# US 12,764,386 B2 · Deep reinforcement learning for personalized screen content optimization

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- Number: US 12,764,386 B2
- Document ID: US12764386B2
- Status: Granted
- Kind: B2
- Application number: 18/917,798
- Filed: 2024-10-16
- Published: 2025-02-06
- Granted: 2026-10-13
- Earliest claimed: 2018-12-20
- Assignees: Adeia Guides Inc.
- Applicants: Adeia Guides Inc.
- Inventors: Kyle Miller
- Web page: https://patentgrep.com/patent/US12764386B2

## CPC

- H04N 21/4666: {using neural networks, e.g. processing the feedback provided by the user}
- G06N 3/088: Non-supervised learning, e.g. competitive learning
- H04N 21/4668: {for recommending content, e.g. movies}
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

Systems and methods are described for selecting content item identifiers for display. The system may identify a set of content items that are likely to be requested in the future based on a history of content item requests. The system then selects a first plurality of content categories using a category selection neural net and selects a first set of recommended content items for the first plurality of content categories. The system increases a reward score for the first plurality of content categories based on receiving a request for a content item that is included in the first set of recommended content items. The system also decreases the reward score for the first plurality of content categories based on determining that the requested content item is included in the set of content items that are likely to be requested in the future. The neural net is trained based on the reward score of the first plurality of content categories to reinforce reward score maximization. The trained neural net is the used to select content items for display.

## Claims

### Claim 1

A method comprising:
training a neural network to display identifiers for recommended content items to a user, wherein the training incentivizes recommendation of only content items that are diverse and dis-incentivizes recommendations of content items that are already likely to be requested by the user;
analyzing connections between neurons of the trained neural network to determine which neural connections reinforce reward score maximization;
based on the analysis, generating a modified neural network by retaining neural connections that lead to the reward score being above a threshold and removing neural connections that lead to the reward score being below the threshold, wherein neural connections removed include connections that relate to content items that the user routinely consumes or is likely to request; and
in response to receiving a request from the user for a content item, using the modified neural network to recommend to the user, content items that are diverse and not likely to be requested by the user.

### Claim 2 (depends on 1)

The method of claim 1, further comprising:
monitoring user requests over a predetermined period of time; and
training the modified neural network based on the monitored user requests.

### Claim 3 (depends on 2)

The method of claim 2, further comprising, increasing reward score for a neural connection related to the monitored user request if the monitored request is related to a content item that is associated with a diverse category.

### Claim 4 (depends on 2)

The method of claim 2, further comprising:
determining that the user routinely consumes content for a particular category;
determining that a first neural connection in the neural network is related to the particular category whose content is routinely consumed by the user; and
removing the first neural connection in response to determining that the first neural connection is related to the particular category whose content is routinely consumed by the user.

### Claim 5 (depends on 4)

The method of claim 4, further comprising, updating the modified neural network after removal of the first neural connection.

### Claim 6 (depends on 1)

The method of claim 1, wherein the neural network comprises a plurality of neurons connecting a plurality of features with a superset of content categories.

### Claim 7 (depends on 6)

The method of claim 6, wherein the plurality of features comprises at least two of: content item requests data, content category requests data, time data, and collaborative filtering vectors.

### Claim 8 (depends on 1)

The method of claim 1, wherein, the content items recommended to the user that are diverse and not likely to be requested by the user are obtained from recommendation engine in response to making an API call to the recommendation engine.

### Claim 9 (depends on 1)

The method of claim 1, further comprising, training the modified neural network with each new request received from the user.

### Claim 10 (depends on 1)

The method of claim 1, wherein the reward score is associated with a content category represented by a neural connection.

### Claim 11

A system comprising:
communications circuitry configured to access a neural network; and
control circuitry configured to:
train the neural network to display identifiers for recommended content items to a user, wherein the training incentivizes recommendation of only content items that are diverse and dis-incentivizes recommendations of content items that are already likely to be requested by the user;
analyze connections between neurons of the trained neural network to determine which neural connections reinforce reward score maximization;
based on the analysis, generate a modified neural network by retaining neural connections that lead to the reward score being above a threshold and removing neural connections that lead to the reward score being below the threshold, wherein neural connections removed include connections that relate to content items that the user routinely consumes or is likely to request; and
in response to receiving a request from the user for a content item, use the modified neural network to recommend to the user, content items to the user that are diverse and not likely to be requested by the user.

### Claim 12 (depends on 11)

The method of claim 11, further comprising, the control circuitry configured to:
monitor user requests over a predetermined period of time; and
train the modified neural network based on the monitored user requests.

### Claim 13 (depends on 12)

The system of claim 12, further comprising, the control circuitry configured to increase reward score for a neural connection related to the monitored user request if the monitored request is related to a content item that is associated with a diverse category.

### Claim 14 (depends on 12)

The system of claim 12, further comprising, the control circuitry configured to:
determine that the user routinely consumes content for a particular category;
determine that a first neural connection in the neural network is related to the particular category whose content is routinely consumed by the user; and
remove the first neural connection in response to determining that the first neural connection is related to the particular category whose content is routinely consumed by the user.

### Claim 15 (depends on 14)

The system of claim 14, further comprising, the control circuitry configured to update the modified neural network after removal of the first neural connection.

### Claim 16 (depends on 11)

The system of claim 11, wherein the neural network comprises a plurality of neurons connecting a plurality of features with a superset of content categories.

### Claim 17 (depends on 16)

The system of claim 16, wherein the plurality of features comprises at least two of: content item requests data, content category requests data, time data, and collaborative filtering vectors.

### Claim 18 (depends on 11)

The system of claim 11, wherein, the content items recommended to the user that are diverse and not likely to be requested by the user are obtained by the control circuitry from recommendation engine in response to making an API call to the recommendation engine.

### Claim 19 (depends on 11)

The system of claim 11, further comprising, the control circuitry configured to train the modified neural network with each new request received from the user.

### Claim 20 (depends on 11)

The system of claim 11, wherein the reward score is associated with a content category represented by a neural connection.