Cloud Assistant Model Distillation for Private Local Query Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Cloud-based assistant services for voice-enabled devices face issues with network bandwidth consumption, increased latency, and reduced privacy due to the need to transfer audio data for query processing.
Innovation Solution
A system that aggregates user queries, extracts attributes, and generates distilled models tailored for specific client devices based on query patterns, allowing local processing to reduce network reliance and enhance privacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud-based assistant services are used for query processing, then query processing capabilities are improved, but network bandwidth consumption increases and latency increases
Solution Approach 1:
The patent segments the assistant service into two parts: a cloud-based service for training and a distilled model for local execution on client devices. This segmentation allows complex query processing capabilities to remain in the cloud while simpler, targeted functions are distributed to edge devices, reducing network bandwidth consumption for routine queries while maintaining high processing capabilities for complex tasks.
Solution Approach 2:
The distilled model acts as an intermediary between the cloud-based assistant service and client devices. It receives training data from the cloud, processes local queries independently, and only communicates with the cloud when necessary. This intermediary layer reduces network bandwidth consumption by handling routine queries locally while maintaining access to cloud-based capabilities when needed.
2Productivity
If cloud-based assistant services are used for query processing, then query processing capabilities are improved, but latency increases
Solution Approach 1:
The system performs preliminary action by pre-training distilled models in the cloud with specific query patterns and capabilities before deploying them to client devices. This allows the local models to immediately handle routine queries without needing to communicate with the cloud, significantly reducing latency while maintaining access to cloud-based training resources for future improvements.
Solution Approach 2:
The patent segments query processing into immediate local handling by distilled models and deferred cloud-based processing for complex or novel queries. This segmentation eliminates latency for routine tasks while preserving access to comprehensive cloud-based capabilities when needed, optimizing the balance between speed and capability.
3Productivity
If cloud-based assistant services are used for query processing, then query processing capabilities are improved, but user privacy is reduced
Solution Approach 1:
The distilled model serves as a privacy-protecting intermediary that processes sensitive user queries locally on client devices without transmitting audio or text data to the cloud. This intermediary layer maintains user privacy by keeping personal information local while still providing assistant functionality, and only communicates with the cloud for model updates rather than query processing.
Solution Approach 2:
The system creates simplified copies (distilled models) of the cloud-based assistant service that can execute locally on client devices. These copied models provide the necessary query processing capabilities while keeping user data local, eliminating the privacy risk associated with transmitting sensitive information to the cloud while maintaining functional capabilities.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method (400) includes receiving user queries (119) directed toward a cloud-based assistant service (120). For each received user query directed toward the cloud-based assistant service, the method also includes extracting one or more attributes (121) from the user query and logging the user query into one or more of a plurality of category buckets (210) based on the one or more attributes extracted from the user query. The method also includes determining when at least one of the plurality of category buckets includes a threshold number of the user queries logged into the at least one category bucket, and when the at least one of the plurality of category buckets includes the threshold number of the user queries, generating a distilled model (350) of the cloud-based assistant service. The distilled model of the cloud-based assistant service is configured to execute on one or more target client devices (102).