Estimating the time until a reply email will be received using a recipient behavior model

• US 9,442,885 B2
• Filed: 12/20/2012
• Issued: 09/13/2016
• Est. Priority Date: 09/26/2012
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Lynar, Timothy M., Steer, Kent C.
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): Seck, Ababacar
• Application Number:
  US13/722,003
• Publication Number:
  US 20140089235A1
• Time in Patent Office: 1,363 Days
• Field of Search: None
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: H04L 51/02 using automatic reactions or user delegation, e.g. automatic replies or chatbot-generated messages
• CPC Class Codes: H04L 51/42 Mailbox-related aspects, e.g. synchronisation of mailboxes

Abstract

A system is provided. The system includes a recipient behavior model for generating an estimate of a receipt time of a reply email from a recipient of an initial email by applying machine learning to the initial email and to training data from other emails. The system further includes an indicator device for indicating the estimate to a user.

Claims

1. A system, comprising:

a set of recipient behavior models for generating an estimate of a receipt time of a reply email from a recipient of an initial email using machine learning; and

an indicator device for indicating the estimate to a user,

wherein the set of recipient behavior models is shared among a set of trusted peers, and wherein the set of recipient behavior models is configured in a master-slave configuration, and a master recipient behavior model from among the recipient behavior models in the set is used to output the estimate that is indicated to the user.

2. The system of claim 1, wherein said recipient behavior model generates a confidence measure of the estimate.

3. The system of claim 1, further comprising a receiver for receiving at least one of the recipient behavior models in the set from a trusted peer, and wherein the more than one recipient behavior model used to generate the estimate comprises the at least one of the recipient behavior models received from the trusted peer.

4. The system of claim 1, wherein the set of recipient behavior models generates the estimate by applying the machine learning to the initial email and to training data from other emails.

5. The system of claim 4, wherein the machine learning comprises at least one of neural networks and decision tree induction.

6. A computer program product for providing an estimate of a receipt time of a reply email from a recipient of an initial email, the computer program product comprising a non-transitory computer readable storage medium having program code embodied therewith, the program code executable by a computer to perform a method comprising:

sharing a set of recipient behavior models among a set of trusted peers;

generating the estimate of the receipt time of the reply email from the recipient of the initial email using more than one recipient behavior model from the set of recipient behavior models and machine learning; and

indicating the estimate to a user using an indication device,

wherein the set of recipient behavior models is configured in a master-slave configuration, and a master recipient behavior model from among the recipient behavior models in the set is used to output the estimate that is indicated to the user.

7. The computer program product of claim 6, wherein at least one previous actual reply time is used to generate the estimate.

8. The computer program product of claim 6, wherein the method further comprises generating a confidence measure of the estimate.

9. The computer program product of claim 6, wherein said sharing step comprises receiving at least one of the recipient behavior models in the set from a trusted peer, and wherein the more than one recipient behavior model used to generate the estimate comprises the at least of the recipient behavior models received from the trusted peer.

10. The computer program product of claim 6, wherein the more than one recipient behavior model generates the estimate by applying the machine learning to the initial email and to training data from other emails.

11. The computer program product of claim 10, wherein the training data comprises at least one of a date sent, a sender, co-recipients, a recipient calendar, sender-receiver relationship information, message attributes, and a number of unread emails in an email inbox of the recipient.

12. The computer program product of claim 10, wherein the machine learning comprises at least one of neural networks and decision tree induction.

13. The computer program product of claim 10, wherein the method further comprises generating an updated estimate based on at least one of additional training data that is newly acquired and evaluated and the same training data being further evaluated over time.

14. The computer program product of claim 13, wherein the updated estimate is generated responsive to a new recipient being added or an existing recipient being removed from the initial email.

15. The computer program product of claim 13, wherein the updated estimate is generated responsive to a user request for the updated estimate.

16. The computer program product of claim 10, wherein the training data is obtained from previous personal correspondence between a sender of the initial email and the recipient of the initial email.

17. The computer program product of claim 10, wherein the training data is obtained from previous peer correspondence between trusted peers, the trusted peers including the recipient of the initial email.

18. The computer program product of claim 10, wherein the training data represents a subset of data corresponding to the previous peer correspondence, the subset determined to preserve private details in the training data.