Sign Language Translation System for Drive-Thru Accessibility
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Solution Overview
Problem
Current systems for sign language recognition in drive-through environments lack comprehensive solutions for accurate translation and integration with natural language understanding, limiting inclusivity and accessibility for customers who communicate primarily through sign language.
Innovation Solution
A method that employs computer vision algorithms and machine learning to translate sign language gestures into text format, integrating with natural language understanding systems for seamless order processing, and provides feedback mechanisms for accuracy, while also supporting multilingual interactions and dynamic gesture recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sign language recognition is integrated into drive-through systems, then inclusivity and accessibility for sign language customers is improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary translation system that converts sign language gestures into text format, which then interfaces with the existing natural language understanding system. This mediator layer (gesture recognition module + translation engine) bridges the gap between sign language input and the restaurant's order processing system, adding inclusivity without requiring complete system redesign
Solution Approach 2:
The system is divided into distinct functional modules: gesture capture components, sign language recognition algorithms, translation to text format, and integration with natural language understanding. This segmentation allows the sign language capability to be added as a separate functional block rather than embedding it throughout the entire system, managing complexity through modular architecture
2Measurement precision
If computer vision algorithms are used to translate sign language gestures, then communication accuracy for sign language users is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining a sign language dictionary mapping gestures to text representations, and pre-positioning cameras to capture optimal gesture views. During actual ordering, the system quickly matches observed gestures against the pre-established dictionary rather than performing full analysis from scratch, reducing processing time while maintaining accuracy
Solution Approach 2:
The patent replaces complex mechanical gesture analysis with algorithmic pattern recognition using computer vision. Instead of sophisticated physical measurement systems, the solution uses image processing algorithms to detect and interpret hand gestures, facial expressions, and body language, achieving accurate translation with reduced computational overhead
3Adaptability or versatility
If sign language translation is integrated with natural language understanding, then communication capability is improved, but system complexity and integration requirements increase
Solution Approach 1:
The system employs a universal text-based intermediate representation that serves multiple functions: it conveys sign language meaning to the natural language understanding system, maintains compatibility with existing speech-to-text workflows, and enables seamless integration with the restaurant's current order processing infrastructure. This universal interface layer handles multiple communication modalities through a single unified pathway
Solution Approach 2:
The patent merges the sign language translation output with the existing natural language understanding input stream, combining gesture-based text translations with speech-to-text transcriptions into a unified order processing workflow. This consolidation allows the system to handle multiple input types (sign language, speech) through a single integrated processing pipeline rather than maintaining separate systems
Data Source
AI summary
The present invention discloses a method for translating sign language for customer interaction with a business. The method involves receiving an order in sign language from a client, translating the order into text format through a series of processes and a decision-making step, displaying the text format on a visual display for the client, confirming the order by the client, and upon confirmation, fulfilling the client's order. This innovative method facilitates communication between clients using sign language and businesses, enhancing accessibility and inclusivity in customer interactions.