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公开(公告)号：US12106199B2
公开(公告)日：2024-10-01
申请号：US18304284
申请日：2023-04-20
Applicant: Salesforce, Inc.
Inventor： Rakesh Ganapathi Karanth , Arun Kumar Jagota , Kaushal Bansal , Amrita Dasgupta
Abstract: An online system performs predictions for real-time tasks and near real-time tasks based on available network bandwidth. A client device receives a regression based machine learning model. Responsive to receiving a task, the client device determines an available network bandwidth for the client device. If the available network bandwidth is below a threshold, the client device uses the regression based machine learning model to perform the task. If the client device determines that the network bandwidth is above the threshold, the client device extracts features of the task, serializes the extracted features, and transmits the serialized features to an online system, causing the online system to use a different machine learning model to perform the task based on the serialized features.
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公开(公告)号：US20230259831A1
公开(公告)日：2023-08-17
申请号：US18304284
申请日：2023-04-20
Applicant: salesforce, Inc.
Inventor： Rakesh Ganapathi Karanth , Arun Kumar Jagota , Kaushal Bansal , Amrita Dasgupta
Abstract: An online system performs predictions for real-time tasks and near real-time tasks based on available network bandwidth. A client device receives a regression based machine learning model. Responsive to receiving a task, the client device determines an available network bandwidth for the client device. If the available network bandwidth is below a threshold, the client device uses the regression based machine learning model to perform the task. If the client device determines that the network bandwidth is above the threshold, the client device extracts features of the task, serializes the extracted features, and transmits the serialized features to an online system, causing the online system to use a different machine learning model to perform the task based on the serialized features.
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