Natural Language Query Matching via Vector Space Embedding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional media guidance systems require users to structure their voice commands in a specific manner to be recognized, limiting natural language queries and prompting users to repeat queries or receive no results if not structured correctly.
Innovation Solution
A media guidance system that processes natural language queries by mapping words to a vector space, allowing for variations in query structure and intent recognition, using algorithms like word-to-vector to generate representation vectors and match queries with templates, enabling recognition of similar intent across different query phrases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional systems use fixed query matching for voice commands, then system complexity is reduced, but query recognition accuracy deteriorates because users must speak in structured formats they do not find natural
Solution Approach 1:
The system transforms discrete query matching into continuous vector space representation. By converting words into vector embeddings and measuring semantic similarity through distance metrics, the system accepts natural language variations while maintaining recognition accuracy. This parameter transformation from categorical to continuous space resolves the contradiction between simplicity and accuracy.
Solution Approach 2:
The patent introduces vector embeddings as an intermediary layer between user input and query matching. Instead of directly comparing discrete queries, the system maps both user input and template queries into vector space, where semantic relationships are preserved. This intermediary transformation enables natural language understanding without increasing system complexity.
2Ease of operation
If the system accepts varied natural language queries, then ease of operation improves, but device complexity increases due to vector space processing and template matching algorithms
Solution Approach 1:
The system employs a universal vector space representation that handles multiple query types and variations through a single framework. The same embedding and similarity computation mechanisms work for different domains and query structures, making the system both easy to use and computationally efficient through reusability.
Solution Approach 2:
The patent uses template queries as reference copies that define intended meanings. By comparing user input against these template vectors in semantic space, the system recognizes intent without requiring exact phrase matching. This copying approach enables natural language acceptance while maintaining structured processing efficiency.
3Speed
If the system uses exact query matching, then processing speed is improved, but adaptability deteriorates because the system cannot recognize variations of the same intent
Solution Approach 1:
The system pre-computes and stores vector embeddings for template queries during system initialization or offline processing. When a user query arrives, the system only needs to compute the input vector and compare it against pre-stored templates using efficient similarity metrics, maintaining fast processing while enabling semantic matching of variations.
Data Source
AI summary
Systems and methods provide a media guidance application that recognizes a plurality of natural language search queries for identifying a set of search results. For example, a user may want to determine when the Yankees are playing their next baseball game. The user may structure their query in multiple ways, such as, “When are the Yankees playing?”“What time is the Yankees game?”“When is the next Yankees baseball game?” The user would expect the same result, a description of when the Yankees are playing, regardless of how the query is structured. The systems and methods enable a user to use a plurality of search queries when searching for items or information to get desired results.