Invention Application
- Patent Title: MACHINE LEARNING FOR PREDICTIVE OPTMIZATION
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Application No.: US17453987Application Date: 2021-11-08
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Publication No.: US20220147897A1Publication Date: 2022-05-12
- Inventor: Elad Liebman , Jeremy Ritter
- Applicant: SparkCognition, Inc.
- Applicant Address: US TX Austin
- Assignee: SparkCognition, Inc.
- Current Assignee: SparkCognition, Inc.
- Current Assignee Address: US TX Austin
- Main IPC: G06Q10/06
- IPC: G06Q10/06 ; G06Q50/30

Abstract:
A method includes obtaining historical data including sensor data from one or more sensors associated with a device and contextual data indicative of one or more conditions external to the device and independent of operation of the device. The method also includes providing at least a portion of the historical data as input to one or more machine-learning-based projection models to generate projection data associated with a future condition of the device. The method further includes providing input data to one or more machine-learning-based optimization models to determine one or more operational parameters that are expected to improve an operational metric associated with one or more devices. The one or more devices include the device, and the input data is based, at least in part, on the historical data and the projection data.
Information query