Style Rule Generation via Machine Learning Analysis

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Solution Overview

Problem

Establishing and updating style rules for entities, such as organizations, typically requires multiple communications between users, consuming power, processing resources, and network overhead.

Innovation Solution

A system that uses a machine learning model to automatically generate and modify style rules by analyzing a plurality of files associated with an entity, reducing the need for user interactions and communications.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If manual communication between users is used to establish and update style rules, then user control and understanding are improved, but power consumption, processing resources, and network overhead increase

Engineering Contradiction:

Improveuser controlVSAvoidpower consumption

Core Design Contradiction:

Ease of operationVSUse of energy by moving object

Solution Approach 1:

The system enables self-service by allowing the style guide system to automatically generate and update style rules without requiring manual user communications. The machine learning model processes files and autonomously determines style rule modifications, eliminating the need for users to engage in multiple communications while maintaining rule accuracy and consistency.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical system of manual user communications with an automated computational system. The machine learning model substitutes human-to-human communication processes with algorithmic analysis, reducing network overhead and processing resources while maintaining the ability to establish and update style rules effectively.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If multiple user communications are used to update style rules, then rule accuracy is improved, but network overhead and processing resources increase

Engineering Contradiction:

Improverule accuracyVSAvoidnetwork overhead

Core Design Contradiction:

Measurement precisionVSLoss of energy

Solution Approach 1:

The patent replaces the mechanical system of multiple user communications with an automated computational system. The machine learning model substitutes human-to-human communication processes with algorithmic analysis, reducing network overhead and processing resources while maintaining the ability to establish and update style rules effectively.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system implements feedback mechanisms where the machine learning model continuously analyzes files and automatically adjusts style rules based on learned patterns. This automated feedback loop maintains rule accuracy without requiring additional user communications, as the system self-corrects and refines rules through iterative processing.

Inventive Principle:

Principle #23Feedback

3Productivity

If automated machine learning is used to generate style rules, then productivity is improved, but device complexity increases

Engineering Contradiction:

Improverule generation efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The style guide system performs multiple functions through a single integrated machine learning model: it analyzes files, generates style rules, updates existing rules, and ensures consistency across all materials. This multi-functionality improves productivity by consolidating what would otherwise require multiple separate processes into one automated system.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20250139187A1Automatically generating and modifying style rules

Publication Date: 2025.05.01 CAPITAL ONE SERVICES LLC

AI summary

In some implementations, a style system may receive, from a repository, a plurality of files associated with an entity. The style system may apply a machine learning model to the plurality of files to determine a set of rules associated with images or text included in the plurality of files. The style system may generate a document that indicates the set of rules and may output, to a user device, the document.