Digital Content Curation With Namespace-Based Relevancy Timing

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

The challenge of identifying relevant digital content for users amidst the vast amount of available content has become increasingly difficult due to the proliferation of digital content and connected sensors.

Innovation Solution

A method and system that utilize a multi-dimensional namespace to compare structured content attributes with event attributes, employing predictive models and machine learning techniques to determine relevancy, and generate curated content for rendering at contextually relevant times, incorporating feedback from users and crowdsourcing to improve model accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If the volume of digital content increases to provide more information, then the quantity of available content improves, but the difficulty of identifying relevant content worsens

Engineering Contradiction:

Improvevolume of digital contentVSAvoiddifficulty of identifying relevant content

Core Design Contradiction:

Quantity of substanceVSDifficulty of detecting and measuring

Solution Approach 1:

The patent replaces manual content filtering and search mechanisms with automated machine learning models and predictive algorithms. The system automatically analyzes user data, event attributes, and content metadata to generate curated content, eliminating the need for users to manually search through vast amounts of digital content.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service by automatically curating and personalizing content based on user profiles and contextual data. The machine learning models continuously learn from user interactions and automatically adjust content recommendations without requiring manual user intervention or search efforts.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If manual content filtering is used to identify relevant content, then the precision of content selection improves, but the time required for content delivery worsens

Engineering Contradiction:

Improveprecision of content selectionVSAvoidtime required for content delivery

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary actions by pre-processing and structuring content metadata, building user profiles, and organizing content libraries in advance. Machine learning models are trained beforehand to recognize patterns and relationships, enabling rapid content retrieval and curation when needed without time-consuming manual filtering.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Manual content filtering is replaced with automated machine learning-based content analysis and selection. The system uses predictive models to automatically assess content relevance based on user data and contextual information, achieving both high precision and fast delivery times simultaneously.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Ease of operation

If generic content is provided to all users, then the ease of content provision improves, but the relevancy of content to individual users worsens

Engineering Contradiction:

Improveease of content provisionVSAvoidrelevancy of content to users

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system applies local quality by customizing content recommendations for each individual user based on their unique profile, preferences, and contextual data. Instead of providing uniform generic content, the machine learning models generate personalized content curation for each user, making content highly relevant to individual needs while maintaining automated provision processes.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically changes content parameters such as topic, format, timing, and delivery channel based on user-specific attributes and contextual factors. Machine learning models adjust these parameters automatically to optimize content relevancy for each user while maintaining efficient automated content provision systems.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12511582B2Curation and provision of digital content

Publication Date: 2025.12.30 NANT HOLDINGS IP LLC

AI summary

A method includes accessing a structured content item from a first database and event data from a second database, the event data including sets of event attributes in a multi-dimensional namespace and associated with a respective point in time; determining a relevancy profile characterizing a metric of relevancy of the structured content item over a respective time interval, the metric of relevancy including a distance in the multi-dimensional namespace between attributes associated with the structured content and the sets of event attributes; generating, using the relevancy profile, second digital content including a subset of the structured content item; and providing the second digital content for rendering on a device. Related apparatus, systems, techniques and articles are also described.