Systems and methods for chat message notification

• US 9,813,495 B1
• Filed: 03/31/2017
• Issued: 11/07/2017
• Est. Priority Date: 03/31/2017
• Status: Active Grant

• Current Assignee: RingCentral Incorporated
• Original Assignee: RingCentral Incorporated
• Inventors: Van Rensburg, Christopher, Vendrow, Vlad
• Primary Examiner(s): Pollack, Melvin H
• Application Number:
  US15/476,919
• Time in Patent Office: 221 Days
• Field of Search: 709204
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 51/046 Interoperability with other network applications or services
• CPC Class Codes: H04L 51/214 using selective forwarding
• CPC Class Codes: H04L 51/224 providing notification on incoming messages, e.g. pushed notifications of received messages
• CPC Class Codes: H04L 51/226 Delivery according to priorities
• CPC Class Codes: H04L 67/104 Peer-to-peer [P2P] networks
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04W 4/12 Messaging; Mailboxes; Announcements

Abstract

Systems and methods are provided for facilitating a chat session. The systems and methods may include receiving chat data, and predicting a likelihood of interest of the user in the chat data, the prediction being based on a model of the user'"'"'s behavior and information reflecting an attribute and an availability associated with a user. The systems and methods may further include providing a notification to a device associated with the user based on the determined likelihood of interest.

Claims

1. A method of facilitating a chat session, the method being performed on a device that provides the chat session and comprising:

receiving chat data;

predicting a likelihood of interest of the user in the chat data, the prediction being based on a model of the user'"'"'s behavior and information reflecting an attribute and an availability associated with a user; and

providing a notification to a device associated with the user based on the determined likelihood of interest.

2. The method of claim 1, further comprising:

determining, based on the model, a first score associated with the chat data and the attribute, and a second score associated with the chat data and the availability;

wherein the likelihood of interest is predicted based on the first and second scores.

3. The method of claim 2, wherein the first score is determined based on a measurement of semantic distance between the first set of keywords included in the chat data and a second set of keywords included in the attribute.

4. The method of claim 2, wherein the attribute includes information related to a set of prior activities of the user;

wherein the first score is an aggregate score determined based on a weighted average of a set of scores, with each of the set of scores being determined based on a degree of correlation between the chat data and an activity of the set of prior activities;

wherein a first weight associated with a first activity of the set of prior activities is higher than a second weight associated with a second activity of the set of prior activities;

wherein the first weight and the second weight are set based on a timing relationship between the first activity and the second activity.

5. The method of claim 1, wherein the notification is also provided based on a determination of whether the chat data includes an identifier associated with the user.

6. The method of claim 1, wherein the availability is determined based on a history of usage of the device by the second user.

7. The method of claim 6, wherein the history of usage of the device comprises a history of the user accessing at least one of the chat session or another chat session.

8. The method of claim 6, wherein the history of usage of the device includes a history of chat data accessed by the second user;

wherein the likelihood of interest is determined based on a relationship between the chat data and the history chat data.

9. The method of claim 1, further comprising:

determining, based on the likelihood of interest and a set of rules, a configuration of the notification, wherein the configuration causes the device to generate a predetermined output to the second user.

10. The method of claim 9, further comprising:

resetting the set of rules after a predetermined time period expires.

11. The method of claim 9, further comprising:

resetting the set of rules after the chat session ends.

12. The method of claim 9, further comprising:

receiving second chat data;

determining a likelihood of relevancy of the second chat data to the chat data;

after determining that the likelihood of relevancy is below a threshold, resetting the set of rules.

13. The method of claim 1, wherein the chat data is received from a sender device;

further comprising;

providing an indication that reflects the indication of interest of the user to the sender device.

14. The method of claim 13, further comprising:

providing the indication to a second device other than the sender device.

15. The method of claim 13, further comprising:

predicting a likelihood of interest of a second user in the chat data;

determining an indication based on the likelihood of interest of the second user; and

providing the indication to the sender device.

16. A non-transitory computer-readable medium storing instructions that are executable by one or more processors of a device that provides a chat session to cause the device to perform a method for facilitating the chat session, the method comprising:

receiving chat data;

predicting a likelihood of interest of the user in the chat data, the prediction being based on a model of the user'"'"'s behavior and information reflecting an attribute and an availability associated with a user; and

providing a notification to a device associated with the user based on the determined likelihood of interest.

17. The non-transitory medium of claim 16, wherein the method further comprises:

determining, based on the model, a first score associated with the chat data and the attribute, and a second score associated with the chat data and the availability;

wherein the likelihood of interest is predicted based on the first and second scores.

18. The non-transitory medium of claim 17, wherein the first score is determined based on a measurement of semantic distance between the first set of keywords included in the chat data and a second set of keywords included in the attribute.

19. The non-transitory medium of claim 17, wherein the attribute includes information related to a set of prior activities of the user;

wherein the first score is an aggregate score determined based on a weighted average of a set of scores, with each of the set of scores being determined based on a degree of correlation between the chat data and an activity of the set of prior activities;

wherein a first weight associated with a first activity of the set of prior activities is higher than a second weight associated with a second activity of the set of prior activities;

wherein the first weight and the second weight are set based on a timing relationship between the first activity and the second activity.

20. An apparatus for conducting a chat session, comprising:

one or more memories having stored thereon computer-executable instructions; and

one or more hardware processors configured to execute the stored instructions to;

receive chat data;

predict a likelihood of interest of the user in the chat data, the prediction being based on a model of the user'"'"'s behavior and information reflecting an attribute and an availability associated with a user; and

provide a notification to a device associated with the user based on the determined likelihood of interest.