ML Search Query Transformation and Result Extraction
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Solution Overview
Problem
Current search engines often fail to provide direct answers to user queries, requiring users to manually sift through search results, pose usability issues for individuals with impairments, and compromise information security and privacy.
Innovation Solution
A system utilizing a machine learning model to transform natural language queries into search queries, identify relevant information within search results, and present answers in a formatted, accessible manner, while preventing access to potentially malicious content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a search engine returns a list of search results directing users to various sources, then the user can access multiple information sources, but the user must manually investigate the results to find the answer, increasing time consumption and operational complexity
Solution Approach 1:
The system extracts the essential answer information from multiple search results and presents it directly to the user, removing the need for manual investigation. The processor identifies and extracts pertinent information that actually contains the answer to the user's question from the search results.
Solution Approach 2:
The system introduces an intermediary processing layer between the search engine and the user. This intermediary automatically analyzes search results, identifies relevant information, and presents it in a consolidated format, mediating between the raw search results and the user's information need.
2Loss of information
If the search engine provides detailed search results, then the information completeness is improved, but the complexity of analyzing and processing the results increases for users with impairments
Solution Approach 1:
The system segments the complex search result analysis into distinct processing steps: identifying query terms, extracting relevant parts from each result, assigning likelihood scores, and selecting the most relevant information. This segmentation makes the information processing more manageable and accessible.
Solution Approach 2:
The system changes the parameter of information presentation by transforming raw search results into a standardized format with assigned likelihood scores and selected relevant parts. This parameter transformation makes the information more accessible to users with various impairments while maintaining completeness.
3Loss of information
If the search engine returns comprehensive search results including advertisements and external links, then the information coverage is improved, but the user's information security and privacy are compromised due to phishing scams, malware, and data collection
Solution Approach 1:
The system extracts only the essential information content from search results while leaving out potentially harmful elements such as advertisements, phishing links, and malware. The processor identifies and extracts pertinent information that contains the answer while filtering out security threats.
Solution Approach 2:
The system converts the potentially harmful comprehensive search results into a beneficial secure information presentation. By automatically analyzing and selecting only the safe, relevant information, the system transforms the risk of comprehensive results into the benefit of secure, curated information delivery.
4Measurement precision
If the user transforms their question into a specialized search query with specific syntax and formatting, then the search precision is improved, but the ease of operation is reduced as users must learn special syntax and terms
Solution Approach 1:
The system inverts the traditional search approach by allowing users to input natural language questions without special syntax, and the system automatically transforms these into precise search queries. Instead of the user transforming the question into search syntax, the system performs the transformation from natural language to optimized search terms.
Data Source
AI summary
An apparatus includes a memory, and a processor operatively coupled to the memory. The processor can be configured to provide an indication of a natural language query to a machine learning model such that the machine learning model provides a general term and a specific term based on the natural language query. The processor can also execute at least one predetermined search engine based on the general term to generate a set of search results. The processor can also identify multiple query terms associated with the specific term and based on a word embedding. The processor is further configured to identify relevant parts in each search result from the set of search results based on the multiple query terms.