ANALYZING COMMUNICATION AND DETERMINING ACCURACY OF ANALYSIS BASED ON SCHEDULING SIGNAL

• US 20180349787A1
• Filed: 06/26/2014
• Published: 12/06/2018
• Est. Priority Date: 06/26/2014
• Status: Abandoned Application

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Horling, Bryan Christopher, Shukla, Ashutosh, Bunn, Paul, Pang, Bo
• Application Number:
  US14/316,643
• Publication Number:
  US 20180349787A1
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 10/107 Computer-aided management of electronic mailing [e-mailing]
• CPC Class Codes: G06Q 10/109 Time management, e.g. calendars, reminders, meetings or time accounting
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

Methods, apparatus and computer-readable media (transitory and non-transitory) are disclosed for analyzing a communication to or from a user to identify an event assumption and/or determine a likelihood that the communication is event-related. In various implementations, an accuracy of the event assumption, as well as an accuracy of the determined likelihood, may be assessed based on one or more scheduling signals, such as user-creation of a corresponding calendar entry. In various implementations, a machine learning classifier may be trained based at least in part on one or both accuracies.

Claims

1. A computer-implemented method, comprising:

analyzing, by a computing system using a machine learning classifier, a communication to or from a user to identify an event assumption;

determining, by the computing system based on one or more scheduling signals, an accuracy of the assumption, wherein the one or more scheduling signals include an actionable item created based on content of the communication, wherein the actionable item includes a textual segment of the communication containing an address of an event associated with the event assumption, wherein selection of the actionable item opens an application that is operable for real time navigation to the address; and

training, by the computing system, the machine learning classifier based at least in part on the accuracy.

2. The computer-implemented method of claim 1, further comprising determining, by the computing system based at least in part on the event assumption and using the machine learning classifier, a likelihood that the communication is event-related.

3. The computer-implemented method of claim 2, further comprising determining, by the computing system, an accuracy of the determined likelihood that the communication is event-related.

4. The computer-implemented method of claim 2, further comprising determining, by the computing system based on a count of corroborative scheduling signals, an accuracy of the determined likelihood that the communication is event-related.

5. The computer-implemented method of claim 1, wherein the one or more scheduling signals include a calendar entry created by the user or by another sender or recipient of the communication.

7. The computer-implemented method of claim 1, wherein the one or more scheduling signals include acceptance or rejection of a candidate calendar entry proposed to the user.

8. The computer-implemented method of claim 1, wherein the analyzing is performed by the computing system without providing any content of the communication to a human being.

6. (canceled)

9-16. -16. (canceled)

17. A non-transitory computer-readable medium comprising instructions that, in response to execution of the instructions by a computing system, cause the computing system to perform operations comprising:

analyze a communication to or from a user using a machine learning classifier to identify an event assumption;

determine, based at least in part on the identified event assumption, a likelihood that the communication is event-related;

determine, based on one or more scheduling signals, an accuracy of the determined likelihood, wherein the one or more scheduling signals include an actionable item created based on content of the communication, wherein the actionable item includes a textual segment of the communication containing an address of an event associated with the event assumption, wherein selection of the actionable item opens an application that is operable for real time navigation to the address; and

train the machine learning classifier based at least in part on the accuracy.

18. The non-transitory computer-readable medium of claim 17, wherein the one or more scheduling signals include a calendar entry created by the user or by another sender or recipient of the communication.

19. The non-transitory computer-readable medium of claim 17, wherein the one or more scheduling signals include an event reminder or task created for or by the user.

20. The non-transitory computer-readable medium of claim 17, wherein the one or more scheduling signals include acceptance or rejection of a candidate calendar entry proposed to the user.