Generative AI Structured Data Extraction for Mixed Reality
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for extracting structured data from unstructured documents, such as instruction manuals, are manual or expensive, prone to errors, and inefficient for use in mixed reality applications.
Innovation Solution
A software and hardware facility utilizing generative artificial intelligence models to convert unstructured instructional content into structured data, following a specified schema, for use in mixed reality applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manual extraction techniques are used to convert unstructured instructional content into structured data, then data extraction can be performed with simple tools, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical extraction processes with an automated system comprising a generative AI model and a parser. The generative AI model automatically converts unstructured instructional content into structured data formats, eliminating the need for manual data entry and processing while significantly reducing extraction time and human effort.
2Ease of manufacture
If traditional data extraction methods are used, then implementation is straightforward, but errors are frequent and data quality is poor
Solution Approach 1:
The patent replaces error-prone manual extraction methods with an automated generative AI-based system. The AI model generates structured data with high accuracy by understanding the semantic meaning of instructional content, while the parser ensures proper formatting and validation, significantly improving data quality and reducing errors compared to traditional methods.
Solution Approach 2:
The system incorporates feedback mechanisms where the parser validates the generated structured data and can request corrections or refinements from the generative AI model. This iterative feedback process ensures high data accuracy and reliability while maintaining ease of implementation through automated error correction.
3Reliability
If extensive processing is applied to extract structured data from unstructured content, then data quality improves, but computation resources and time increase significantly
Solution Approach 1:
The patent extracts only the essential structured data elements needed for mixed reality applications from the unstructured instructional content using the generative AI model. Rather than performing exhaustive processing on all content, the system selectively extracts relevant information such as task steps, parameters, and instructions, improving data quality while minimizing unnecessary computation and resource consumption.
4Device complexity
If manual data extraction is performed, then hardware requirements are minimal, but productivity and output volume are limited
Solution Approach 1:
The patent replaces manual extraction processes with an automated generative AI system that can process large volumes of unstructured instructional content simultaneously. This substitution enables high productivity and substantial data output without proportionally increasing hardware complexity, as the system leverages efficient AI model architecture and processing algorithms.
Data Source
AI summary
Systems and methods for generating structured data for mixed reality applications using generative artificial intelligence are disclosed. Unstructured data including instructional content is received. A schema to which the unstructured data is to conform is established. Then, a system task prompt based on the unstructured instructional content and the schema input is established. The system task prompt is provided to a generative artificial intelligence model. Structured data is received from the generative artificial intelligence model. A procedure usable in a mixed reality application is constructed using the structured data.