Browsing Cluster Hint Models for Faster Web Page Loading
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Solution Overview
Problem
Existing web page loading techniques are inefficient due to the lack of effective hinting models for resource loading, leading to increased page load times and suboptimal user experiences.
Innovation Solution
Utilizing automated browsing clusters to generate and update hinting models through machine-generated feedback, providing preliminary hints when necessary and refining models over time to improve page load timing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If automated browsing clusters are used to generate and update hinting models, then web page loading efficiency is improved and presentation time is reduced, but system complexity and computational resources required increase
Solution Approach 1:
The system performs preliminary actions by using automated browsing clusters to fetch web pages and generate hinting models in advance. These models are updated continuously in the background before users actually need them, allowing the system to prepare optimization data proactively rather than reactively when a page is requested.
Solution Approach 2:
The patent introduces automated browsing clusters as intermediary components that act as middlemen between web servers and user browsers. These clusters fetch pages, extract resources, and generate hints that are then provided to users, mediating the complex processing tasks away from end-user devices and centralizing them in the hinting machine infrastructure.
2Productivity
If machine-generated hints are provided to improve resource fetching, then initial page availability is improved, but the extent of automation and computational overhead increase
Solution Approach 1:
The automated browsing clusters operate autonomously to fetch web pages, extract resource information, and generate updated hinting models without requiring manual intervention. The system serves itself by automatically maintaining and updating its own hinting infrastructure, with clusters continuously crawling and learning from new web content independently.
Solution Approach 2:
The system implements feedback loops where automated browsing clusters continuously fetch pages, generate hints, and update models based on newly discovered web content. This ongoing feedback process allows the hinting models to adapt and improve over time as they learn from actual web page structures and resource patterns observed during automated browsing operations.
Data Source
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AI summary
Embodiments seek to improve prefetch hinting by using automated browsing clusters to generate and update hinting models used for machine-generated hints. For example, hinting machines can include browsing clusters that autonomously fetch web pages in response to update triggers (e.g., client web page requests, scheduled web crawling, etc.) and generate timing and/or other hinting-related feedback relating to which resources were used to load the fetched web pages. The hinting machines can use the hinting feedback to generate and/or update hinting models, which can be used for machine-generation of hints. Some embodiments can provide preliminary hinting functionality in response to client hinting requests, for example, when hinting models for a requested page are insufficient (e.g., unavailable, outdated, etc.). For example, without having a sufficient hinting model in place, the hinting machine can fetch the page to generate preliminary hinting feedback, which it can use to machine-generate preliminary hints.