Computer-Implemented System And Method For Distributed Activity Detection

• US 20180013843A1
• Filed: 07/06/2016
• Published: 01/11/2018
• Est. Priority Date: 07/06/2016
• Status: Active Grant

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Inventors: Roberts, Michael, Ahern, Shane, Bart, Evgeniy, Gunning, David
• Granted Patent: US 11,477,302 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/337 Profile generation, learning or modification
• CPC Class Codes: G06F 16/908 using metadata automatically derived from the content
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 20/10 using kernel methods, e.g. support vector machines [SVM]
• 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 4/029 Location-based management or tracking services

Abstract

A computer-implemented system and method for distributed activity detection is provided. Contextual data collected for a user performing an activity is processed on a mobile computing device. The mobile computing device extracts features from the contextual data and compares the features with a set of models. Each model represents an activity. A confidence score is assigned to each model based on the comparison with the features and the mobile computing device transmits the features to a server when the confidence scores for the models are low. The server trains a new model using the features and sends the new model to the mobile computing device.

Claims

1. A computer-implemented system for distributed activity detection, comprising:

at least one of a mobile computing device and a sensor device to process contextual data for a user performing an activity, to extract features from the contextual data via the mobile computing device, to compare the features with a set of models stored on the mobile computing device, wherein each model represents an activity, to assign a confidence score to each model based on the comparison with the features, to transmit the features to a server when the confidence scores for each model are low; and

a server to train a new model on the server using the features and to send the new model to the mobile computing device or the sensor device.

2. A system according to claim 1, wherein the mobile computing device further extracts a further set of features from additional contextual data received, compares the further features generated from a set of stored models, assigns a confidence score to each model based on the comparison with the features, and assigns the activity associated with the model having the highest confidence score to the activity being performed by the user.

3. A system according to claim 2, wherein the mobile computing device further receives from the user an assignment of a different activity for the further features to replace the assigned activity associated with the model having the highest confidence score.

4. A system according to claim 1, wherein the mobile computing device further adds the new model to the models stored on the mobile computing device.

5. A system according to claim 1, wherein the mobile computing device further generates vectors for the features, wherein the vectors of the features are compared with the models on the mobile computing device.

6. A system according to claim 1, wherein the mobile computing device further transmits a request to the user of the mobile computing device to identify at least one of the activity being performed and the activity is still being performed.

7. A system according to claim 6, wherein the request comprises a list of activities for selection by the user.

8. A system according to claim 6, wherein the mobile computing device further receives from the user an identity of the activity being performed and sends the identified activity to the server with the features.

9. A system according to claim 1, further comprising:

training the new model on the server using a combination of labeled population data indexed by activity label and the user'"'"'s specific contextual data.

10. A system according to claim 1, wherein the feature extraction depends on a type of the contextual data.

11. A computer-implemented method for distributed activity detection, comprising:

processing on a mobile computing device contextual data for a user performing an activity;

extracting features from the contextual data via the mobile computing device;

comparing the features with a set of models stored on the mobile computing device, wherein each model represents an activity;

assigning a confidence score to each model based on the comparison with the features;

transmitting the features to a server when the confidence scores for each model are low;

training a new model on the server using the features; and

sending from the server, the new model to the mobile computing device.

12. A method according to claim 11, further comprising:

extracting a further set of features from additional contextual data received;

comparing the further features with the set of models stored on the mobile computing device;

assigning a confidence score to each model based on the comparison with the features; and

assigning the activity associated with the model having the highest confidence score to the activity being performed by the user.

13. A method according to claim 12, further comprising:

receiving from the user an assignment of a different activity for the further features to replace the assigned activity associated with the model having the highest confidence score.

14. A method according to claim 11, further comprising:

adding the new model to the models stored on the mobile computing device.

15. A method according to claim 11, further comprising:

generating vectors for the features, wherein the vectors of the features are compared with the models on the mobile computing device.

16. A method according to claim 11, further comprising:

transmitting a request to the user of the mobile computing device to identify at least one of the activity being performed and the activity is still being performed.

17. A method according to claim 16, wherein the request comprises a list of activities for selection by the user.

18. A method according to claim 16, further comprising:

receiving from the user an identity of the activity being performed and sending the identified activity to the server with the features.

19. A method according to claim 11, further comprising:

training the new model on the server using a combination of labeled population data indexed by activity label and the user'"'"'s specific contextual data.

20. A method according to claim 11, wherein the feature extraction depends on a type of the contextual data.