Smart Device Assistant Caching for Low-Latency Local Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing techniques for controlling smart devices through automated assistants often result in high latency and excessive resource usage due to the need for remote processing and data transmission.
Innovation Solution
Implementing a caching system on assistant client devices that stores mappings of text to semantic representations, allowing for local processing and reduced reliance on remote systems for common commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If remote automated assistant servers process user input data to generate control requests, then comprehensive processing capability is achieved, but high latency and excessive resource usage occur
Solution Approach 1:
The system segments processing tasks between local assistant client devices and remote automated assistant servers. The local device handles speech-to-text conversion and basic command recognition, while the remote server handles complex semantic understanding and control request generation. This segmentation allows latency-sensitive operations to occur locally while maintaining comprehensive processing capability through remote support.
Solution Approach 2:
The system performs preliminary speech-to-text conversion and basic command recognition locally before transmitting data to remote servers. By preprocessing the input data locally, the system reduces the amount of data that needs to be transmitted and processed remotely, thereby reducing overall latency while maintaining processing comprehensiveness.
2Reliability
If remote automated assistant servers process user input data, then comprehensive processing capability is achieved, but excessive client and server resources are consumed
Solution Approach 1:
The system divides processing responsibilities between local and remote components. Local speech-to-text conversion and basic command recognition reduce the computational burden on remote servers, thereby reducing overall energy consumption while maintaining processing capability. The local device handles routine operations that would otherwise consume remote server resources.
Solution Approach 2:
The system performs only the necessary speech-to-text conversion and basic command recognition locally, rather than attempting complete semantic understanding locally. This partial local processing reduces energy consumption on both client and server sides while still achieving comprehensive processing through the combination of local and remote operations.
3Measurement precision
If high bandwidth audio data and text are transmitted to remote servers, then accurate processing is achieved, but excessive network resources are used
Solution Approach 1:
The system extracts and transmits only the essential speech-to-text conversion results and basic command information to remote servers, rather than transmitting the entire high bandwidth audio data. This extraction approach maintains processing accuracy by preserving the core semantic information while significantly reducing network resource consumption.
4Reliability
If remote servers generate control requests, then comprehensive control capability is achieved, but high latency occurs due to transmission distance
Solution Approach 1:
The system segments the control request generation process into local and remote components. The local device performs speech-to-text conversion and basic command recognition immediately, providing fast initial response. The remote server then handles complex semantic understanding and generates the final control request. This segmentation improves response speed by performing latency-sensitive operations locally while maintaining comprehensive control capability through remote server involvement.
Data Source
AI summary
Various implementations relate to techniques, for controlling smart devices, that are low latency and/or that provide computational efficiencies (client and/or server) and/or network efficiencies. Those implementations relate to generating and/or utilizing cache entries, of a cache that is stored locally at an assistant client device, in control of various smart devices (e.g., smart lights, smart thermostats, smart plugs, smart appliances, smart routers, etc.). Each of the cache entries includes a mapping of text to one or more corresponding semantic representations.