# US 12,015,816 B2 · User preferences based synthesis of group recommendations

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- Number: US 12,015,816 B2
- Document ID: US12015816B2
- Status: Granted
- Kind: B2
- Application number: 18/203,520
- Filed: 2023-05-30
- Published: 2023-09-21
- Granted: 2024-06-18
- Earliest claimed: 2021-11-12
- Assignees: Disney Enterprises, Inc.
- Applicants: Disney Enterprises, Inc.
- Inventors: Sarah Depew; Claudia Cervon
- Web page: https://patentgrep.com/patent/US12015816B2

## CPC

- H04N 21/2668: Creating a channel for a dedicated end-user group, e.g. insertion of targeted commercials based on end-user profiles {(information retrieval from the Internet by querying with filtering and personalisation G06F16/9535; arrangements for replacing or switching information during the broadcast H04H20/10; push services over packet-switching network H04L12/1859; adaptation of message content in packet-switching networks H04L51/063)}
- G06Q 10/42: Determination of affinities or common interests between users
- G06Q 30/0201: Market modelling; Market analysis; Collecting market data
- G06Q 30/0251: Targeted advertisements
- G06Q 30/0255: {based on user history}
- G06Q 30/0631: {Recommending goods or services}
- H04L 67/306: User profiles
- H04N 21/252: {Processing of multiple end-users' preferences to derive collaborative data}
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

A system includes a computing platform having processing hardware and a memory storing an asset library and software code including a recommendation engine. The processing hardware executes the software code to receive, from a user, group generation data and a preferences profile of the user, the group generation data identifying a group including the user and another user, and to send, to the other user, an invitation to join the group. The processing hardware also executes the software code to receive, from the other user, one of an acceptance or a refusal of the invitation, obtain, in response to receiving the acceptance, a preferences profile of the other user, generate, using the preferences profiles of the user and the other user, a group preferences profile for the group, and identify, using the recommendation engine and the group preferences profile, one or more assets in the asset library for the group.

## Claims

### Claim 1

A system comprising:
a computing platform including a processing hardware and a system memory;
an asset library and a software code including a recommendation engine stored in the system memory;
the processing hardware configured to execute the software code to:
receive, from a user, a group generation data and a preferences profile of the user, the group generation data identifying a group including the user and at least one other user;
send, to the at least one other user, an invitation to join the group;
obtain, in response to receiving an acceptance of the invitation, a preferences profile of the at least one other user;
generate, using the preferences profile of the user and the preferences profile of the at least one other user, a group preferences profile for the group;
use the recommendation engine and the group preferences profile having a combined filtering criteria to identify one or more assets in the asset library for the group, wherein the combined filtering criteria comprise at least one of (i) one or more actors, (ii) a mood, or (iii) a watch time; and
output a group recommendation, the group recommendation identifying the identified one or more assets.

### Claim 2 (depends on 1)

The system of claim 1, wherein the software code is configured to provide a graphical user interface (GUI), and wherein the processing hardware is further configured to execute the software code to display a prioritized list of the one or more identified assets to the user and the at least one other user via the GUI.

### Claim 3 (depends on 1)

The system of claim 1, further comprising a user history database including a user history of at least one of the user or the at least one other user;
wherein the processing hardware is further configured to execute the software code to obtain the user history from the user history database;
wherein the group preferences profile is generated further using the user history.

### Claim 4 (depends on 1)

The system of claim 1, wherein the group preferences profile is a short term profile that is deleted from the system after an occurrence of a predetermined action.

### Claim 5 (depends on 1)

The system of claim 1, wherein the combined filtering criteria comprise the watch time.

### Claim 6 (depends on 1)

The system of claim 1, wherein the group preferences profile is re-generated automatically by the system when a new user is added to the group or when one of the user or the at least one other user is removed from the group.

### Claim 7 (depends on 1)

The system of claim 1, wherein the processing hardware is further configured to execute the software code to:
receive, from the user or the at least one other user, a selection of a first asset of the identified one or more assets; and
present the first asset to the user and the at least one other user for asynchronous consumption.

### Claim 8 (depends on 1)

The system of claim 1, wherein the processing hardware is further configured to execute the software code to:
receive, from the user or the at least one other user, a selection of a first asset of the identified one or more assets; and
present the first asset to the user and the at least one other user for synchronous consumption.

### Claim 9 (depends on 1)

The system of claim 1, wherein the preferences profile of the user includes first filtering criteria and the preferences profile of the at least one other user includes second filtering criteria, and wherein the combined filtering criteria is generated using the first filtering criteria and the second filtering criteria.

### Claim 10 (depends on 1)

The system of claim 1, wherein the recommendation engine includes at least one machine learning (ML) predictive model, and wherein the processing hardware is further configured to execute the software code to:
receive, from a system administrator of the system, a corrective input changing the group recommendation to a revised group recommendation; and
improve, by applying machine learning based on the corrective input, a performance of the recommendation engine including the at least one ML predictive model.

### Claim 11

A computer-implemented method comprising:
receiving, from a user, a group generation data and a preferences profile of the user, the group generation data identifying a group including the user and at least one other user;
sending, to the at least one other user, an invitation to join the group;
obtaining, in response to receiving an acceptance of the invitation, a preferences profile of the at least one other user;
generating, using the preferences profile of the user and the preferences profile of the at least one other user, a group preferences profile for the group;
using the recommendation engine and the group preferences profile, having a combined filtering criteria to identify one or more assets in the asset library for the group, wherein the combined filtering criteria comprise at least one of (i) one or more actors, (ii) a mood, or (iii) a watch time; and
outputting a group recommendation, the group recommendation identifying the identified one or more assets.

### Claim 12 (depends on 11)

The method of claim 11, further comprising:
displaying a prioritized list of the one or more identified assets to the user and the at least one other user via a graphical user interface (GUI).

### Claim 13 (depends on 11)

The method of claim 11, further comprising:
obtaining the user history from a user history of at least one of the user or the at least one other user;
wherein generating the group preferences profile further uses the user history.

### Claim 14 (depends on 11)

The method of claim 11, further comprising:
deleting the group preferences profile from the system after an occurrence of a predetermined action.

### Claim 15 (depends on 11)

The method of claim 11, wherein the combined filtering criteria comprise the watch time.

### Claim 16 (depends on 11)

The method of claim 11, further comprising:
automatically re-generating the group preferences profile when a new user is added to the group or when one of the user or the at least one other user is removed from the group.

### Claim 17 (depends on 11)

The method of claim 11, further comprising:
receiving from the user or the at least one other user a selection of a first asset of the identified one or more assets; and
presenting the first asset to the user and the at least one other user for asynchronous consumption.

### Claim 18 (depends on 11)

The method of claim 11, further comprising:
receiving from the user or the at least one other user a selection of a first asset of the identified one or more assets; and
presenting the first asset to the user and the at least one other user for synchronous consumption.

### Claim 19 (depends on 11)

The method of claim 11, wherein the preferences profile of the user includes first filtering criteria and the preferences profile of the at least one other user includes second filtering criteria, and wherein the combined filtering criteria is generated using the first filtering criteria and the second filtering criteria.

### Claim 20 (depends on 11)

The method of claim 11, wherein the recommendation engine includes at least one machine learning (ML) predictive model, the method further comprising:
receiving from a system administrator of the system a corrective input changing the group recommendation to a revised group recommendation; and
improving, by applying machine learning based on the corrective input, a performance of the recommendation engine including the at least one ML predictive model.