Media Guidance Application Natural Language Query Keyword Significance
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Solution Overview
Problem
Conventional search engines struggle to effectively identify relevant information resources for natural language queries, leading users to restructure their queries in an attempt to obtain more relevant results, which often results in frustration due to the inability of these systems to distinguish between high and low-significance keywords.
Innovation Solution
A media guidance system that processes natural language queries by identifying high-significance keywords and directing the query to relevant information resources, such as specific online forums or databases, to provide accurate and relevant results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional search engines process natural language queries, then users can submit queries in everyday language, but the search engines fail to effectively identify relevant information resources
Solution Approach 1:
The patent introduces an intermediary system that bridges natural language queries and information resources. This intermediary analyzes the query structure, identifies high-significance keywords, and maps them to relevant information resources, thereby resolving the mismatch between natural language input and structured information retrieval.
Solution Approach 2:
The patent segments the natural language query into individual keywords and evaluates their significance. By breaking down the query into component parts and assessing each keyword's contribution to the user's information need, the system can prioritize relevant terms and improve resource identification accuracy.
2Productivity
If conventional search engines rely on keyword matching, then simple concepts can be found quickly, but complex natural language queries cannot be effectively processed
Solution Approach 1:
The patent implements a dynamic keyword significance assessment mechanism that adapts to different query types. Rather than treating all keywords equally, the system dynamically evaluates each keyword's importance based on the query context, allowing it to handle both simple and complex queries effectively.
Solution Approach 2:
The patent changes the parameter of keyword evaluation from uniform matching to significance-weighted matching. By assigning different significance levels to different keywords based on their contribution to the user's information need, the system can effectively process queries of varying complexity.
3Reliability
If users restructure their queries to obtain more relevant results, then they may find better information, but the process increases user frustration and time consumption
Solution Approach 1:
The patent performs preliminary analysis of the natural language query to identify high-significance keywords and relevant information resources before the user needs to iterate. By proactively analyzing the query structure and mapping it to appropriate resources, the system reduces the need for users to restructure their queries multiple times.
Solution Approach 2:
The patent implements a feedback mechanism where the system learns from query patterns and user interactions. By analyzing which keywords and resource types lead to successful information retrieval, the system can improve its resource identification accuracy over time, reducing the need for query restructuring.
Data Source
AI summary
A media guidance application may detect that results provided in response to a natural language query entered by the user are not relevant to the user. The media guidance application may then identify a media asset type and a media asset name that corresponds to the natural language query. Based on the media asset type and the media asset name, the media guidance application may select an information resource, transmit the natural language query to the information resource, and retrieve and generate for display an answer to the natural language query from the information resource.