Determining Preferential Device Behavior

• US 20140136451A1
• Filed: 03/01/2013
• Published: 05/15/2014
• Est. Priority Date: 11/09/2012
• Status: Abandoned Application

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Marti, Lukas M., Huang, Ronald Keryuan, Ma, Shannon M.
• Application Number:
  US13/783,195
• Publication Number:
  US 20140136451A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

Systems, methods and computer program products are disclosed for machine learning to determine preferential device behavior. In some implementations, a server receives inputs, including attributes from a client device, crowd-sourced data from a number of other devices and a priori knowledge. The server includes a concept engine that applies machine-learning process to the inputs. The output of the machine learning process is transported to the client device. At the client device, a client engine associates attributes observed at the device to the machine learning output to determine a user profile. Applications may access the user profile to determine preferential device behavior, such as provide targeted information to the user or take action on the device that is personalized to the user of the device.

Claims

1. A method comprising:

associating observed user behavior with output of a machine learning process, the output derived from attributes observed at the device and aggregated attributes from a number of other devices;

determining a preferential device behavior based on results of the associating; and

adapting the device or initiating an action on the device based on the preferential device behavior,

wherein the method is performed by one or more hardware processors.

2. The method of claim 1, where the machine learning process is implemented on a server computer coupled to the device.

3. The method of claim 1, where the machine learning process implements a decision tree that can be dynamically programmed.

4. The method of claim 1, where the machine learning process is a supervised learning process.

5. The method of claim 1, where the machine learning process is an unsupervised learning process.

6. The method of claim 1, wherein the output is derived from attributes observed at the device, aggregated attributes from a number of other devices and prior knowledge.

7. A method comprising:

obtaining attributes observed at a device;

obtaining attributes from a number of other devices;

processing the attributes using a machine learning process; and

providing output of the machine learning process to the device,

wherein the method is performed by one or more hardware processors.

8. A system comprising:

one or more processors;

memory coupled to the one or more processors and configured to store instructions, which, when executed by the one or more processors, cause the one or more processors to perform operations comprising;

associating observed user behavior with output of a machine learning process, the output derived from attributes observed at the device and aggregated attributes from a number of other devices;

determining a preferential device behavior based on results of the associating; and

adapting the device or initiating an action on the device based on the preferential device behavior,

wherein the method is performed by one or more hardware processors.

9. The system of claim 8, where the machine learning process is implemented on a server computer coupled to the device.

10. The system of claim 8, where the machine learning process implements a decision tree that can be dynamically programmed.

11. The system of claim 8, where the machine learning process is a supervised learning process.

12. The system of claim 8, where the machine learning process is an unsupervised learning process.

13. The system of claim 8, wherein the output is derived from attributes observed at the device, aggregated attributes from a number of other devices and prior knowledge.

14. A system comprising:

obtaining attributes observed at a device;

obtaining attributes from a number of other devices;

processing the attributes using a machine learning process; and

providing output of the machine learning process to the device,

wherein the method is performed by one or more hardware processors.