Iterative Neural Attention Training for Adaptive Semantic Generation

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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 and method that enables automatic adaptation and generation of attention and reflection streams, incorporating fuzzy content networks and adaptive recommendations to enhance communication with metaphorical constructs and wit.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Extent of automation

If existing semantic-based approaches (e.g., RDF) are used, then semantic structure and meaning can be represented, but significant manual effort is required and the system does not adapt to usage

Engineering Contradiction:

Improveautomatic adaptation to usageVSAvoidmanual configuration complexity

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The neural network system automatically learns and adapts to usage patterns without requiring manual configuration or programming of semantic rules. The system serves itself by autonomously extracting meanings, relationships, and contextual information from data, eliminating the need for manual semantic annotation and system reconfiguration.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically changes its operational parameters and semantic representations based on usage patterns and contextual data. The neural network adjusts its internal representations, attention mechanisms, and processing parameters in real-time to adapt to varying usage scenarios, transforming static semantic structures into dynamic, context-aware models.

Inventive Principle:

Principle #35Parameter changes

2Adaptability or versatility

If existing semantic-based systems are used, then structured communication can be achieved, but they lack self-awareness, imagination, introspection, and creative communication capabilities

Engineering Contradiction:

Improvecreative communication capabilityVSAvoidlack of self-awareness and introspection

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The system incorporates feedback mechanisms where the neural network monitors its own processing, usage patterns, and contextual responses. This feedback loop enables the system to develop self-awareness by observing its own operations, introspect on its decision-making processes, and continuously refine its creative communication capabilities based on observed outcomes and contextual nuances.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system transitions from static semantic representations to dynamic, evolving models that can imagine and create new semantic structures. The neural network dynamically generates creative communications by combining existing concepts in novel ways, adapting its semantic framework based on contextual requirements and usage patterns, thereby achieving both creativity and self-awareness.

Inventive Principle:

Principle #15Dynamics

3Productivity

If manual semantic configuration is performed, then initial semantic structure can be established, but the system cannot automatically adapt to evolving usage patterns

Engineering Contradiction:

Improveautomatic adaptation speedVSAvoidtime required for manual reconfiguration

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary learning and adaptation during initial deployment, automatically extracting semantic structures and usage patterns from data. This preliminary action establishes a foundation that enables continuous automatic adaptation without requiring manual reconfiguration, significantly reducing the time needed for system updates and adaptations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The neural network system continuously learns and adapts to usage patterns in real-time, maintaining productive adaptation without interruption. The system persists in learning from ongoing usage data, ensuring that semantic representations and communication capabilities evolve continuously without the need for manual reconfiguration cycles, thereby eliminating time losses associated with manual updates.

Inventive Principle:

Principle #20Continuity of useful action

Data Source

PatentUS12586001B1Iterative attention-based neural network training and processing

Publication Date: 2026.03.24 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.