Signed Gesture Processing With Anonymized Landmark Transmission
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Solution Overview
Problem
Client devices struggle to securely and efficiently process signed natural language inputs due to transmission of personally identifying characteristics, which leads to data security concerns and unnecessary consumption of computational and networking resources.
Innovation Solution
Client devices generate anonymized data by processing signed natural language inputs using machine learning models, select a subset of anonymized data, and compress it before transmission to remote systems, ensuring data security and resource conservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data or video stream of signed natural language input is transmitted to a remote system, then the signed input can be processed, but network bandwidth is consumed and latency increases
Solution Approach 1:
The patent extracts only the essential gesture information from the full image/video data by identifying and transmitting key anatomical landmarks (hand positions, facial feature positions) rather than the complete visual data. This extraction approach maintains processing accuracy while dramatically reducing network bandwidth consumption and energy loss.
Solution Approach 2:
The patent segments the visual data into discrete anatomical landmarks and gesture parameters that can be transmitted separately and efficiently. By dividing the continuous image stream into structured data points representing specific body parts and their movements, the system reduces data volume while preserving the essential information needed for accurate signed language interpretation.
2Loss of information
If full image data of signed natural language input is transmitted, then complete information is available for processing, but data size increases transmission time and computational cost
Solution Approach 1:
The system extracts only the critical gesture-related information from the complete image data by identifying key anatomical landmarks and their temporal sequences. This selective extraction maintains sufficient information completeness for accurate signed language processing while eliminating redundant visual data, thereby reducing transmission time and latency.
Solution Approach 2:
The patent performs preliminary processing at the client device to identify and extract relevant gesture parameters before transmission. By pre-processing the visual data to isolate essential gesture information and converting it into compact data structures, the system reduces the amount of data that needs to be transmitted, thus minimizing transmission latency while preserving information completeness.
3Productivity
If personally identifying characteristics are transmitted with signed input data, then the data can be processed, but data security concerns arise
Solution Approach 1:
The patent extracts only the gesture and sign language information from the visual data while deliberately excluding personally identifying characteristics such as facial features, body shape, and other biometric data. This selective extraction maintains the productivity needed for signed language processing while eliminating data security risks associated with transmitting personal information.
Solution Approach 2:
The system introduces an intermediary processing layer that transforms the original visual data into an anonymized representation containing only gesture information. This intermediary transformation acts as a mediator between the user's signed input and the remote processing system, preserving processing capability while protecting user privacy by removing identifiable characteristics before transmission.
Data Source
AI summary
Implementations described herein relate to distributed processing of sign language input(s) and/or other gestures across multiple computing devices. For example, processor(s) of client device can receive user input that visually indicates a gesture and an identity of a user; generate, based on processing the user input, anonymized data that indicates the gesture of the user and that anonymizes the personal identity of the user; transmit a subset of the anonymized data to a computing device (e.g., a remote server or another client device); receive a natural language interpretation of the gesture of the user from the computing device; and perform an action based on the natural language interpretation of the gesture of the user. Notably, processor(s) of the computing device can generate the natural language interpretation of the gesture of the user and based on processing the subset of the anonymized data.