Native machine learning service for user adaptation on a mobile platform

• US 8,429,103 B1
• Filed: 08/02/2012
• Issued: 04/23/2013
• Est. Priority Date: 06/22/2012
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Aradhye, Hrishikesh, Hua, Wei, Lin, Ruei-sung
• Primary Examiner(s): Gaffin, Jeffrey A
• Assistant Examiner(s): OLUDE AFOLABI, OLATOYOSI
• Application Number:
  US13/565,508
• Time in Patent Office: 264 Days
• Field of Search: 706/11, 706/12, 706/60
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04M 1/2749 Automatic dialling or redialling systems, e.g. on off-hook or redial on busy
• CPC Class Codes: H04M 1/6008 in the transmitter circuit
• CPC Class Codes: H04M 1/72448 with means for adapting the functionality of the device according to specific conditions
• CPC Class Codes: H04M 1/72451 according to schedules, e.g. using calendar applications
• CPC Class Codes: H04M 1/72454 according to context-related or environment-related conditions
• CPC Class Codes: H04M 1/72457 according to geographic location

Abstract

Disclosed are apparatus and methods for providing machine-learning services. A machine-learning service executing on a mobile platform can receive data related to a plurality of features. In some cases, the received data can include data related to features received from an application and data related to features received from the mobile platform. The machine-learning service can determine at least one feature based on the received data. The machine-learning service can generate an output by performing a machine-learning operation on the at least one feature. The machine-learning operation can be selected from among an operation of ranking the at least one feature, an operation of classifying the at least one feature, an operation of predicting the at least one feature, and an operation of clustering the at least one feature. The machine-learning service can send the output.

Claims

1. A method, comprising:

receiving data related to a plurality of features by a machine-learning service executing on a mobile platform, wherein the received data comprises a called party of a telephone call to be originated by the mobile platform;

determining at least one feature in the plurality of features based on the received data using the machine-learning service;

generating an output by the machine-learning service performing a machine-learning operation on the at least one feature of the plurality of features, wherein the machine-learning operation is selected from among;

an operation of ranking the at least one feature, an operation of classifying the at least one feature, an operation of predicting the at least one feature, and an operation of clustering the at least one feature, wherein the output comprises a prediction of a volume setting and/or a mute setting of the mobile platform; and

sending the output from the machine-learning service.

2. The method of claim 1, further comprising:

storing the at least one feature and the output for use by the machine-learning service;

receiving second data related to a second plurality of features at the machine-learning service;

determining a second feature in the second plurality of features based on the second data;

generating a second output by the machine-learning service performing the machine-learning operation on the second feature and the stored at least one feature and the output; and

sending the second output from the machine-learning service.

3. The method of claim 1, wherein generating the output by the machine-learning service comprises selecting a machine-learning algorithm to perform the machine-learning operation.

4. The method of claim 3, wherein selecting the machine-learning algorithm to perform the machine-learning operation comprises the machine-learning service selecting the machine-learning algorithm.

5. The method of claim 3, wherein selecting the machine-learning algorithm to perform the machine-learning operation comprises receiving a selection related to the machine-learning algorithm from an application executing on the mobile platform, and wherein the application differs from the machine-learning service.

6. The method of claim 5, wherein receiving a selection related to the machine-learning algorithm from the application comprises receiving a selection related to the machine-learning algorithm from the application via an Application Programming Interface (API) of the machine-learning service.

7. The method of claim 1, wherein the received data comprises data related to communication signals and locations, wherein the mobile platform is located at a first location, and wherein the output comprises an indication of whether to search for the communications signal at the first location.

8. The method of claim 1, wherein the received data comprises data related to durations of communication sessions, and wherein the output comprises a prediction of a duration of a new communication session.

9. The method of claim 1, wherein the received data further comprises data related to features of telephone calls originated by the mobile platform, and wherein the output further comprises the called party of the telephone call to be originated by the mobile platform.

10. The method of claim 1, wherein the received data further comprises data related to volume and mute settings of the mobile platform.

11. The method of claim 1, wherein the received data comprises data related to one or more images, and wherein the output comprises a name related to at least one image of the one or more images.

