Digital Content Curation Using Event-Based Relevancy Profiles

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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 digitally connected sensors and computing technology.

Innovation Solution

A system and method that utilizes a multi-dimensional namespace to compare structured content attributes with event attributes using predictive models, determining a relevancy profile to curate and provide digital content at contextually relevant times, incorporating machine learning techniques and user feedback to improve relevance determination.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Quantity of substance

If the volume of digital content increases to provide more comprehensive 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 analytics. The system uses algorithms to automatically analyze user data, event attributes, and content relevance, substituting human cognitive effort with computational processing to identify relevant content in large volumes of digital information.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service content curation by automatically generating personalized content recommendations without requiring active user search or selection. The machine learning models continuously analyze user behavior, event data, and content attributes to autonomously curate and deliver relevant content, allowing users to passively receive customized information streams.

Inventive Principle:

Principle #25Self-service

2Measurement precision

If manual content filtering is used to ensure relevance, then the accuracy of content selection improves, but the time required for content delivery worsens

Engineering Contradiction:

Improveaccuracy of content relevanceVSAvoidtime for content delivery

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs preliminary content analysis and relevance assessment in advance by continuously training machine learning models on user data and event attributes. Content is pre-filtered, pre-ranked, and pre-curated based on predicted user interests and upcoming events, so that when users need content, it is already prepared and ready for immediate delivery without real-time processing delays.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent dynamically adjusts content relevance parameters and model weights based on changing user preferences, event contexts, and temporal factors. The system modifies filtering criteria, relevance thresholds, and model parameters in response to user feedback and evolving conditions, maintaining high accuracy while adapting to different scenarios without requiring manual reconfiguration.

Inventive Principle:

Principle #35Parameter changes

3Device complexity

If generic content recommendation systems are used, then the device complexity remains low, but the personalization and relevance of content worsens

Engineering Contradiction:

Improvesystem complexityVSAvoidpersonalization of content

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The patent implements a universal machine learning framework that handles multiple content types, user profiles, and event categories through a single flexible system. The same core algorithms and models serve diverse functions including content filtering, recommendation generation, temporal analysis, and personalization across different domains, reducing the need for separate specialized systems while maintaining high adaptability.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system incorporates continuous feedback loops where user interactions, content consumption patterns, and explicit feedback are fed back into the machine learning models to refine personalization. This feedback mechanism enables the system to adapt and improve content recommendations over time based on actual user behavior, enhancing personalization without requiring complex manual configuration or intervention.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260057306A1Curation And Provision Of Digital Content

Publication Date: 2026.02.26 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.