Method to Predict a Communicative Action that is Most Likely to be Executed Given a Context

• US 20130346347A1
• Filed: 08/07/2012
• Published: 12/26/2013
• Est. Priority Date: 06/22/2012
• Status: Abandoned Application

• Current Assignee: Google Inc. (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Aradhye, Hrishikesh, Hua, Wei, Lehmann, Daniel, Patterson, Anna, Lin, Ruei-Sung
• Application Number:
  US13/568,957
• Publication Number:
  US 20130346347A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04M 1/6008 in the transmitter circuit
• 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
• CPC Class Codes: H04M 2250/06 including a wireless LAN interface

Abstract

Disclosed are apparatus and methods for providing machine-learning services. A context-identification system executing on a mobile platform can receive data comprising context-related data associated with the mobile platform and application-related data received from the mobile platform. The context-identification system can identify a context using the context-related data associated with the mobile platform and/or the application-related data received from the mobile platform. Based on at least one context identified, context-identification system can predict a communicative action associated with the mobile platform by performing a machine-learning operation on the received data. An instruction can be received to execute the communicative action associated with the mobile platform.

Claims

1. A method, comprising:

receiving data at a context-identification system executing on a mobile platform, wherein the received data comprises;

context-related data associated with the mobile platform and application-related data received from the mobile platform;

identifying, by the context identification system, at least one context using the context-related data associated with the mobile platform and/or the application-related data received from the mobile platform, wherein the at least one context is indicative of a change in location from a first location to a second location;

based on the at least one context indicative of the change in location from the first location to the second location, predicting at least one communicative action associated with the mobile platform, wherein the at least one communicative action comprises a communicative action involving a phone number;

performing a machine-learning operation on the received data, wherein performing the machine-learning operation on the received data comprises ranking the phone number amongst other phone numbers associated with the mobile platform, and wherein the ranking is based at least on the change in location from the first location to the second location; and

receiving an instruction to execute the at least one communicative action associated with the mobile platform.

2. The method of claim 1, wherein the communicative action involving the phone number comprises at least one of:

dialing the phone number using the mobile platform, sending a message to the phone number using the mobile platform, opening a contact associated with the phone number on the mobile platform, and executing an application associated with the phone number on the mobile platform.

3. The method of claim 2, wherein the application-related data received from the mobile platform includes one or more of:

a dialing indication, a received call indication, a missed call indication, one or more digits of the phone number, a messaging indication, text of a message, a contact indication of a contact, data related to the contact, and an application indication of an application to be executed on the mobile platform.

4. The method of claim 2, wherein the communicative action involving the phone number includes an instruction to execute dialing the phone number using a phone dialing application, and wherein the instruction to execute dialing the phone number is based on the application-related data received from the mobile platform.

5. The method of claim 1, wherein the context-related data associated with the mobile platform corresponds to one or more context signals comprising one or more of the following:

(a) a current time, (b) a current date, (c) a current day of the week, (d) a current month, (e) a current season, (f) a time of a future event or future context, (g) a date of a future event or future context, (h) a day of the week of a future event or future context, (i) a month of a future event or future user-context, (j) a season of a future event or future context, (k) a time of a past event or past context, (l) a date of a past event or past context, (m) a day of the week of a past event or past context, (n) a month of a past event or past context, (o) a season of a past event or past context, (p) ambient temperature, (q) a current, future, or past weather forecast at a current location, (r) a current, future, or past weather forecast at a location of a planned event, (s) a current, future, or past weather forecast at or near a location of a previous event, (t) information on a calendar associated with a user-profile, (u) information accessible via a user'"'"'s social networking account, (v) noise level or any recognizable sounds detected by a device, (w) devices that are currently available to communicate with the mobile platform, (x) devices in proximity to the mobile platform, (y) devices that are available to receive instruction from the mobile platform, (z) information derived from cross-referencing at least one of;

information on the user'"'"'s calendar, information sent to the user, and/or information available via the user'"'"'s social networking account, (aa) health statistics or characterizations of the user'"'"'s current health, (bb) a user'"'"'s recent context as determined from sensors on or near the user and/or other sources of context information associated with the mobile platform, (cc) a current location of the mobile platform, (dd) a past location of the mobile platform, and (ee) a future location of the mobile platform.

6. The method of claim 5, wherein performing the machine-learning operation associated with the at least one feature comprises at least one of the following:

clustering the one or more context signals, classifying the one or more context signals, and predicting a future context signal based on the one or more context signals.

7. The method of claim 5, wherein the mobile platform is configured to communicate with one or more other devices to obtain the one or more context signals.

8. 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 at a context-identification system executing on a mobile platform, wherein the received data comprises;

context-related data associated with the mobile platform and application-related data received from the mobile platform;

identifying, by the context-identification system, at least one context using the context-related data associated with the mobile platform and/or the application-related data received from the mobile platform, wherein the at least one context is indicative of a change in location from a first location to a second location;

based on the at least one context indicative of the change in location from the first location to the second location, predicting at least one communicative action associated with the mobile platform, wherein the at least one communicative action comprises a communicative action involving a phone number;

performing a machine-learning operation on the received data, wherein performing the machine-learning operation on the received data comprises ranking the phone number amongst other phone numbers associated with the mobile platform, and wherein the ranking is based at least on the change in location from the first location to the second location; and

receiving an instruction to execute the at least one communicative action associated with the mobile platform.

9. The article of manufacture of claim 8, wherein the communicative action involving the phone number comprises at least one of:

dialing the phone number using the mobile platform, sending a message to the phone number using the mobile platform, opening a contact associated with the phone number on the mobile platform, and executing an application associated with the phone number on the mobile platform.