12. The method of claim 1, wherein the received data comprises data aggregated from a plurality of mobile platforms.

13. A method, comprising:

receiving feature-related data by a machine-learning service executing on a mobile platform, wherein the feature-related data comprises data related to a called party of a telephone call to be originated by the mobile platform and data related to a second plurality of features received from the mobile platform, and wherein the called party of the telephone call to be originated by the mobile platform and the second plurality of features differ;

determining, using the machine-learning service, at least one feature from among the called party of the telephone call to be originated by the mobile platform and the second plurality of features based on the feature-related data;

generating an output by the machine-learning service performing a machine-learning operation on the at least one feature, wherein the output comprises a prediction of a volume setting and/or a mute setting of the mobile platform; and

sending the output from the machine-learning service to the application.

14. The method of claim 13, wherein the first plurality of features comprises a feature selected from among a time, a location, a duration, a signal strength, a power level, a party of a telephone call, an e-mail address, a contact, and a calendar entry.

15. The method of claim 13, wherein the second plurality of features comprises a feature selected from among an image-related feature and a speech-related feature.

16. An article of manufacture including a non-transitory computer-readable storage medium having instructions stored thereon that, when executed by a processor, cause the processor to perform functions comprising:

receiving data related to a plurality of features, wherein the received data comprises data related to called parties of telephone calls originated by the mobile platform;

determining at least one feature in the plurality of features based on the received data;

generating an output by performing a machine-learning operation on the at least one feature of the plurality of features, wherein the machine-learning operation is selected from among;

an operation of ranking the at least one feature, an operation of classifying the at least one feature, an operation of predicting the at least one feature, and an operation of clustering the at least one feature, wherein the output comprises a prediction of a volume setting and/or a mute setting of the mobile platform; and

sending the output.

17. The article of manufacture of claim 16, further comprising:

storing the at least one feature and the output;

receiving second data related to a second plurality of features;

determining at least a second feature in the second plurality of features based on the second data;

generating a second output by performing the machine-learning operation on the at least the second feature and the stored at least one feature and the output; and

sending the second output.

18. The article of manufacture of claim 16, wherein the received data comprises data related to communication signals and locations, wherein the mobile platform is located at a first location, and wherein the output comprises an indication of whether to search for the communications signal at the first location.

19. The article of manufacture of claim 16, wherein the received data comprises data related to durations of communication sessions, and wherein the output comprises a prediction of a duration of a new communication session.

20. The article of manufacture of claim 16, wherein the output further comprises a called party of a telephone call to be originated by the mobile platform.

21. The article of manufacture of claim 16, wherein the received data further comprises data related to volume and mute settings of the mobile platform.

22. The article of manufacture of claim 16, wherein the received data comprises data related to one or more images, and wherein the output comprises a name related to at least one image of the one or more images.

23. A mobile platform, comprising:

a processor; and

a non-transitory computer-readable storage medium configured to store instructions that, when executed by the processor, cause the mobile platform to perform functions comprising;

receiving data related to a plurality of features wherein the received data comprises data related to called parties of telephone calls originated by the mobile platform,

determining at least one feature in the plurality of features based on the received data;

generating an output by performing a machine-learning operation on the at least one feature of the plurality of features, wherein the machine-learning operation is selected from among;

an operation of ranking the at least one feature, an operation of classifying the at least one feature, an operation of predicting the at least one feature, and an operation of clustering the at least one feature, wherein the output comprises a prediction of a volume setting and/or a mute setting of the mobile platform; and

sending the output.

24. The mobile platform of claim 23, wherein the functions further comprise:

storing the at least one feature and the output in the non-transitory computer-readable storage medium;

receiving second data related to a second plurality of features;

determining at least a second feature in the second plurality of features based on the second data;

generating a second output by the machine-learning service performing the machine-learning operation on the at least the second feature and the stored at least one feature and the output; and

sending the second output.

25. The mobile platform of claim 23, wherein generating the output by the machine-learning service comprises selecting a machine-learning algorithm to perform the machine-learning operation.

26. The mobile platform of claim 23, wherein the received data comprises data related to communication signals and locations, wherein the mobile platform is located at a first location, and wherein the output comprises an indication of whether to search for the communications signal at the first location.

27. The mobile platform of claim 23, wherein the received data comprises data related to durations of communication sessions, and wherein the output comprises a prediction of a duration of a new communication session.

28. The mobile platform of claim 23, wherein the output further comprises the called party of the telephone call to be originated by the mobile platform.

29. The mobile platform of claim 23, wherein the received data comprises data related to one or more images, and wherein the output comprises a name related to at least one image of the one or more images.

30. The mobile platform of claim 23, further comprising a network communications interface module, wherein receiving the data related to the plurality of features comprises receiving the data related to the plurality of features via the network communications interface module.