AI Data Parsing System for Customized Search Output
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Solution Overview
Problem
Users face the challenge of manually sorting through vast amounts of information from various resources to find relevant results, as existing search engines often fail to efficiently prioritize and present customized output based on user queries, leading to time-consuming searches and suboptimal information retrieval.
Innovation Solution
A system utilizing a communications module, analytics engine, AI engine, and natural language engine to parse content based on user queries, assign confidence rankings, filter content based on user learning styles, and present summarized outputs, thereby prioritizing and customizing search results for users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually sort through vast amounts of information from various resources, then they can find relevant results, but the time and effort required increases significantly
Solution Approach 1:
The patent introduces an intermediary system comprising an analytics engine, AI engine, and natural language engine that acts as a mediator between users and information resources. This intermediary automatically parses content, assigns confidence rankings, filters based on learning styles, and generates summarized outputs, eliminating the need for users to manually sort through vast information while maintaining high retrieval accuracy
Solution Approach 2:
The system enables self-service by automatically performing information retrieval, parsing, ranking, and summarization tasks without requiring user intervention. The analytics engine autonomously analyzes content from multiple resources, assigns confidence rankings based on credibility metrics, and presents customized summaries tailored to user learning styles, allowing users to obtain relevant information instantly without manual effort
2Adaptability or versatility
If existing search engines rank information based on relevance, then some prioritization is achieved, but the output is not customized to user's learning style and skill level
Solution Approach 1:
The patent applies local quality by customizing different aspects of the output based on specific user characteristics. The system analyzes user learning styles (visual, auditory, reading-writing, tactile) and skill levels, then tailors the presentation format, detail level, and explanation depth of the summarized content to match each user's specific needs, rather than providing uniform output for all users
Solution Approach 2:
The system implements dynamics by making the output adaptable and flexible based on real-time user input and preferences. The natural language engine dynamically adjusts the summarized content's complexity, format, and focus based on the user's learning style and skill level detected through interaction, allowing the system to evolve its output characteristics to better serve each user
3Reliability
If users manually evaluate information credibility and relevance, then they can identify the best information, but the process becomes extremely time-consuming
Solution Approach 1:
The patent applies preliminary action by having the analytics engine pre-analyze and evaluate information from multiple resources before users request it. The system proactively assigns confidence rankings to content based on credibility metrics such as source reputation, author expertise, and cross-validation across resources, so that when users query the system, the information is already filtered and ranked for reliability, eliminating the need for users to perform manual credibility evaluation
Data Source
AI summary
An approach is provided in which an information handling system parses content received from resources based on a user query corresponding to a user. Next, the information handling system selects a set of prioritized content from the parsed content based on a confidence ranking the plurality of parsed content. The information handling system then filters the set of prioritized content based on a learning style of the user and presents a summarized output of the filtered set of prioritized content to the user.