Neural Network Sign Language Identification
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Solution Overview
Problem
Current systems lack an efficient method to analyze and identify new signs in sign language, leading to slow adoption and multiple variations for new terms, as they rely on human review and are limited by the volume of data that can be processed, and struggle to contextualize gestures in visual media files.
Innovation Solution
A method and system using a neural network to analyze sign language data, incorporating natural language processing and pattern recognition to identify new signs by correlating finger-spelled words with sign structures, and determining the prevalence of new signs through metadata and user interactions, thereby aggregating and validating new signs for inclusion in sign language dictionaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human review is used to identify new signs, then accuracy of sign identification is maintained, but processing speed and volume of data analyzed are limited
Solution Approach 1:
The patent replaces the mechanical human review process with an automated neural network system. The neural network analyzes visual media files to identify new signs, substituting human cognitive processing with computational algorithms that can process large volumes of data at high speed while maintaining identification accuracy through trained pattern recognition models.
Solution Approach 2:
The system enables self-service by automatically identifying and validating new signs without requiring continuous human intervention. The neural network autonomously processes data, identifies patterns, and determines when new signs have reached sufficient prevalence for dictionary inclusion, reducing dependency on human reviewers while maintaining quality standards.
2Reliability
If manual analysis of sign language data is performed, then contextual accuracy is maintained, but time required for processing increases
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network on extensive corpora of sign language data before actual analysis begins. This pre-training enables the system to quickly and accurately identify new signs in context without requiring time-consuming manual analysis during the actual processing phase, as the model already understands sign language patterns and contexts.
Solution Approach 2:
The system substitutes manual contextual analysis with automated neural network processing that can simultaneously evaluate multiple visual media files for contextual accuracy. The neural network analyzes patterns, gestures, and contexts at machine speed, dramatically reducing processing time while maintaining the reliability of contextual accuracy through sophisticated pattern recognition.
3Productivity
If comprehensive data processing is implemented, then identification of new signs improves, but system complexity increases
Solution Approach 1:
The patent extracts the core identification function from the complex system by using a specialized neural network module that focuses solely on detecting new signs. This extracted function can be integrated into existing translation software without requiring complete system redesign, thereby achieving improved identification capability while managing complexity through modular architecture.
Solution Approach 2:
The neural network system is designed with multi-functionality to handle various tasks including sign identification, prevalence determination, and dictionary updates within a single integrated platform. This universal approach consolidates multiple functions into one system, improving identification capability while avoiding the complexity that would arise from separate specialized systems.
4Speed
If rapid processing of large datasets is performed, then speed of new sign detection improves, but accuracy may be compromised
Solution Approach 1:
The patent implements feedback mechanisms where the neural network continuously refines its detections by analyzing prevalence data and user interactions. The system feeds back information about sign usage patterns and community acceptance to adjust its identification criteria, enabling rapid processing of large datasets while maintaining high detection accuracy through iterative refinement and validation.
Data Source
AI summary
A system for receiving a corpus of sign language data in which a plurality of known signs each correspond to known meanings, generate a model for identifying new sign language signs using the corpus, and identifying, using the model, a new sign language sign that does not match any of the plurality of known signs.