Using machine learning and other models to determine a user preference to cancel a stream or download

Patent metadata

Field	Value
Publication number	US-11115695-B2
Application number	US-201715815321-A
Country	US
Kind code	B2
Filing date	Nov 16, 2017
Priority date	Nov 16, 2017
Publication date	Sep 7, 2021
Grant date	Sep 7, 2021

Abstract

A system and method are disclosed for training a machine learning model using information pertaining to transmissions of one or more media items to user devices associated with a user account. In an implementation, training data for the machine learning model includes first contextual information associated with a first user device and second contextual information associated with a second user device. The number of the transmissions to the user devices for the user account exceeds a threshold number of transmissions allowed for the user account. Training data further includes generating a first target output that identifies an indication of a preference of a user preference to keep or cancel each of the transmission. The method includes providing the training data to train the machine learning model. The trained machine learning model may be used to determine which of the new transmissions is to be canceled.

First claim

What is claimed is:

1. A method for training a machine learning model using information pertaining to transmissions of one or more media items to a plurality of user devices associated with a user account, the method comprising:
generating training data for the machine learning model, wherein generating the training data comprises:
generating first training input, the first training input comprising first contextual information associated with a first user device of the plurality of user devices;
generating second training input, the second training input comprising second contextual information associated with a second user device of the plurality of users devices, wherein a number of the transmissions to the plurality of user devices for the user account exceeds a threshold number of transmissions that is allowed for the user account; and
generating a first target output for the first training input and the second training input, wherein the first target output identifies
an indication of a preference of a user associated with the user account to cancel a first transmission to the first user device responsive to the number of the transmissions to the plurality of user devices exceeding the threshold number, and
an indication of a preference of the user to keep a second transmission to the second user device responsive to the number of the transmissions exceeding the threshold number; and

providing the training data to train the machine learning model on (i) a set of training inputs comprising the first training input and the second training input, and (ii) a set of target outputs comprising the first target output.

2. The method of claim 1 , wherein the one or more media items are different instances of a same media item.

3. The method of claim 1 , wherein the indication of the preference of the user associated with the user account to cancel the first transmission to the first user device represents a user selection to cancel the transmission associated with the first user device.

4. The method of claim 1 ,
wherein the user account is a shared user account associated with a plurality of users, wherein generating the training data further comprises:
generating third training input, the third training input comprising user profile activity information indicative of user activities associated with a particular user profile of the shared user account; and
wherein the set of training inputs comprises the first, the second, and the third training input.

5. The method of claim 1 ,
wherein first contextual information associated with the first user device comprises user activity information indicative of user interaction with an application of the first user device, wherein the application is to receive at least one of the one or more media items.

6. The method of claim 1 ,
wherein first contextual information associated with the first user device comprises device information indicative of a device type of the first user device.

7. The method of claim 1 ,
wherein first contextual information associated with the first user device comprises first location information indicative of a geolocation of the first user device.

8. The method of claim 1 ,
wherein first contextual information associated with the first user device comprises second location information indicative of a proximity of the first user device to other user devices associated with the user account.

9. The method of claim 1 ,
wherein first contextual information associated with the first user device comprises third location information indicative of a contextual location of the first user device.

10. The method of claim 1 ,
wherein first contextual information associated with the first user device comprises:
session information indicative of a user interaction with an application to receive at least one of the one or more media items during a session, wherein the session begins at an opening of the application and ends at a closing of the application, and
visit information indicative of a user interaction with the application during a visit, wherein the visit begins responsive to a user interaction with the application after a first period of user inactivity during the session and ends after a second period of user inactivity during the session.

11. The method of claim 1 , wherein each training input of the set of training inputs is mapped to the target output in the set of target outputs.

12. The method of claim 1 , further comprising:
receiving an indication that the number of the transmissions to the plurality of user devices exceeds the threshold number of transmissions allowed for the user account;
generating, by the machine learning model, a test output that identifies which of the transmissions of the one or more media items is to be canceled;
creating a recommendation to cancel a transmission of the identified media item to a respective one of the plurality of user devices;
receiving user input to cancel the transmission of the identified media item in view of the recommendation; and
adjusting the machine learning model based on the user input.

13. The method of claim 1 , wherein the transmissions of the one or more media items comprise concurrent streams of the one or more media items.

14. A method for using a trained machine learning model with respect to transmissions of one or more media items to a plurality of user devices associated with a user account to determine which of the transmissions is to be canceled, the method comprising:
determining that a number of the transmissions of the one or more media items to the plurality of user devices for the user account exceeds a threshold number of transmissions that is allowed for the user account;
responsive to determining that the number of the transmissions to the plurality of user devices for the user account exceeds the threshold number that is allowed for the user account,
providing to the trained machine learning model first input comprising first contextual information associated with a first user device of the plurality of user devices, and second input comprising second contextual information associated with a second user device of the plurality of user devices; and

obtaining, from the trained machine learning model, one or more outputs identifying (i) a first transmission to the first user device and a second transmission to the second user device, (ii) a level of confidence for a preference of a user associated with the user account to cancel the first transmission in response to the number of the transmissions to the plurality of user devices exceeding the threshold number allowed for the user account, and a level of confidence for a preference of the user to cancel the second transmission in response to the number of the transmissions to the plurality of user devices exceeding the threshold number allowed for the user account.

15. The method of claim 14 , further comprising:
canceling either the first transmission or the second transmission of the one or more media items in view of the level of confidence for the preference of the user to cancel the first transmission and the second transmission.

16. The method of claim 14 , wherein the first transmission is a first stream of the one or more media items to the first user device and the second transmission is a second stream of the one or more media items to the second user device, wherein the first stream and the second stream are concurrent streams, and wherein the method comprises:
canceling either the first stream to the first user device or the second stream to the se

Assignees

• Google Llc

Inventors

• Lewis Justin
• Price Thomas

Classifications

• G06N20/00 Primary

  Machine learning · CPC title

• H04L67/535

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

• H04N21/25858

  involving client software characteristics, e.g. OS identifier · CPC title

• H04L41/16

  using machine learning or artificial intelligence · CPC title

• H04N21/25891

  being end-user preferences (retrieval of video data in a video database based on user preferences G06F16/739; arrangements for recognizing users' preferences H04H60/46; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18) · CPC title