LLM-Based Object Identity Search System
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Solution Overview
Problem
Current technologies face challenges in querying databases using non-natural language methods and transforming narratives into creative outputs effectively.
Innovation Solution
A machine learning-based processing system that analyzes text-based inputs to extract natural language elements, generates prompts, and uses a trained machine learning model to generate responses, which are then transformed into media content elements corresponding to different aspects of an object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If non-natural language-based methods are used to query databases, then search functionality is provided, but ease of operation deteriorates
Solution Approach 1:
The patent introduces a natural language processing intermediary layer between the user and the database query system. This intermediary translates natural language queries into executable database queries, eliminating the need for users to learn complex query languages while maintaining full database search functionality. The intermediary handles the complexity of query translation automatically.
Solution Approach 2:
The patent replaces mechanical/technical query interfaces with natural language communication. Instead of requiring users to manually construct structured queries using specific syntax and protocols, the system substitutes this mechanical process with natural language understanding, where users simply speak or type their information needs in everyday language.
2Adaptability or versatility
If narratives are transformed into creative outputs, then creative production capability is improved, but ease of operation deteriorates
Solution Approach 1:
The patent implements a self-service creative transformation system where the narrative processing and creative output generation are automated. The system automatically analyzes input narratives, identifies key elements, generates appropriate creative outputs (such as stories, descriptions, or content variations), and presents results without requiring manual intervention or expert knowledge from the user.
Solution Approach 2:
The patent introduces an AI-based intermediary system that bridges the gap between raw narrative input and creative output generation. This intermediary automatically processes narrative structures, identifies thematic elements, and transforms them into diverse creative formats, eliminating the need for users to manually perform the complex transformation process.
3Productivity
If manual processing is used for data analysis and creative generation, then control over the process is maintained, but productivity deteriorates
Solution Approach 1:
The patent replaces manual data analysis and creative generation processes with automated AI systems. The system automatically processes large volumes of data, identifies patterns, generates creative content, and produces results without human intervention, dramatically increasing productivity while the automated nature of the process manages the complexity through algorithmic processing.
Solution Approach 2:
The patent implements self-service automation where the system independently performs data analysis, narrative transformation, and creative output generation. The automated system monitors its own performance, adjusts processing parameters, and generates results without requiring continuous human oversight, thereby maintaining high productivity while managing operational complexity through self-regulation.
Data Source
AI summary
Machine learning based processing systems and techniques are described. In some examples, a machine learning based processing system analyzes text-based input to extract a plurality of natural language elements from the text-based input. The text-based input is associated with an object. The machine learning based processing system generates a prompt from at least a subset of the plurality of natural language elements. The machine learning based processing system analyzes the prompt by using a trained machine learning model to generate a response. The response is responsive to the prompt. The machine learning based processing system analyzes the response to extract a plurality of media content elements from the response. The plurality of media content elements corresponds to different aspects of the object.