Edge Device Local Neural Network for Offline Natural Language Generation
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Solution Overview
Problem
Smart devices that support natural language conversation, such as voice-responsive intelligent personal assistants, rely heavily on cloud servers for natural language processing, leading to functionality issues when network connections are unreliable, as they cannot generate appropriate responses without continuous cloud-based computation.
Innovation Solution
Implementing a local neural network on edge devices, specifically using LSTM architecture, to predict and generate responses based on event data and user inputs, allowing the device to function independently of cloud servers when network connections are unavailable, by training the network with data collected from interactions with the cloud server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud-based processing is used for natural language processing, then processing accuracy is improved, but system reliability deteriorates when network connections are unavailable
Solution Approach 1:
The patent segments the natural language processing system into two parts: a cloud-based neural network for accurate processing and a local decision-making module for autonomous operation. The edge device divides processing tasks between cloud communication (when available) and local inference (when cloud is unavailable), allowing the system to maintain both high accuracy through cloud processing and reliability through local autonomy.
Solution Approach 2:
The system performs preliminary action by pre-processing event data locally and preparing context information before cloud communication is needed. The edge device extracts features and prepares data structures in advance, so when the cloud becomes unavailable, the local model can immediately use the prepared context and previously learned patterns to generate responses without interruption.
2Reliability
If local neural network is implemented for autonomous operation, then system reliability is improved, but device complexity increases
Solution Approach 1:
The patent applies local quality by implementing a simplified version of the neural network model locally rather than a full duplicate. The local model uses condensed knowledge from cloud training and focuses on specific decision-making tasks, providing sufficient reliability for autonomous operation while keeping the local computational requirements and device complexity manageable.
Solution Approach 2:
The system introduces an intermediary layer that mediates between the complex cloud-based neural network and the simpler local decision-making process. This intermediary includes context extraction modules and feature processing components that translate complex cloud processing outputs into simplified representations suitable for local inference, reducing the complexity burden on the edge device.
3Measurement precision
If cloud server processing is used, then response accuracy is improved, but loss of time increases due to network dependency
Solution Approach 1:
The patent implements dynamics by making the processing architecture adaptive rather than static. The system dynamically switches between cloud-based processing (for high accuracy when available) and local processing (for fast response when cloud is unavailable). This dynamic adaptation allows the system to optimize the trade-off between response accuracy and response time based on real-time network conditions.
Solution Approach 2:
The system performs preliminary action by pre-processing event data locally and maintaining context information ready for immediate use. When cloud processing is unavailable, the local model can generate responses using pre-extracted features and stored context without the time penalty of data transmission and cloud processing, significantly reducing response time while maintaining acceptable accuracy.
Data Source
AI summary
Systems and methods for natural language generation by an edge computing device are disclosed. In one embodiments, a method comprises: receiving, by an edge computing device, event data from an edge event; determining, by the edge computing device, that a network connection to a cloud server is not available; extracting, by the edge computing device, features of the event data; predicting, by a local neural network of the edge computing device, an action for the edge computing device to take based on the features of the event data, wherein the action is associated with a confidence level; and determining, by the edge computing device, whether the confidence level meets a predetermined threshold value.