A set of classifiers are applied to metric definitions of an electronic contract between computing devices of a service provider and a service consumer. Each classifier includes a selector pattern and a set of variable declarations. Performance data of the service provider computing device are filtered according to the selector pattern of the each classifier. One or more algebraic expressions of the metric definitions are evaluated in accordance with the each classifier and the filtered performance data to determine compliance of the service provider computing device with the electronic contract. Evaluation results indicating whether the service provider computing device is in compliance with the electronic contract are captured and reported.
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公开(公告)号：US20210304062A1
公开(公告)日：2021-09-30
申请号：US16832809
申请日：2020-03-27
IPC分类号： G06N20/00
摘要： One embodiment provides a method for federated learning across a plurality of data parties, comprising assigning each data party with a corresponding namespace in an object store, assigning a shared namespace in the object store, and triggering a round of federated learning by issuing a customized learning request to at least one data party. Each customized learning request issued to a data party triggers the data party to locally train a model based on training data owned by the data party and model parameters stored in the shared namespace, and upload a local model resulting from the local training to a corresponding namespace in the object store the data party is assigned with. The method further comprises retrieving, from the object store, local models uploaded to the object store during the round of federated learning, and aggregating the local models to obtain a shared model.
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公开(公告)号：US10999160B2
公开(公告)日：2021-05-04
申请号：US16848692
申请日：2020-04-14
IPC分类号： H04L12/24
摘要： One embodiment provides a method for monitoring context-dependent quality of service in a shared computing environment that includes detecting, by a processor, a change in context. Context includes user context and external context, and user context comprises physical activity, mood, engagement levels and type of events. Prior assigned service classes are updated to updated service classes based on the change in context. Service level agreement (SLA) statistics for each assigned service class are aggregated and collected. Each assigned service class includes at least one SLA based on aggregate services received by individual users in that assigned service class, and aggregating SLA statistics is based on a statistical function.
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公开(公告)号：US10757058B2
公开(公告)日：2020-08-25
申请号：US15436365
申请日：2017-02-17
发明人： Nathalie Baracaldo Angel , Pawan R. Chowdhary , Heiko H. Ludwig , Robert J. Moore , Amir J. Safavi
IPC分类号： G06F15/173 , H04L12/58 , H04L29/06
摘要： One embodiment provides a method comprising intercepting an incoming communication for a user, and determining whether to classify the incoming communication as a suspicious incoming communication based on content of the incoming communication and a learned classification model or learned signatures. The method further comprises monitoring one or more outgoing communications from the user, and invoking an action relating to scam prevention in response to determining an outgoing communication from the user is linked to a suspicious incoming communication.
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公开(公告)号：US20180352025A1
公开(公告)日：2018-12-06
申请号：US15615151
申请日：2017-06-06
发明人： Obinna B. Anya , Heiko H. Ludwig , Nagapramod S. Mandagere , Mohamed Mohamed , Mu Qiao , Ramani R. Routray , Samir Tata
摘要： One embodiment provides a quality of service (QoS) monitoring framework for dynamically binding one or more customer applications to one or more microservices in a dynamic service environment, collecting compliance data and contextual data from the dynamic service environment and one or more hosting environments, and modifying a monitoring infrastructure for the one or more customer applications based on the compliance data and the contextual data.
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公开(公告)号：US20180262403A1
公开(公告)日：2018-09-13
申请号：US15452380
申请日：2017-03-07
IPC分类号： H04L12/24
摘要： One embodiment provides a method for monitoring context-dependent quality of service in a shared computing environment that includes detecting, by a processor, a change in context. Service classes are dynamically assigned to individual users and respective applications within shared computing environment customers based on the change in context. Service level agreement (SLA) statistics for each assigned service class are aggregated and collected.
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公开(公告)号：US11588621B2
公开(公告)日：2023-02-21
申请号：US16706328
申请日：2019-12-06
发明人： Nathalie Baracaldo Angel , Runhua Xu , Yi Zhou , Ali Anwar , Heiko H. Ludwig
IPC分类号： H04L9/08 , H04L9/30 , G06F16/903 , G06N20/00
摘要： Systems and techniques that facilitate universal and efficient privacy-preserving vertical federated learning are provided. In various embodiments, a key distribution component can distribute respective feature-dimension public keys and respective sample-dimension public keys to respective participants in a vertical federated learning framework governed by a coordinator, wherein the respective participants can send to the coordinator respective local model updates encrypted by the respective feature-dimension public keys and respective local datasets encrypted by the respective sample-dimension public keys. In various embodiments, an inference prevention component can verify a participant-related weight vector generated by the coordinator, based on which the key distribution component can distribute to the coordinator a functional feature-dimension secret key that can aggregate the encrypted respective local model updates into a sample-related weight vector. In various embodiments, the inference prevention component can verify the sample-related weight vector, based on which the key distribution component can distribute to the coordinator a functional sample-dimension secret key that can aggregate the encrypted respective local datasets into an update value for a global model.
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公开(公告)号：US11487963B2
公开(公告)日：2022-11-01
申请号：US16571321
申请日：2019-09-16
摘要： Embodiments relate to a system, program product, and method for automatically determining which activation data points in a neural model have been poisoned to erroneously indicate association with a particular label or labels. A neural network is trained network using potentially poisoned training data. Each of the training data points is classified using the network to retain the activations of the last hidden layer, and segment those activations by the label of corresponding training data. Clustering is applied to the retained activations of each segment, and a cluster assessment is conducted for each cluster associated with each label to distinguish clusters with potentially poisoned activations from clusters populated with legitimate activations. The assessment includes analyzing, for each cluster, a distance of a median of the activations therein to medians of the activations in the labels.
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