Machine Learning Database Updates for Accessible Content Resources
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Solution Overview
Problem
Entities face challenges in maintaining consistent design and accessibility standards across extensive repositories of content resources due to the difficulty in managing diverse file types and structures, leading to inconsistent updates and confusion for users with impairments.
Innovation Solution
A system utilizing machine learning models to generate and modify creative content resources, adhering to entity-specific rules and standards, such as branding guidelines, to ensure timely updates and consistency across databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entities manually update content repositories to reflect accessibility guidelines, then design consistency and accessibility compliance can be maintained, but the extensive scope and diverse file types make updates timely and complete
Solution Approach 1:
The patent replaces manual mechanical updating processes with an automated machine learning system that processes content resources. The ML model automatically identifies and applies accessibility guidelines to diverse file types, eliminating the need for manual review and update of each content item while ensuring consistent compliance across the entire repository.
Solution Approach 2:
The system enables content resources to self-update by embedding executable code within content items that automatically retrieves and applies accessibility guideline updates. When guidelines change, the embedded code allows affected content resources to autonomously modify themselves to comply with new standards without human intervention.
2Adaptability or versatility
If entities maintain extensive repositories of content resources with diverse file types and structures, then comprehensive content coverage is achieved, but management difficulty and inconsistency increase
Solution Approach 1:
The patent creates a universal management system that handles multiple file types and structures through a single automated ML-based platform. The system universally processes diverse content resources regardless of their specific format or structure, applying consistent accessibility standards across all types of content without requiring separate management processes for each file type.
Solution Approach 2:
The system changes the state of content resources by automatically modifying their parameters (such as embedding executable code, updating accessibility attributes) to ensure compliance. This parameter transformation approach allows the system to manage diverse file types by standardizing their accessibility-related parameters through automated processing.
3Manufacturing precision
If designers manually redesign each file to comply with accessibility guidelines, then design quality and consistency can be ensured, but productivity and efficiency decrease
Solution Approach 1:
The patent replaces manual designer intervention with an automated machine learning system that ensures design consistency. The ML model consistently applies accessibility guidelines across all content resources without variation, eliminating human error and inconsistency while maintaining high design quality through algorithmic precision.
Solution Approach 2:
The system performs preliminary actions by pre-processing content resources to identify those affected by guideline changes before actual updates are needed. This preliminary identification and preparation allows for efficient batch processing and ensures that updates are applied systematically across the entire repository rather than reactively on a file-by-file basis.
Data Source
AI summary
Methods and systems for modifying a database of creative content resources using machine learning models. In some aspects, a system may be used to generate new resources and/or modify a subset of resources of the database. The system accesses the database and obtains data indicative of elements and (2) structural specifications for each resource. The system obtains and inputs (1) a user prompt for generating a new creative content resource and (2) a set of rules indicative of standardized assets and structural specifications into a machine learning model to obtain the new creative content resource. The system obtains an indication for replacing a recurring asset included in the new creative content resource with a replacement asset and replaces the recurring asset with the replacement asset in each creative content resource of a subset of creative content resources from the database.