# US 12,192,583 B2 · Curating narrative experiences through automated content compilation

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- Number: US 12,192,583 B2
- Document ID: US12192583B2
- Status: Granted
- Kind: B2
- Application number: 17/968,738
- Filed: 2022-10-18
- Published: 2023-02-09
- Granted: 2025-01-07
- Earliest claimed: 2020-12-14
- Assignees: Disney Enterprises, Inc.
- Applicants: Disney Enterprises, Inc.
- Inventors: Adam D. Eivy; Katharine S. Navarre; Ricky Kane Stapler
- Web page: https://patentgrep.com/patent/US12192583B2

## CPC

- H04N 21/252: {Processing of multiple end-users' preferences to derive collaborative data}
- H04N 21/466: Learning process for intelligent management, e.g. learning user preferences for recommending movies {(services using the results of monitoring in broadcast systems H04H60/61)}
- H04N 21/25883: {being end-user demographical data, e.g. age, family status or address (arrangements for identifying locations of users in broadcast systems H04H60/52)}
- H04N 21/25891: {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)}
- H04N 21/4532: {involving end-user characteristics, e.g. viewer profile, preferences (monitoring of user activities for profile generation for accessing a video database G06F16/739; user profiles in network data switching protocols H04L67/306; processing of user preferences or user profiles in wireless networks H04W8/18)}
- H04N 21/4755: {for defining user preferences, e.g. favourite actors or genre (retrieval personalisation and generation of user profiles for the retrieval of video data G06F16/739; user profiles in network data switching protocols H04L67/306)}
- H04N 21/8133: {specifically related to the content, e.g. biography of the actors in a movie, detailed information about an article seen in a video programme}
- H04N 21/8549: Creating video summaries, e.g. movie trailer {(retrieval in video databases by using presentations in form of a video summary G06F16/739)}
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION

## Abstract

A content compilation system includes a computing platform having a hardware processor and a memory storing a software code configured to provide an editorial interface. The hardware processor executes the software code to receive compilation authoring data via the editorial interface, identify one or more end-user(s) for receiving a content compilation, access a consumption profile of the end-user(s), obtain, using the consumption profile and a first authoring criterion in the compilation authoring data, content items from one or more content sources. The software code further aggregates, using a second authoring criterion in the compilation authoring data, the content items into content subsets, groups, using a third authoring criterion, at least some of the content subsets to produce the content compilation, computes a desirability score predicting the desirability of the content compilation to the end-user(s), and provides, when the desirability score satisfies a predetermined threshold, the content compilation to the end-user(s).

## Claims

### Claim 1

A content compilation system comprising:
a computing platform having a hardware processor and a system memory;
a compilation authoring template generated using a trained-machine learning model and stored in the system memory, wherein the trained machine learning model is configured to generate the compilation authoring template implementing an editorial style of a human editor; and
a software code stored in the system memory;
the hardware processor configured to execute the software code to:
obtain a plurality of content items from at least one content source;
aggregate the plurality of content items, based on relevance to one another, into a plurality of content subsets, according to a respective one of a plurality of narrative stories of each of the plurality of content subsets;
produce a content compilation, using the compilation authoring template and by concatenating the plurality of content subsets, to create a narrative experience having a narrative arc determined by a sequence in which the plurality of content subsets are combined to produce the content compilation; and
provide the content compilation to one or more end-users.

### Claim 2 (depends on 1)

The content compilation system of claim 1, wherein the content compilation is individualized for the one or more end-users based on a profile of the one or more end-users.

### Claim 3 (depends on 1)

The content compilation system of claim 1, wherein the content compilation is unique to each of the one or more end-users.

### Claim 4 (depends on 1)

The content compilation system of claim 1, wherein the hardware processor is further configured to execute the software code to:
determine a first desirability score predicting a first desirability of the content compilation to the one or more end-users.

### Claim 5 (depends on 4)

The content compilation system of claim 4, wherein the first desirability score comprises an end-user relevance score.

### Claim 6 (depends on 4)

The content compilation system of claim 4, wherein the hardware processor is further configured to execute the software code to:
determine a second desirability score predicting a second desirability of a second content compilation to the one or more end-users; and
provide the second content compilation to the one or more end-users, when the second desirability score satisfies a predetermined threshold.

### Claim 7 (depends on 1)

The content compilation system of claim 1, wherein the hardware processor is further configured to execute the software code to:
receive feedback data, wherein the feedback data rates an actual desirability of the content compilation to the one or more end-users; and
modify, using the trained machine learning model and the feedback data, the compilation authoring template to improve a performance by the content compilation system.

### Claim 8

A method for use by a content compilation system including a computing platform having a hardware processor and a system memory storing a software code and a compilation authoring template generated using a trained machine learning model, wherein the trained machine learning model is configured to generate the compilation authoring template implementing an editorial style of a human editor, the method comprising:
obtaining, by the software code executed by the hardware processor, a plurality of content items from at least one content source;
aggregating, by the software code executed by the hardware processor, the plurality of content items, based on relevance to one another, into a plurality of content subsets, according to a respective one of a plurality of narrative stories of each of the plurality of content subsets;
producing a content compilation, by the software code executed by the hardware processor, using the compilation authoring template and by concatenating the plurality of content subsets, to create a narrative experience having a narrative arc determined by a sequence in which the plurality of content subsets are combined to produce the content compilation; and
providing, by the software code executed by the hardware processor, the content compilation to one or more end-users.

### Claim 9 (depends on 8)

The method of claim 8, wherein the content compilation is individualized for the one or more end-users based on a profile of the one or more end-users.

### Claim 10 (depends on 8)

The method of claim 8, wherein the content compilation is unique to each of the one or more end-users.

### Claim 11 (depends on 8)

The method of claim 8, further comprising:
determining, by the software code executed by the hardware processor, a first desirability score predicting a first desirability of the content compilation to the one or more end-users.

### Claim 12 (depends on 11)

The method of claim 11, wherein the first desirability score comprises an end-user relevance score.

### Claim 13 (depends on 11)

The method of claim 11, further comprising:
determining, by the software code executed by the hardware processor, a second desirability score predicting a second desirability of a second content compilation to the one or more end-users; and
providing the second content compilation to the one or more end-users, by the software code executed by the hardware processor, when the second desirability score satisfies a predetermined threshold.

### Claim 14 (depends on 8)

The method of claim 8, further comprising:
receiving, by the software code executed by the hardware processor, feedback data, wherein the feedback data rates an actual desirability of the content compilation to the one or more end-users; and
modifying, by the software code executed by the hardware processor, using the trained machine learning model and the feedback data, the compilation authoring template to improve a performance by the content compilation system.