Mobile Search Pre-fetching Latency Optimization
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Solution Overview
Problem
Limited network connection speed causes latency in mobile devices when accessing online content, leading to a suboptimal user experience, as existing prediction schemes for pre-fetching and pre-rendering are not accurate enough, resulting in unnecessary resource usage.
Innovation Solution
A client-server system where a search application on a mobile device submits a user query to a server, which returns ranked search results based on user-specific and device-specific information, allowing for pre-fetching and pre-rendering of the most relevant content before user selection, thereby reducing latency and improving browsing experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If pre-fetching and pre-rendering techniques are employed to predict user content requests, then latency is reduced and user experience is improved, but device bandwidth and power consumption increase due to downloading and rendering content that may not be accessed
Solution Approach 1:
The system performs preliminary actions by pre-fetching and pre-rendering content before the user actually requests it. The search application predicts which content the user will need based on search queries and user behavior patterns, then proactively downloads and renders this content in advance, reducing latency when the user does request the content.
Solution Approach 2:
The system uses feedback mechanisms to continuously learn from user behavior patterns, search queries, and content access patterns to improve prediction accuracy. This feedback loop allows the prediction model to adapt to changing user preferences and behaviors, thereby reducing unnecessary pre-fetching of content that won't be accessed.
2Reliability
If pre-fetching and pre-rendering all URL's visible on webpage or associated with whitelist is performed, then content availability is improved, but device bandwidth and power consumption waste increases due to unnecessary content download and rendering
Solution Approach 1:
Instead of uniformly pre-fetching all content, the system applies local quality by customizing prediction based on individual user characteristics, search history, and contextual information. This targeted approach ensures that content is pre-fetched only when and where it is most likely to be needed, avoiding unnecessary bandwidth consumption.
Solution Approach 2:
The system dynamically changes prediction parameters based on real-time data such as search queries, user location, device state, and temporal patterns. This adaptive parameter adjustment allows the system to optimize prediction accuracy for each specific context, reducing unnecessary content download while maintaining high content availability for actual user needs.
Data Source
AI summary
Techniques for optimizing delivery of online content to a mobile client device. In an aspect, a search application on the client device receives a user query, which is submitted to a server over a communications channel. The server retrieves a plurality of ranked search results relevant to the user query, and returns the search results to the client device. The search application further pre-fetches from the server and/or pre-renders content associated with a top plurality of the ranked search results, such that content may be instantly displayed at the client device if requested by the user. The identification and ranking of relevant search results by the server may be performed based on information provided by the client device that is customized to the user and/or the scenario, e.g., user account name, geographical location, device capabilities, etc.