Ameliorative resource action during an e-conference

Patent metadata

Field	Value
Publication number	US-11539540-B1
Application number	US-202117456900-A
Country	US
Kind code	B1
Filing date	Nov 30, 2021
Priority date	Nov 30, 2021
Publication date	Dec 27, 2022
Grant date	Dec 27, 2022

Abstract

A method, a computer program product, and a system for enacting ameliorative resource action during an e-conference. Exemplary embodiments of the present inventive concept may include a method for enacting ameliorative resource action during an e-conference. The method may include collecting data from a user's computer device during the e-conference. Features may be extracted from the collected e-conference data. A user's participation within the e-conference and a resource consumption thereof may be forecasted by applying a user activity model to the extracted features. The ameliorative resource action may be enacted based upon the forecasted user's participation and the resource consumption thereof.

First claim

The invention claimed is:

1. A method for enacting an ameliorative resource action during an e-conference, the method comprising:
collecting data from a user's computer device during the e-conference;
extracting features from the collected e-conference data;
forecasting a user's participation within the e-conference and a resource consumption thereof by applying a user activity model to the extracted features; and
enacting the ameliorative resource action based upon the forecasted user's participation and the resource consumption thereof,
wherein the collected data is selected from a group consisting of e-conference audio feed, e-conference video feed, e-conference activity of the user, an itinerary of the user, and resource consumption of a computing device associated with the user,
wherein the extracted features include a topic of the e-conference, a user activity during the e-conference, a resource consumption during the e-conference, and an itinerary corresponding to the user, and
wherein the extracting features from the collected e-conference data further comprises:
applying natural language processing to spoken speech of the e-conference audio feed and written text of the e-conference video feed;
applying a convolutional neural network to graphics within the e-conference video feed; and
extracting the topic corresponding to the e-conference based on an output of the natural language processing and the convolutional neural network.

2. The method of claim 1 , wherein the model correlates the topic with the forecasted user's participation and the resource consumption thereof.

3. The method of claim 1 , wherein the ameliorative resource action is selected from a group consisting of completing background processing, completing background network activity, pre-running a cooling fan, switching from a tunneled to a non-tunneled VPN, prompting a user to join phone based audio when network performance is not adequate for IP based audio, dynamically pausing or reducing background processing and/or network activity.

4. The method of claim 1 , further comprising:
determining an accuracy of the forecasted user's participation and the resource consumption thereof; and
adjusting the user activity model based on the determined accuracy.

5. A computer program product for enacting an ameliorative resource action during an e-conference, the computer program comprising:
one or more computer-readable storage media and program instructions stored on the one or more computer-readable storage media, the program instructions including a method, the method comprising:
collecting data from a user's computer device during the e-conference;
extracting features from the collected e-conference data;
forecasting a user's participation within the e-conference and a resource consumption thereof by applying a user activity model to the extracted features; and
enacting the ameliorative resource action based upon the forecasted user's participation and the resource consumption thereof,
wherein the collected data is selected from a group consisting of e-conference audio feed, e-conference video feed, e-conference activity of the user, an itinerary of the user, and resource consumption of a computing device associated with the user,
wherein the extracted features include a topic of the e-conference, a user activity during the e-conference, a resource consumption during the e-conference, and an itinerary corresponding to the user, and
wherein the extracting features from the collected e-conference data further comprises:
applying natural language processing to spoken speech of the e-conference audio feed and written text of the e-conference video feed;
applying a convolutional neural network to graphics within the e-conference video feed; and
extracting the topic corresponding to the e-conference based on an output of the natural language processing and the convolutional neural network.

6. The method of claim 5 , wherein the model correlates the topic with the forecasted user's participation and the resource consumption thereof.

7. The method of claim 5 , wherein the ameliorative resource action is selected from a group consisting of completing background processing, completing background network activity, pre-running a cooling fan, switching from a tunneled to a non-tunneled VPN, prompting a user to join phone based audio when network performance is not adequate for IP based audio, dynamically pausing or reducing background processing and/or network activity.

8. The method of claim 5 , further comprising:
determining an accuracy of the forecasted user's participation and the resource consumption thereof; and
adjusting the user activity model based on the determined accuracy.

9. A computer system for enacting an ameliorative resource action during an e-conference, the system comprising:
one or more computer processors, one or more computer-readable storage media, and program instructions stored on the one or more of the computer-readable storage media for execution by at least one of the one or more processors, the program instructions including a method comprising:
collecting data from a user's computer device during the e-conference;
extracting features from the collected e-conference data;
forecasting a user's participation within the e-conference and a resource consumption thereof by applying a user activity model to the extracted features; and
enacting the ameliorative resource action based upon the forecasted user's participation and the resource consumption thereof,
wherein the collected data is selected from a group consisting of e-conference audio feed, e-conference video feed, e-conference activity of the user, an itinerary of the user, and resource consumption of a computing device associated with the user,
wherein the extracted features include a topic of the e-conference, a user activity during the e-conference, a resource consumption during the e-conference, and an itinerary corresponding to the user,
wherein the extracting features from the collected e-conference data further comprises:
applying natural language processing to spoken speech of the e-conference audio feed and written text of the e-conference video feed;
applying a convolutional neural network to graphics within the e-conference video feed; and
extracting the topic corresponding to the e-conference based on an output of the natural language processing and the convolutional neural network.

10. The method of claim 9 , wherein the model correlates the topic with the forecasted user's participation and the resource consumption thereof.

11. The method of claim 9 , wherein the ameliorative resource action is selected from a group consisting of completing background processing, completing background network activity, pre-running a cooling fan, switching from a tunneled to a non-tunneled VPN, prompting a user to join phone based audio when network performance is not adequate for IP based audio, dynamically pausing or reducing background processing and/or network activity.

Assignees

• IBM

Inventors

• Decrop Clement
• Keen Martin G
• Ganci Jr John M
• Silverstein Zachary A

Classifications

• H04L12/1827 Primary

  Network arrangements for conference optimisation or adaptation · CPC title

• H04L12/1831

  Tracking arrangements for later retrieval, e.g. recording contents, participants activities or behavior, network status · CPC title

• H04L67/535

  Tracking the activity of the user (network monitoring arrangements H04L43/00; recording of computer activity G06F11/34) · CPC title