Contextual Semantic Summarization Through Iterative Chunk Refinement

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Solution Overview

Problem

Existing generative AI systems face challenges such as non-deterministic outputs, difficulty in formulating effective prompts, inconsistent content generation, and susceptibility to human and inherent errors, which complicate content processing and retrieval.

Innovation Solution

A system utilizing a generative AI platform with dynamic prompt generation, multi-modal content generation, and self-correction mechanisms, including a prompt generator, callback functions, and computer models like language, image, and 3D engines, to enhance content generation, storage, and retrieval, addressing issues of prompt effectiveness and consistency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If generative AI systems are used for content generation, then content creation capability is improved, but output consistency and reliability deteriorate due to non-deterministic outputs

Engineering Contradiction:

Improvecontent creation capabilityVSAvoidoutput consistency

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system implements feedback loops where the consistency evaluation model assesses generated content against reference content and provides feedback signals. This feedback is used to adjust generation parameters and re-generate content until consistency thresholds are met, transforming the non-deterministic generative process into a controlled iterative refinement system that maintains both creativity and consistency.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent replaces traditional deterministic content generation mechanisms with AI-based evaluation and comparison systems. Instead of relying on fixed rules or manual review, the system uses machine learning models to automatically evaluate consistency, compare generated content with reference material, and guide regeneration, substituting mechanical/deterministic processes with intelligent adaptive systems.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If complex prompt engineering is implemented to improve content quality, then content accuracy is improved, but system complexity and operational difficulty increase

Engineering Contradiction:

Improvecontent accuracyVSAvoidprompt formulation difficulty

Core Design Contradiction:

Manufacturing precisionVSEase of operation

Solution Approach 1:

The system performs self-service by automatically generating, evaluating, and refining prompts without requiring extensive manual intervention. The consistency evaluation model and generation system work together autonomously to adjust prompts based on feedback from content comparison, reducing the need for complex manual prompt engineering while maintaining high content accuracy.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary actions by pre-evaluating generated content against consistency criteria before final output. The multi-stage generation and evaluation process prepares and refines content in advance, ensuring accuracy is achieved through systematic preliminary checks rather than complex post-hoc prompt adjustments.

Inventive Principle:

Principle #10Preliminary action

3Speed

If multiple compute nodes are used to distribute content generation tasks, then processing speed is improved, but output consistency across nodes deteriorates

Engineering Contradiction:

Improvecontent generation speedVSAvoidcross-node consistency

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The system enforces homogeneity across distributed compute nodes by implementing a centralized consistency evaluation model that all nodes reference. This model ensures that regardless of which node generates content, the same consistency criteria and reference comparisons are applied, standardizing output quality and consistency across the distributed system while maintaining parallel processing benefits.

Inventive Principle:

Principle #33Homogeneity

4Manufacturing precision

If extensive content review and correction processes are implemented, then content quality is improved, but processing time and computational resources increase

Engineering Contradiction:

Improvecontent qualityVSAvoidprocessing time

Core Design Contradiction:

Manufacturing precisionVSLoss of time

Solution Approach 1:

The system applies partial review actions by focusing evaluation efforts on specific consistency-critical aspects of generated content rather than exhaustive review of all content. The consistency evaluation model targets key comparison points against reference material, performing sufficient verification to ensure quality without the overhead of complete content re-review, thus reducing processing time while maintaining quality standards.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12437188B2Systems and methods for contextual and semantic summarization

Publication Date: 2025.10.07 REVE AI INC

AI summary

A system may, in a first pass: divide content to be summarized into a plurality of chunks and, for each chunk: execute a language model with the chunk and an instruction to summarize the chunk, generate, based on the executed language model, a summary of the chunk. In a subsequent pass, the system may: generate a plurality of groups of summaries, each group of summaries from among the plurality of groups of summaries comprising two or more summaries, each summary corresponding to a respective chunk, for each group of summaries from among the plurality of groups: execute a language model with the group of summaries and an instruction to summarize the group of summaries, generate, based on the executed language model on the group of summaries, a group summary. The system may iteratively repeat the subsequent pass for group summaries until a summary of the content is reached.