Iterative Attention-Based Training for Adaptive Syntax Generation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing semantic-based approaches require significant manual effort and do not adapt to usage, lacking self-awareness, imagination, introspection, and creative communication capabilities.

Innovation Solution

A processor-based system that generates adaptive recommendations using fuzzy content networks and user behavior analysis to create engaging communications with self-awareness, imagination, and creative constructs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If existing semantic-based approaches (e.g., RDF) are used, then semantic structure and data organization are improved, but manual effort requirements increase significantly and adaptability to usage is lost

Engineering Contradiction:

Improveadaptability to usageVSAvoidmanual effort

Core Design Contradiction:

Adaptability or versatilityVSExtent of automation

Solution Approach 1:

The system enables semantic models to self-update and self-adapt by automatically learning from usage patterns. The neural network monitors interactions and autonomously modifies the semantic model without requiring manual intervention, allowing the system to serve itself in maintaining and evolving its semantic structure based on actual usage behavior

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback mechanisms where usage data is continuously fed back into the neural network to refine and update the semantic model. This closed-loop process allows the semantic structure to adapt dynamically to changing usage patterns, resolving the contradiction between maintaining semantic structure and achieving adaptability

Inventive Principle:

Principle #23Feedback

2Adaptability or versatility

If existing semantic-based systems are used, then data organization is improved, but capabilities for self-awareness, imagination, introspection, and creative communication are lost

Engineering Contradiction:

Improvecreative communication capabilityVSAvoidsystem architecture

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent merges traditional semantic web technologies with deep learning neural networks into a unified system. This combination integrates the structured data organization capabilities of semantic approaches with the creative and adaptive capabilities of neural networks, achieving both data organization and creative communication without requiring entirely separate systems

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The hybrid system provides multi-functionality by enabling the same platform to perform data organization, pattern recognition, creative generation, and adaptive learning. The neural network serves multiple purposes including understanding semantic structures, generating creative content, and learning from usage patterns, thereby achieving versatile capabilities without proportionally increasing complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12462197B1Iterative attention-based neural network training and processing

Publication Date: 2025.11.04 FLINN STEVEN D

AI summary

An iterative attention-based neural network training and processing method and system iteratively applies a focus of attention of a trained neural network on syntactical elements and generates probabilities associated with representations of the syntactical elements, which in turn inform a subsequent focus of attention of the neural network, resulting in updated probabilities. The updated probabilities are then applied to generate syntactical elements for delivery to a user. The user may respond to the delivered syntactical elements, providing additional training information to the trained neural network.