Manufacturing Instruction Generation From Annotated Text and Image Data
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Solution Overview
Problem
Existing methods for generating manufacturing instructions from hybrid textual and image data are laborious and inaccurate due to varying designer terminologies, requiring frequent manual intervention and correction.
Innovation Solution
A system and method that utilizes a language model to generate textual instructions for manufacturers by extracting digital character data from annotated files, calculating an extraction quality score, and determining an interrogator output to produce accurate manufacturing instructions using a geometric model and language model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual methods are used to generate manufacturing instructions from designer files, then flexibility in handling various designer terminologies is maintained, but the process becomes laborious and inaccurate requiring frequent manual intervention
Solution Approach 1:
The patent introduces an intermediary system comprising OCR technology, natural language processing models, and terminology mapping mechanisms that translate designer files into manufacturing instructions. This intermediary layer automatically handles the conversion process, resolving the contradiction by providing both automation and maintained accuracy through specialized translation components.
Solution Approach 2:
The system transforms the parameters of the design file by converting various designer terminologies and formats into standardized manufacturing instruction parameters. Through parameter transformation and normalization, the system achieves automated processing while maintaining reliability across different design inputs.
2Manufacturing precision
If standardized terminology is imposed on designer inputs, then manufacturing instructions become more consistent, but designers lose flexibility in expressing their intent
Solution Approach 1:
The terminology mapping system is dynamic and adaptive, allowing it to learn and accommodate different designer terminologies while outputting standardized manufacturing instructions. The system can be trained on specific designer vocabularies and adjusts its mapping accordingly, maintaining both consistency in output and flexibility in input acceptance.
Solution Approach 2:
The system performs preliminary terminology standardization and validation during the translation process, resolving terminology variations before generating final instructions. This preliminary processing ensures consistency in the output while allowing designers to use their preferred terminologies in the input files.
3Reliability
If manual review and correction of generated instructions is performed, then instruction accuracy improves, but production time increases
Solution Approach 1:
The system incorporates feedback mechanisms where generated instructions are automatically validated against the original design file, and correction patterns from manual reviews are fed back into the system to improve future automated generation. This feedback loop maintains high accuracy while reducing the need for manual intervention over time.
Solution Approach 2:
The system applies selective manual review only to critical sections or low-confidence generations rather than reviewing entire instruction sets. This partial action approach maintains productivity by automating most of the process while applying human oversight only where necessary to ensure accuracy.
Data Source
AI summary
A system for generating textual instructions for manufacturers from hybrid textual and image data includes a manufacturing instruction generator that may generate a language processing module from a first training set including at least a training annotated file describing at least a first product to manufacture, the at least an annotated file containing one or more textual data, and at least an instruction set containing one or more manufacturing instructions to manufacture the at least a first product. Manufacturing instruction generator may use the language processing to generate textual instructions for manufacturers from at least an annotated file and may initiate manufacture using the generated manufacturing instructions.