Multimodal Content Retrieval With LLM Callbacks for Accurate Generation

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing generative AI systems face challenges in generating consistent, appropriate, and accurate content due to non-deterministic outputs, reliance on ineffective prompts, and the difficulty in handling unstructured content, leading to issues like incohesive summaries and hallucinations, which are exacerbated by parallelization and human errors.

Innovation Solution

A generative AI platform that includes a prompt generator for dynamic prompt creation, uses parallel processing to enhance accuracy, employs self-correcting systems to refine outputs, and transforms unstructured content into structured formats for better analysis and retrieval, utilizing models like LLMs, diffusion models, and 3D engines for multi-modal content generation and retrieval.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If parallel processing is used to enhance accuracy, then content generation accuracy is improved, but computational load increases

Engineering Contradiction:

Improvecontent generation accuracyVSAvoidcomputational load

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system divides content generation into multiple parallel processing paths, where different compute nodes process different aspects or portions of content generation simultaneously. This segmentation enables accuracy improvement through multiple perspectives while managing computational load through distributed processing.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system merges results from multiple parallel processing paths through aggregation and consensus mechanisms. By combining outputs from different compute nodes, the system achieves higher accuracy through ensemble methods while distributing the computational burden across multiple nodes.

Inventive Principle:

Principle #5Merging (Combining)

2Adaptability or versatility

If generative AI models are used for content generation, then content creation capability is improved, but output consistency deteriorates due to non-deterministic behavior

Engineering Contradiction:

Improvecontent creation capabilityVSAvoidoutput consistency

Core Design Contradiction:

Adaptability or versatilityVSStability of the object's composition

Solution Approach 1:

The system implements feedback loops where generated content is evaluated against consistency criteria, and results are fed back to adjust generation parameters. This feedback mechanism maintains output consistency while preserving the creative capabilities of generative AI models by guiding them toward consistent outcomes.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically adjusts generation parameters based on desired consistency levels. By modifying temperature, top-k, and other sampling parameters, the system balances between creative versatility and output consistency, allowing content creation capability to flourish while maintaining stable output characteristics.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If unstructured content is processed directly, then processing speed is maintained, but analysis accuracy deteriorates

Engineering Contradiction:

Improveprocessing speedVSAvoidanalysis accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system performs preliminary structuring of unstructured content before main analysis, organizing data into standardized formats that enhance analysis accuracy. This preliminary action is executed efficiently using optimized parsing algorithms that maintain overall processing speed while improving subsequent analysis precision.

Inventive Principle:

Principle #10Preliminary action

4Reliability

If human review is incorporated to reduce errors, then content accuracy is improved, but processing time increases

Engineering Contradiction:

Improvecontent accuracyVSAvoidprocessing time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system applies human review selectively to only those content pieces that fall below automated quality thresholds or require subjective judgment. This partial application of human review maintains high accuracy for critical content while preserving processing speed for routine content that passes automated checks.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12536416B2Generative artificial intelligence for content generation with searchable repository

Publication Date: 2026.01.27 REVE AI INC

AI summary

A system may include a content repository that stores multi-modal content comprising text, visual content, and/or audio content. The system may include a processor programmed to: access a prompt comprising a text query, a visual query comprising text that describes a visual to be found, and/or an audio query comprising text that describes audio to be found, execute a language model based on the prompt to identify content from the content repository, receive, from the language model, a request for a callback function that seeks additional information to satisfy the multi-modal query, execute the callback function to obtain the additional information and provide the additional information to the language model in response to the request for the callback function, re-execute the language model based on the multi-modal query and the additional information, obtain, from the language model, content responsive to the prompt based on the additional information.