Smart Assistant Cache Mapping for Low-Latency Device Control
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Solution Overview
Problem
Existing automated assistant systems face high latency and excessive resource usage due to the need for remote processing of user inputs to control smart devices, particularly when local hardware constraints limit on-device natural language understanding and fulfillment capabilities.
Innovation Solution
Implementing a caching mechanism on assistant client devices that stores mappings of text to semantic representations, allowing for local interpretation and control of smart devices, reducing the need for remote processing and minimizing latency and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user inputs are transmitted to remote automated assistant servers for processing, then natural language understanding and smart device control can be achieved, but latency increases and network resources are excessively consumed
Solution Approach 1:
The system pre-processes user inputs locally using on-device natural language understanding models before transmission, or pre-loads frequently used commands and their corresponding semantic representations into local memory. This preliminary action reduces the amount of data that needs to be transmitted and processed remotely, thereby reducing latency while maintaining NLU capability.
Solution Approach 2:
The system segments natural language processing into multiple stages: local preprocessing (tokenization, basic intent detection), selective transmission of only necessary data to remote servers, and local post-processing of results. This segmentation allows critical time-sensitive operations to occur locally while leveraging remote resources only when necessary, reducing overall latency.
2Adaptability or versatility
If user inputs are transmitted to remote automated assistant servers for processing, then comprehensive smart device control can be achieved, but network resource consumption increases
Solution Approach 1:
The system implements partial remote processing by transmitting only essential data (e.g., recognized intent, device identifiers, parameters) to remote servers rather than transmitting complete audio recordings or full context. This partial action approach maintains comprehensive device control capability while significantly reducing network bandwidth consumption and energy usage.
Solution Approach 2:
The system creates and stores local copies of frequently accessed device control protocols, device topologies, and command mappings in on-device memory. This allows the device to autonomously execute common control operations without network communication, reserving remote server interactions for complex or novel commands, thereby reducing network resource consumption while maintaining versatility.
3Loss of time
If on-device natural language understanding is implemented, then latency is reduced, but local hardware resources are overwhelmed
Solution Approach 1:
The system implements local quality by deploying specialized, optimized NLU models tailored to specific device types and use cases rather than using generic comprehensive models. Each device or device category receives a customized lightweight model that operates efficiently within local hardware constraints, enabling low-latency processing without overwhelming device resources.
Solution Approach 2:
The system dynamically adjusts the level of local vs. remote processing based on available device resources, current operational context, and complexity of the input. When device resources are abundant and inputs are simple, more processing occurs locally. When resources are constrained or inputs are complex, the system selectively offloads specific processing tasks to remote servers, optimizing the balance between latency and resource usage.
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.