Conditional Response Cache for Automated Assistant Latency
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Solution Overview
Problem
Automated assistants experience delays and inefficiencies in responding to user queries due to network and processing constraints, leading to wasted computational resources and latency issues, particularly when multiple users request similar information simultaneously.
Innovation Solution
Implementing an assistant response cache on client devices that is dynamically updated based on user context and intent, allowing for local fulfillment of requests without immediate server access, and expiring cache entries when conditions such as time or location change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the automated assistant retrieves data from the server for every user query, then the accuracy and relevance of responses are maintained, but response latency increases and network bandwidth is consumed
Solution Approach 1:
The system performs preliminary actions by proactively fetching and caching response fulfillment data before users actually submit requests. The server identifies potential user intents based on contextual factors (location, time, device state) and pre-computes responses, storing them in local caches on client devices. This eliminates the need for real-time server queries when users submit matching requests, thereby reducing latency while maintaining accuracy.
Solution Approach 2:
The system implements local quality by maintaining distributed caches on individual client devices rather than relying on a centralized server for all queries. Each device has its own local cache storing response fulfillment data specific to that device's context and user preferences. This local storage enables fast response fulfillment without network dependency, while the server maintains the authoritative data source for cache updates.
2Loss of time
If the automated assistant caches response fulfillment data locally, then response latency is reduced, but data accuracy may deteriorate over time
Solution Approach 1:
The system applies dynamics by implementing conditional cache invalidation rules that automatically update or invalidate cached data based on changing contextual factors. When location, time, or device state changes exceed predefined thresholds, the system determines that cached data may no longer be accurate and triggers server updates. This dynamic adaptation ensures cache data remains relevant without requiring continuous server synchronization, balancing speed and accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms where the server monitors cache usage patterns and contextual changes, then proactively pushes update notifications to client devices when cached data becomes stale. The client device receives feedback about cache validity status and automatically refreshes data when needed, creating a closed-loop system that maintains accuracy while minimizing unnecessary network traffic.
3Speed
If the automated assistant pre-fetches and caches data for potential user requests, then response speed improves, but network bandwidth and processing resources are consumed
Solution Approach 1:
The system changes parameters by dynamically adjusting cache update triggers based on contextual factors such as user behavior patterns, time of day, location stability, and device battery status. Instead of uniformly pre-fetching data for all potential queries, the system adapts its caching strategy to prioritize high-probability intents and defer low-priority updates, optimizing the balance between response speed and resource consumption.
Solution Approach 2:
The system applies partial action by caching only the most critical and frequently accessed response fulfillment data rather than pre-fetching all possible data. The server identifies and caches high-value intents based on historical usage patterns and contextual relevance, leaving less critical data to be fetched on-demand. This selective caching approach achieves significant latency reduction for common queries while minimizing unnecessary network bandwidth consumption.
Data Source
AI summary
Implementations set forth herein relate to conditionally caching responses to automated assistant queries according to certain contextual data that may be associated with each automated assistant query. Each query can be identified based on historical interactions between a user and an automated assistant, and—depending on the query, fulfillment data can be cached according to certain contextual data that influences the query response. Depending on how the contextual data changes, a cached response stored at a client device can be discarded and/or replaced with an updated cached response. For example, a query that users commonly ask prior to leaving for work can have a corresponding assistant response that depends on features of an environment of the users. This unique assistant response can be cached, before the users provide the query, to minimize latency that can occur when network or processing bandwidth is unpredictable.