Dynamic Content Request Allocation for Gateway Adaptation
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Solution Overview
Problem
Mobile communication devices face inefficiencies in content delivery due to statically configured content gateways, which fail to adapt to changing conditions such as content source handling capacity and business arrangements, leading to delayed content presentation and increased processing loads.
Innovation Solution
A content request allocation adaptation system that analyzes records of content request transactions to dynamically adapt content request distribution models based on current conditions, including time-outs, error codes, day of the week, time of day, and processing loads, and updates the configuration of the content request allocation system to optimize content source sequencing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a statically configured content gateway is used, then the system structure is simple and easy to operate, but the system cannot adapt to changing conditions such as content source handling capacity and business arrangements, leading to delayed content presentation and increased processing loads
Solution Approach 1:
The patent implements dynamic allocation of content requests to content sources based on current system conditions. The gateway dynamically adjusts the sequence and selection of content sources according to real-time parameters such as handling capacity, business arrangements, and content types, transforming the static configuration into a dynamic adaptive system that optimizes content delivery efficiency
Solution Approach 2:
The system incorporates feedback mechanisms that monitor content source performance metrics including handling capacity, response times, and success rates. This feedback information is used to continuously adjust the content request allocation strategy, enabling the gateway to adapt to changing conditions while maintaining system efficiency and reducing processing loads
2Productivity
If multiple content sources are monitored and analyzed in real-time, then content delivery efficiency is improved, but processing loads on the gateway increase
Solution Approach 1:
The system performs preliminary analysis and pre-establishes allocation strategies for content sources based on historical data and predicted conditions. By preparing content request sequences in advance based on anticipated scenarios, the gateway reduces the need for complex real-time decision-making, thereby improving content delivery efficiency while minimizing processing loads during actual content requests
Solution Approach 2:
The gateway dynamically changes operational parameters such as content source selection sequences, request timing, and allocation weights based on current system conditions. By adjusting these parameters rather than reconfiguring the entire system, the gateway achieves improved content delivery efficiency while keeping processing overhead manageable through targeted parameter optimization
Data Source
AI summary
A method of allocating content requests to content sources. The method comprises accessing records by an application executing on a computer system from a data store, analyzing the records with reference to time-outs of content requests, content responses with error code, content responses that failed to satisfy content screening criteria, with reference to day of the week, time of day, and processing loads on content sources, based on the analysis of the records adapting a model of content request distribution by the application, where the model defines a plurality of different content request allocation sequences where each content request allocation sequence is associated with a criteria for applying that sequence based on current conditions, selecting a sequence by the application based on comparing current conditions to the criteria and writing the selected content request allocation sequence by the application into a configuration of the content request allocation system.