Neural Network Video Curation for Web Search Relevance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current web search technologies face challenges in providing relevant and manageable results, especially for product or service searches, due to the vast number of low-relevance hits, which can lead to user dissatisfaction and decreased conversion rates.
Innovation Solution
A processor-implemented method for video manipulation using machine learned curating of videos, where a neural network selects and displays short-form videos based on textual information from web pages, incorporating adaptive learning to refine relevance and improve user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If general search strings are used to find content, then the search can be simple and quick, but the search results become overwhelming with too many low-relevance hits
Solution Approach 1:
The patent extracts and highlights only the most relevant search results using AI analysis, separating valuable content from the overwhelming bulk of irrelevant results. This allows users to quickly access important information without being overwhelmed by the total number of hits.
Solution Approach 2:
The system dynamically changes the relevance parameters of search results based on AI analysis of user behavior, content quality, and contextual factors. This transforms the static list of results into a dynamically optimized ranking that prioritizes high-relevance content.
2Quantity of substance
If the web page presents many search results to ensure completeness, then comprehensive coverage is achieved, but user conversion rate decreases due to information overload
Solution Approach 1:
The patent segments the overwhelming search results into distinct categories and prioritized groups using AI analysis. This segmentation allows the system to maintain comprehensive coverage while organizing information in a digestible format that improves user decision-making and conversion rates.
Solution Approach 2:
The system applies partial action by selectively emphasizing only the most relevant results rather than treating all results equally. This selective emphasis maintains completeness while directing user attention to high-value content that drives conversion.
3Measurement precision
If AI techniques are used to curate search results, then relevance and quality improve, but system complexity increases
Solution Approach 1:
The patent introduces an AI intermediary layer that sits between the search engine and the user interface. This intermediary automatically analyzes and ranks results, providing high precision without requiring complex user-side processing or manual curation systems.
Solution Approach 2:
The AI system performs self-service by automatically analyzing search results, user behavior patterns, and content quality metrics to dynamically optimize result ranking. This automation achieves high relevance without requiring manual intervention or complex configuration.
Data Source
AI summary
Techniques for video manipulation based on machine learned video curating are disclosed. Web page content is loaded, where the content includes a frame for short-form videos. The content of the web page is analyzed for textual information. A short-form video server is accessed. Short-form videos are selected from the short-form video server, where the selecting includes automatically curating the short-form videos. Adaptive learning is used for the selecting, based on a user's web page behavior. The adaptive learning includes collecting the user's web page behavior before the selecting. Automatic curating includes selecting, by a neural network, a subset of short-form videos appropriate for the web page. The web page frame is populated with the short-form videos obtained from the video server. Representations of the short-form videos are displayed within the frame on the web page. The short-form videos are auto played within the frame.