Content Classification Correlation for Adaptive Digital Personalization

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Solution Overview

Problem

Existing systems fail to consider content classes and subclasses in adapting digital environments for users, leading to ineffective personalization based on user interactions and psychological profiles.

Innovation Solution

A system that identifies and utilizes correlations between content classifications and psychological profiles to determine predicted user responses, allowing for personalized content presentation by classifying content into hierarchical taxonomical classes and subclasses, and determining master-correlations to adjust digital environments accordingly.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If existing systems adapt digital environments based on user interactions, then user personalization is achieved, but content class and subclass correlations are ignored

Engineering Contradiction:

Improveuser personalizationVSAvoidcontent classification information

Core Design Contradiction:

Adaptability or versatilityVSLoss of information

Solution Approach 1:

The patent segments content into hierarchical classes and subclasses, allowing the system to process and adapt to content at multiple levels of granularity. This segmentation enables the system to maintain content classification information while achieving user personalization by analyzing interactions with specific content categories.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent implements a nested hierarchical structure where content classes contain subclasses, which in turn contain specific content items. This nesting allows the system to simultaneously consider broad content categories and specific content details when adapting digital environments to user preferences.

Inventive Principle:

Principle #7Nested doll (Nesting)

2Measurement precision

If content-specific correlations are determined between pieces of content and psychological parameter values, then personalized content presentation is improved, but system complexity increases

Engineering Contradiction:

Improvepredicted response accuracyVSAvoidcorrelation determination system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent performs preliminary correlation analysis between content classifications and psychological parameter values before actual content presentation. By pre-determining these correlations and storing them in a database, the system reduces real-time computational complexity while maintaining high prediction accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses feedback from user interactions with content to refine and update the correlations between content classes and psychological parameters. This feedback mechanism allows the system to improve prediction accuracy over time while managing complexity through iterative learning rather than complex real-time calculations.

Inventive Principle:

Principle #23Feedback

3Adaptability or versatility

If master-correlations are determined between content classes and psychological parameter strengths, then content presentation adaptability is enhanced, but processing time increases

Engineering Contradiction:

Improvecontent presentation adaptabilityVSAvoidcorrelation processing time

Core Design Contradiction:

Adaptability or versatilityVSLoss of time

Solution Approach 1:

The patent calculates master-correlations between content classes and psychological parameter strengths in advance and stores them in a database. This preliminary computation eliminates the need for time-consuming real-time correlation calculations, allowing the system to quickly adapt content presentation based on pre-computed relationships.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a simplified representation (copy) of the complex relationships between content and psychological parameters through master-correlations. This copied relationship model allows for rapid querying and adaptation without processing the full complexity of individual content-item correlations each time.

Inventive Principle:

Principle #26Copying

Data Source

PatentUS20250342938A1Systems and methods to identify and utilize correlations between content classifications and psychological profiles of users to provide an adaptable digital environment

Publication Date: 2025.11.06 SOLSTEN INC

AI summary

Systems and methods to identify and utilize correlations between content classifications and psychological profiles of users to provide an adaptable digital environment are disclosed. Exemplary implementations may: obtain interaction information; determine, based on the interaction information, content-specific correlations between psychological parameter value(s) and/or the individual psychological profiles of individual users and individual pieces of content; determine content classes and content subclasses that characterize the individual pieces of content; determine, based on the content-specific correlations, master-correlations between the content class(es) and/or the content subclass(es) and a strength of the individual one or more psychological parameter values relative to other ones of the psychological parameter values included in the psychological profiles; determine individual predicted responses to the individual pieces of content based on strengths of the master-correlations, the psychological profiles of the master-correlations, and the interaction information; identify and present prospective pieces of content for the users based on the predicted responses.