Predictive content placement on a managed services system
First Claim
1. A computer-implemented method, comprising:
- receiving, from a viewing device, user data associated with user activity of a first user;
generating, using the user data, an algorithm, and a plurality of operating parameters, a first estimator that is a maximum likelihood state vector, wherein the first estimator predicts future viewing activity of the first user;
receiving a stochastic recommendation for content from a server, the stochastic recommendation being based on the first estimator and a second estimator, wherein the second estimator is generated using user activity of a second user that is different from the first user; and
presenting the content on the viewing device based on the stochastic recommendation.
1 Assignment
0 Petitions
Accused Products
Abstract
A distributed stochastic learning agent analyzes viewing and/or interactive service behavior patterns of users of a managed services system. The agent may operate on embedded and/or distributed devices such as set-top boxes, portable video devices, and interactive consumer electronic devices. Content may be provided with services such as video and/or interactive applications at a future time with maximum likelihood that a subscriber will be viewing a video or utilizing an interactive service at that future time. For example, user impressions can be maximized for content such as advertisements, and content may be scheduled in real-time to maximize viewership from across all video and/or interactive services.
-
Citations
25 Claims
-
1. A computer-implemented method, comprising:
-
receiving, from a viewing device, user data associated with user activity of a first user; generating, using the user data, an algorithm, and a plurality of operating parameters, a first estimator that is a maximum likelihood state vector, wherein the first estimator predicts future viewing activity of the first user; receiving a stochastic recommendation for content from a server, the stochastic recommendation being based on the first estimator and a second estimator, wherein the second estimator is generated using user activity of a second user that is different from the first user; and presenting the content on the viewing device based on the stochastic recommendation. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
-
-
11. A system, comprising:
-
one or more memories; one or more processors coupled to the one or more memories; and a client-agent stored in the one or more memories and executing on the one or more processors and configured to; receive, from a viewing device, user data associated with user activity of a first user; generate, using the user data, an algorithm, and a plurality of operating parameters, a first estimator that is a maximum likelihood state vector, wherein the first estimator predicts future viewing activity of the first user; transmit the first estimator to a server for determining a stochastic recommendation for content; receive the stochastic recommendation from the server, the stochastic recommendation being based on the first estimator and a second estimator, wherein the second estimator is generated using user activity of a second user that is different from the first user; and present the content on the viewing device based on the stochastic recommendation. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
-
-
21. A non-transitory computer-readable medium having instructions stored thereon, that when executed by one or more processors cause the one or more processors to perform operations, the operations comprising:
-
receiving, from a viewing device, user data associated with user activity of a first user; generating, using the user data, an algorithm, and a plurality of operating parameters, a first estimator that is a maximum likelihood state vector, wherein the first estimator predicts future viewing activity of the first user; receiving a stochastic recommendation for content from a server, the stochastic recommendation being based on the first estimator and a second estimator, wherein the second estimator is generated using user activity of a second user that is different from the first user; and presenting the content on the viewing device based on the stochastic recommendation. - View Dependent Claims (22, 23)
-
-
24. A computer-implemented method, comprising:
-
receiving, from a first viewing device, a first estimator that is a first maximum likelihood state vector, wherein the first estimator predicts future viewing activity of the first user, the first estimator being generated using first user data associated with user activity of a first user, an algorithm, and a plurality of operating parameters; receiving, from a second user device, a second estimator that is a second maximum likelihood state vector, wherein the second estimator predicts future viewing activity of the second user, the second estimator being generated using second user data associated with user activity of a second user, the algorithm, and the plurality of operating parameters; determining, by a server, a stochastic recommendation for content based on the first estimator and the second estimator; and presenting the content on the first viewing device based on the stochastic recommendation.
-
-
25. A non-transitory computer-readable medium having instructions stored thereon, that when executed by one or more processors cause the one or more processors to perform operations, the operations comprising:
-
receiving, from a first user device, a first estimator that is a first maximum likelihood state vector, wherein the first estimator predicts future viewing activity of the first user, the first estimator being generated using first user data associated with user activity of a first user, an algorithm, and a plurality of operating parameters; receiving, from a second user device, a second estimator that is a second maximum likelihood state vector, wherein the second estimator predicts future viewing activity of the second user, the second estimator being generated using second user data associated with user activity of a second user, the algorithm, and the plurality of operating parameters; determining, by a server, a stochastic recommendation for content based on the first estimator and the second estimator; and presenting the content on the first user device based on the stochastic recommendation.
-
Specification