Content Delivery System Using Predictive Caching and Parallel Transmission
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Solution Overview
Problem
Current approaches for determining and communicating additional content in response to user requests are inefficient and costly, particularly due to the lack of standard methods for cache management and embedding complex content, which can result in wasteful behavior and inefficient processing.
Innovation Solution
The system employs machine learning and parallel communication techniques to predict and send additional relevant resources to user devices, using hypermedia APIs and caching semantics, allowing for efficient transmission and caching of content, and enabling user devices to reject or accept resources based on their needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the content server embeds additional relevant content within the same response as the requested content, then the functionality is enhanced, but the processing cost increases and efficiency decreases
Solution Approach 1:
The patent segments the content delivery process by separating the requested content and additional relevant content into different responses. The server sends the requested content in one response and the additional content in separate subsequent responses, allowing the client to selectively accept or reject each. This segmentation reduces the processing burden of embedding complex content structures while maintaining the ability to provide enhanced functionality through optional additional content.
2Ease of operation
If the content server sends additional relevant content in response to user requests, then the user experience is improved, but the network bandwidth and processing resources are wasted
Solution Approach 1:
The patent implements a self-service mechanism where the client device autonomously determines whether to accept or reject additional relevant content based on its local cache status and current needs. The client compares the additional content's identifier against its cache, automatically accepting content that is new or updated and rejecting content that is already cached. This eliminates the need for the server to waste network bandwidth transmitting content that the client already possesses, while still providing enhanced user experience when relevant content is actually needed.
3Ease of manufacture
If the content server determines and communicates additional content using conventional approaches, then the implementation is simple, but the efficiency is limited and costs are high
Solution Approach 1:
The patent introduces a feedback mechanism where the client device sends responses back to the server indicating whether it accepted or rejected additional relevant content. The server uses this feedback, along with cache status information provided by the client, to optimize future content delivery decisions. This feedback loop enables the system to learn from previous interactions and improve efficiency over time by sending fewer redundant content transmissions, while maintaining implementation feasibility through standardized HTTP protocol extensions.
4Productivity
If the content server sends multiple responses in parallel with additional content, then the resource delivery is optimized, but the device complexity increases
Solution Approach 1:
The patent applies preliminary action by having the client device proactively provide its cache status information to the server before receiving additional content. The client includes cache status data in its requests, enabling the server to pre-determine which additional content is worth sending based on what the client already has cached. This preliminary exchange of information optimizes resource delivery by avoiding transmission of redundant content, while keeping communication complexity manageable through structured data formats and standardized protocols.
Data Source
AI summary
Methods and systems are described for efficient communication of content. A user device may request content from a content device. The content device may determine a browsing context of the user device. A user model based on machine learning may be determined based on the browsing context. The user model may be used to predicted additional content that might be requested by the user device. The additional content or a portion thereof may be sent to the user device in parallel with the originally requested content. As the user device rejects, accepts, or further requests the additional content, the user model may be further refined.