Automated Content Compilation System for Personalized Media Curation
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Solution Overview
Problem
Conventional methods for presenting digital media content fail to efficiently guide consumers to related or complementary content, overwhelming them with numerous options and requiring manual aggregation, which is resource-intensive and inefficient.
Innovation Solution
An automated content compilation system that uses a trained machine learning model to curate narrative experiences by aggregating relevant digital media content based on user consumption profiles, predicting desirability scores, and proactively delivering personalized content compilations without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional synopses are used to describe content items in isolation, then consumers can review individual content descriptions, but the system fails to provide guidance with respect to related or complementary content and cannot aggregate numerous varied content items
Solution Approach 1:
The patent introduces an automated content compilation system as an intermediary between content producers and consumers. This system aggregates numerous varied content items into curated compilations, filtering and organizing the overwhelming amount of available content into manageable, relevant collections that guide consumers to related content without requiring manual aggregation by either party.
Solution Approach 2:
The system performs self-service by automatically aggregating and curating content compilations based on consumer profiles and consumption patterns. Rather than requiring manual intervention to organize content, the system autonomously processes content items, identifies relationships, and creates personalized compilations, freeing both consumers and producers from manual aggregation tasks.
2Productivity
If manual aggregation and curation of content is performed, then content can be organized and reviewed, but significant human resources are consumed and efficiency is reduced
Solution Approach 1:
The patent replaces manual mechanical processes of content aggregation and curation with an automated computational system. The system uses algorithms to process content metadata, identify relationships between content items, and generate compilations, eliminating the need for human editors to manually review and organize content while significantly increasing curation efficiency and reducing time investment.
Solution Approach 2:
The content compilation system performs self-service by automatically organizing content based on consumer profiles and consumption patterns. The system continuously learns from user behavior data to refine its aggregation algorithms, creating increasingly accurate and relevant content compilations without requiring ongoing manual intervention or human resources.
3Quantity of substance
If numerous varied content items are aggregated for review, then comprehensive content coverage is achieved, but the individual content items become too numerous and varied to be effectively aggregated and reviewed
Solution Approach 1:
The patent segments the vast quantity of varied content items into organized compilations based on identified relationships and consumer preferences. Rather than presenting all content items individually, the system divides them into thematic groups and curated collections that maintain comprehensive coverage while making the content more manageable and easier to review through structured organization.
Solution Approach 2:
The automated content compilation system serves as an intermediary that processes and organizes the overwhelming quantity of content items into manageable compilations. This intermediary layer filters and structures the content based on relationships and consumer profiles, transforming the unmanageable mass of individual items into organized, reviewable collections that maintain comprehensive coverage.
Data Source
AI summary
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).