Query Response System Using Product Noun Detection
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Solution Overview
Problem
Existing query response systems face challenges in providing meaningful responses due to the brevity and lack of context in user queries, especially in search engines, as they often rely on formal syntax like Boolean logic, which can be unfamiliar to users and requires accurate translation to the information corpus.
Innovation Solution
The system utilizes an approach that identifies 'product nouns' through Markov models or other tools to analyze queries, ranks relevant products, and provides responses by pre-prepared data structures, including candidate validation, imputation, and product noun detection processes, to improve query response accuracy and relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If formal syntax like Boolean logic is used to specify queries, then query precision is improved, but user accessibility deteriorates
Solution Approach 1:
The patent introduces an intermediary layer that translates natural language queries into structured query representations. Instead of requiring users to directly use Boolean logic, the system acts as a mediator that automatically parses and interprets natural language, converting it into precise query formulations that achieve both accuracy and user-friendliness.
Solution Approach 2:
The patent replaces the mechanical syntax-based query system (Boolean logic) with a natural language processing system. This substitution eliminates the need for users to learn formal syntax while maintaining query precision through advanced NLP techniques that understand intent and context.
2Ease of operation
If brief queries are accepted, then ease of operation is improved, but information completeness deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the system analyzes brief queries, identifies missing context, and either infers relevant information or presents targeted clarification questions to users. This feedback loop allows the system to compensate for information loss while maintaining ease of operation.
Solution Approach 2:
The system performs preliminary actions by pre-processing and analyzing queries to identify implicit intent and missing context before executing the search. This preliminary analysis enables the system to retrieve relevant information even from brief queries by inferring the user's underlying needs.
3Measurement precision
If complex analysis is performed on queries, then response accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-processing queries to identify key intent and structure before full analysis. This preliminary step allows the system to focus computational resources on the most relevant aspects of query analysis, maintaining accuracy while reducing overall processing time.
Solution Approach 2:
The system employs partial action by performing different levels of analysis on different queries based on their complexity and the system's confidence in initial interpretation. For straightforward queries, minimal analysis is performed, while complex queries receive more thorough processing, optimizing the balance between accuracy and speed.
Data Source
AI summary
It is possible to provide meaningful responses to queries using systems which consider usage of words in the queries when analyzing those queries and determining what information is possibly relevant. This approach can be applied in online shopping systems by identification of nouns or noun phrases reflecting products available through the system.