Zero-Shot Model for PCBA Component Description Noise Reduction
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Solution Overview
Problem
The inconsistency in data entry and data quality, along with the lack of uniformity in PCBA component descriptions, leads to excessive noise in component descriptions, hindering advanced analytics teams from gaining insights from component failure data.
Innovation Solution
A zero-shot model is used to identify salient component class descriptions by processing non-standard PCBA component descriptions, filtering out invalid keywords, and assigning validity scores to words based on predefined valid and invalid label designations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional data collection methods are used for PCBA component descriptions, then data can be gathered from multiple manufacturers and repair partners, but noise and inconsistency in descriptions increase, reducing data quality
Solution Approach 1:
The patent introduces an intermediary natural language processing system that acts as a mediator between diverse manufacturer descriptions and the standardized component classification system. This intermediary processes and normalizes the varying descriptions from multiple sources, enabling data aggregation without compromising quality.
Solution Approach 2:
The system changes the parameters of component descriptions by extracting and standardizing key attributes such as component type, function, and specifications. By transforming unstandardized descriptions into structured parameters with validity scores, the system maintains data quality while processing large quantities of descriptions.
2Loss of information
If non-standard component descriptions are processed without filtering, then all available data is retained, but noise prevents effective analytics and insight extraction
Solution Approach 1:
The patent extracts salient features and key information from non-standard component descriptions while leaving out noise and irrelevant details. By identifying and extracting only the meaningful components such as component type, function, and critical specifications, the system retains essential information while eliminating harmful noise.
Solution Approach 2:
The system converts the harmful effect of non-standardized descriptions into a benefit by using the variation in descriptions as training data for validity scoring. The noise and inconsistency are transformed into opportunities to develop and apply natural language processing techniques that identify and score the reliability of different description elements.
3Reliability
If strict standardization is applied to PCBA component descriptions, then data uniformity improves, but the ability to accommodate diverse manufacturer formats decreases
Solution Approach 1:
The patent implements a dynamic system that adapts to different manufacturer formats while working toward standardization. The natural language processing model is trained on diverse formats and can dynamically adjust its extraction and scoring strategies based on the input format, enabling both adaptability and eventual uniformity.
Solution Approach 2:
The system creates a universal processing framework that handles multiple manufacturer formats through a single standardized interface. By developing a multi-functional natural language processing system that can interpret various description styles and output standardized structured data, the system achieves both adaptability to diverse inputs and uniformity in outputs.
4Reliability
If manual review and standardization of component descriptions are performed, then data quality improves, but processing time and resource requirements increase
Solution Approach 1:
The patent replaces manual mechanical review processes with an automated natural language processing system. The AI model automatically extracts, validates, and scores component description elements without human intervention, maintaining high data quality while dramatically improving processing efficiency and reducing resource requirements.
Solution Approach 2:
The system enables self-service processing where the component descriptions are automatically evaluated and scored by the trained natural language processing model. The system serves itself by autonomously identifying salient features, assigning validity scores, and flagging problematic descriptions without requiring manual quality assurance processes.
Data Source
AI summary
Facilitating reduction of noise in non-standard printed circuit board assembly component descriptions using a zero-shot model to identify salient component class descriptions is presented herein. A system receives defined valid label designations(s) representing an accepted domain of component class descriptions; receives defined invalid label designations(s) representing a rejected domain of component class descriptions; and replaces non-alphanumeric characters of respective component descriptions with respective spaces to obtain revised component descriptions, removes, from the revised component descriptions, word(s) that include number(s) to obtain reduced component descriptions, expands, using a defined knowledge base comprising an online library of information, respective words of the reduced component descriptions to obtain respective expanded words representing natural-language expressions of the respective words, and based on the defined valid and invalid label designation(s) and the respective expanded words, selects, using a zero-shot model, words from the reduced component description for inclusion in a final reduced component description.