10. The article of manufacture of claim 9, wherein the application-related data received from the mobile platform includes one or more of:

a dialing indication, a received call indication, a missed call indication, one or more digits of the phone number, a messaging indication, text of a message, a contact indication of a contact, data related to the contact, and an application indication of an application to be executed on the mobile platform.

11. The article of manufacture of claim 9, wherein the communicative action involving the phone number includes an instruction to execute dialing the phone number using a phone dialing application, and wherein the instruction to execute dialing the phone number is based on the application-related data received from the mobile platform.

12. The article of manufacture of claim 8, wherein the context-related data associated with the mobile platform corresponds to one or more context signals comprising one or more of the following:

(a) a current time, (b) a current date, (c) a current day of the week, (d) a current month, (e) a current season, (f) a time of a future event or future context, (g) a date of a future event or future context, (h) a day of the week of a future event or future context, (i) a month of a future event or future user-context, (j) a season of a future event or future context, (k) a time of a past event or past context, (l) a date of a past event or past context, (m) a day of the week of a past event or past context, (n) a month of a past event or past context, (o) a season of a past event or past context, (p) ambient temperature, (q) a current, future, or past weather forecast at a current location, (r) a current, future, or past weather forecast at a location of a planned event, (s) a current, future, or past weather forecast at or near a location of a previous event, (t) information on a calendar associated with a user-profile, (u) information accessible via a user'"'"'s social networking account, (v) noise level or any recognizable sounds detected by a device, (w) devices that are currently available to communicate with the mobile platform, (x) devices in proximity to the mobile platform, (y) devices that are available to receive instruction from the mobile platform, (z) information derived from cross-referencing at least one of;

information on the user'"'"'s calendar, information sent to the user, and/or information available via the user'"'"'s social networking account, (aa) health statistics or characterizations of the user'"'"'s current health, (bb) a user'"'"'s recent context as determined from sensors on or near the user and/or other sources of context information associated with the mobile platform, (cc) a current location of the mobile platform, (dd) a past location of the mobile platform, and (ee) a future location of the mobile platform.

13. The article of manufacture of claim 12, wherein performing the machine-learning operation associated with the at least one feature comprises at least one of the following:

clustering the one or more context signals, classifying the one or more context signals, and predicting a future context signal based on the one or more context signals.

14. The article of manufacture of claim 12, wherein the mobile platform is configured to communicate with one or more other devices to obtain the one or more context signals.

15. 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 at a context-identification system executing on a mobile platform, wherein the received data comprises;

context-related data associated with the mobile platform and application-related data received from the mobile platform;

identifying, by the context-identification system, at least one context using the context-related data associated with the mobile platform and/or the application-related data received from the mobile platform, wherein the at least one context is indicative of a change in location from a first location to a second location;

based on the at least one context indicative of the change in location from the first location to the second location, predicting at least one communicative action associated with the mobile platform, wherein the at least one communicative action comprises a communicative action involving a phone number;

performing a machine-learning operation on the received data, wherein performing the machine-learning operation on the received data comprises ranking the phone number amongst other phone numbers associated with the mobile platform, and wherein the ranking is based at least on the change in location from the first location to the second location; and

receiving an instruction to execute the at least one communicative action associated with the mobile platform.

16. The mobile platform of claim 15, wherein the communicative action involving the phone number comprises at least one of:

dialing the phone number using the mobile platform, sending a message to the phone number using the mobile platform, opening a contact associated with the phone number on the mobile platform, and executing an application associated with the phone number on the mobile platform.

17. The mobile platform of claim 16, wherein the application-related data received from the mobile platform includes one or more of:

a dialing indication, a received call indication, a missed call indication, one or more digits of the phone number, a messaging indication, text of a message, a contact indication of a contact, data related to the contact, and an application indication of an application to be executed on the mobile platform.

18. The mobile platform of claim 16, wherein the communicative action involving the phone number includes an instruction to execute dialing the phone number using a phone dialing application, and wherein the instruction to execute dialing the phone number is based on the application-related data received from the mobile platform.

19. The mobile platform of claim 15, wherein the context-related data associated with the mobile platform corresponds to one or more context signals comprising one or more of the following:

(a) a current time, (b) a current date, (c) a current day of the week, (d) a current month, (e) a current season, (f) a time of a future event or future context, (g) a date of a future event or future context, (h) a day of the week of a future event or future context, (i) a month of a future event or future user-context, (j) a season of a future event or future context, (k) a time of a past event or past context, (l) a date of a past event or past context, (m) a day of the week of a past event or past context, (n) a month of a past event or past context, (o) a season of a past event or past context, (p) ambient temperature, (q) a current, future, or past weather forecast at a current location, (r) a current, future, or past weather forecast at a location of a planned event, (s) a current, future, or past weather forecast at or near a location of a previous event, (t) information on a calendar associated with a user-profile, (u) information accessible via a user'"'"'s social networking account, (v) noise level or any recognizable sounds detected by a device, (w) devices that are currently available to communicate with the mobile platform, (x) devices in proximity to the mobile platform, (y) devices that are available to receive instruction from the mobile platform, (z) information derived from cross-referencing at least one of;

information on the user'"'"'s calendar, information sent to the user, and/or information available via the user'"'"'s social networking account, (aa) health statistics or characterizations of the user'"'"'s current health, (bb) a user'"'"'s recent context as determined from sensors on or near the user and/or other sources of context information associated with the mobile platform, (cc) a current location of the mobile platform, (dd) a past location of the mobile platform, and (ee) a future location of the mobile platform.

20. The mobile platform of claim 19, wherein performing the machine-learning operation associated with the at least one feature comprises at least one of the following:

clustering the one or more context signals, classifying the one or more context signals, and predicting a future context signal based on the one or more context signals.