Neural Network Content Selection for Bandwidth and Latency Reduction
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Solution Overview
Problem
Websites face challenges in displaying a large number of images or visual content items due to bandwidth and memory restrictions, leading to significant latency or failure in rendering content, which hampers interaction rates with target audiences.
Innovation Solution
A neural network is employed to select a subset of images that are most likely to engage the target audience, generating a benchmark for comparison, allowing only the top-performing images to be uploaded, thereby reducing the number of content items displayed and mitigating technical issues associated with high interaction rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a large number of content items are displayed on a webpage to increase target audience interaction, then interaction rate is improved, but bandwidth consumption increases and latency occurs
Solution Approach 1:
The patent extracts only the most relevant content items from a large pool using neural network-based relevance scoring. Instead of displaying all available content items, the system identifies and selects only those with highest relevance scores, thereby reducing bandwidth consumption while maintaining interaction rate.
Solution Approach 2:
The system changes the parameter of content selection by using dynamic relevance scoring based on user context, device characteristics, and content properties. This parameter-based filtering allows the system to optimize the number and type of content items displayed, reducing overall bandwidth usage while preserving high-interaction content.
2Productivity
If a large number of content items are displayed on a webpage to increase target audience interaction, then interaction rate is improved, but system latency increases
Solution Approach 1:
The patent performs preliminary action by pre-scoring and ranking content items using neural networks before they need to be displayed. Relevance scores are calculated in advance based on user profiles, device characteristics, and content metadata, so that when content needs to be displayed, the selection process is already complete, reducing latency.
Solution Approach 2:
The system extracts only the top-ranked content items that meet relevance thresholds, eliminating the need to process and render large numbers of low-relevance items. This extraction approach reduces the computational burden during content delivery and minimizes system latency.
3Productivity
If a large number of content items are displayed on a webpage to increase target audience interaction, then interaction rate is improved, but memory consumption increases
Solution Approach 1:
The patent applies extraction by selecting only the most relevant content items based on neural network scoring, rather than loading all available content into memory. This selective extraction significantly reduces memory consumption while ensuring that high-value content items are preserved for display.
Solution Approach 2:
The system uses parameter-based filtering where content items are evaluated against multiple parameters (relevance score, user preferences, device capacity) and only those meeting the thresholds are retained in memory. This parameter-driven approach optimizes memory usage by excluding low-value content.
Data Source
AI summary
A method for training a text generation machine learning model for prompt generation includes receiving a plurality of training text strings; executing a text generation machine learning model using each of the plurality of training text strings to generate a plurality of training generated text strings, each of the plurality of generated text strings corresponding to generating a content item; executing a content scoring machine learning model using each of the plurality of generated text strings to generate a text performance score for each of the plurality of training generated text strings; for each of the plurality of generated text strings, adjusting one or more weights or parameters of the text generation machine learning model proportional to the text performance score for the generated text string.