Configuring an electronic device using artificial intelligence

Patent metadata

Field	Value
Publication number	US-11494200-B2
Application number	US-201815994442-A
Country	US
Kind code	B2
Filing date	May 31, 2018
Priority date	May 2, 2018
Publication date	Nov 8, 2022
Grant date	Nov 8, 2022

Abstract

The devices, systems, and methods described herein enable automatically configuring an electronic device using artificial intelligence (AI). The devices, systems, and methods enable accessing telemetry data representing device usage data, inputting the accessed telemetry data into machine learning models that are matched to device metadata, and determining notifications to publish to components of the electronic device. The notifications represent events predicted to occur on the electronic device. The notifications are published to the components of the electronic device such that the electronic device is configured according to the published notifications. The determined notifications enable the identification of optimal settings for the electronic device based on the usage pattern of the device and enable components of the electronic device to preemptively take action on events which are predicted to occur in the future.

First claim

What is claimed is:

1. An electronic device comprising:
at least one processor;
at least one memory storing telemetry data gathered from the electronic device, the telemetry data at least representing device usage data, the memory further storing device metadata;
a client service having computer-executable instructions that, in response to execution by the at least one processor, cause the at least one processor to:
send, to a cloud service, a request for at least one machine learning model based on the device metadata of the electronic device,
receive, from the cloud service, the at least one machine learning model created for the electronic device based on the device metadata,
update the received at least one machine learning model at the electronic device with information that is associated with at least one of the electronic device or a user of the electronic device,
input the telemetry data from the memory into the updated at least one machine learning model, and
determine, based on the telemetry data input into the updated at least one machine learning model, a notification to publish to a component of the electronic device that is customized to at least one of the user or the electronic device; and

a client broker having computer-executable instructions that, in response to execution by the at least one processor, cause the at least one processor to publish the determined notification to the component, the notification representing an event predicted to occur on the electronic device,
wherein the component has computer-executable instructions that, in response to execution by the at least one processor, cause the at least one processor to configure the electronic device based at least on the published notification.

2. The electronic device of claim 1 , wherein the component includes an operating system of the electronic device and the client service has computer-executable instructions that, in response to execution by the processor, determine a notification relating to at least one setting of the operating system, and wherein configuring, by the component, includes configuring the at least one setting of the operating system based at least on the published notification.

3. The electronic device of claim 1 , wherein the component includes at least one of an application of the electronic device or hardware of the electronic device, and the client service has computer-executable instructions that, in response to execution by the processor, determine a notification relating to the at least one of the application or the hardware, and wherein configuring, by the component, includes configuring at least one of the application or the hardware based at least on the published notification.

4. The electronic device of claim 1 , wherein the information that is associated with at least one of the electronic device or the user of the electronic device and used to update the received at least one machine learning model is unknown to the cloud service.

5. The electronic device of claim 1 , wherein the component includes hardware of the electronic device and the client service has computer-executable instructions that, in response to execution by the processor, determine a notification relating to the hardware, the notification representing a predicted usage of the hardware based at least on the telemetry data, and wherein configuring, by the component, includes configuring the hardware with at least one application relating to the predicted usage of the hardware.

6. The electronic device of claim 1 , further comprising a universal telemetry client that has computer-executable instructions that, in response to execution by the processor, upload the telemetry data from the memory to the cloud service for updating the at least one machine learning model at the cloud service based on at least one of the telemetry data of the electronic device or telemetry data from another electronic device.

7. The electronic device of claim 1 , wherein the telemetry data input by the client service from the memory into the at least one machine learning model includes real-time telemetry data from the electronic device.

8. The electronic device of claim 1 , wherein the client service has computer-executable instructions that, in response to execution by the processor, reject the determined notification based on information that is associated with at least one of the user or the electronic device.

9. The electronic device of claim 1 , wherein the information that is associated with at least one of the electronic device or the user of the electronic device and used to update the at least one machine learning model comprises at least one of custom hardware of the electronic device, a proprietary chip, hardware of the electronic device, software of the electronic device, whether the electronic device is a work device, or whether the electronic device is a personal device.

10. The electronic device of claim 1 , wherein the client service has computer-executable instructions that, in response to execution by the at least one processor, request the at least one machine learning model from the cloud service based on the device metadata, the at least one machine learning model being an open neural network exchange (ONNX) model.

11. A method implemented on an electronic device, the method comprising:
sending, to a cloud service, a request for at least one machine learning model based on device metadata of the electronic device;
receiving, from the cloud service, the at least one machine learning model matched to the device metadata;
updating the received at least one machine learning model at the electronic device with information that is associated with at least one of the electronic device or a user of the electronic device;
accessing telemetry data representing at least device usage data;
inputting the accessed telemetry data into the updated at least one machine learning model to determine a notification that is customized to at least one of the user or the electronic device to publish to a component of the electronic device;
publishing the determined notification to the component, the notification representing an event predicted to occur on the electronic device; and
wherein the component is configured to configure the electronic device based at least on the published notification.

12. The method of claim 11 , wherein inputting the accessed telemetry data into the updated at least one machine learning model includes determining a notification relating to at least one setting of an operating system of the electronic device, and wherein configuring, by the component, includes configuring the at least one setting of the operating system based at least on the published notification.

13. The device implemented method of claim 11 , wherein inputting the accessed telemetry data into the updated at least one machine learning model includes determining a notification relating to at least one of an application of the electronic device or hardware of the electronic device, and wherein configuring, by the component, includes configuring the at least one of the application or the hardware based at least on the published notification.

14. The method of claim 11 , wherein the information that is associated with at least one of the electronic device or the user of the electronic device and used to update the received at least one machine learning model is unknown to the cloud service.

15. The method of claim 11 , wherein inputting the accessed telemetry data into the updated at least one machine learning model includes determining a notification relating to ha

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Liang Chen
• Nitta Bryston M
• Lahiri Shayak
• Guajardo Adrian Francisco Teran

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06F9/4451 Primary

  User profiles; Roaming · CPC title

• G06N20/20 Primary

  Ensemble learning · CPC title

• H04W4/02

  Services making use of location information · CPC title