AI Product Query Analysis for Real-Time Ecommerce Matching
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Solution Overview
Problem
Online shopping for technically complex items is challenging due to difficulties in lucidly describing requirements, as consumers struggle to find suitable products on ecommerce sites and even experienced salespeople face challenges in providing accurate recommendations.
Innovation Solution
A product query analysis system using an artificial intelligence-based engine that receives natural language queries, interprets requirements, extracts product component details, and applies neural network models like word2vec and doc2vec to identify suitable products in real-time, sorting results by suitability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword matching is used to search products, then the search process is simple and fast, but the system cannot accurately understand user intent and requirements for technically complex items
Solution Approach 1:
The patent introduces an artificial intelligence engine as an intermediary between the user's natural language query and the product database. This AI engine comprehends the query, extracts product requirements, and translates them into searchable criteria, thereby improving understanding accuracy without requiring complex changes to the core database search infrastructure
Solution Approach 2:
The patent replaces traditional mechanical keyword-matching mechanisms with an AI-based comprehension system. Instead of relying on exact keyword matches, the system uses natural language processing to understand user intent, extract requirements, and map them to product specifications, significantly improving query understanding accuracy
2Measurement precision
If detailed product requirements are specified to improve matching accuracy, then product recommendations become more accurate, but the time required to process the query increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and structuring product data in the database, organizing it in a way that facilitates efficient querying. The AI engine also pre-identifies key requirement categories and extraction patterns, enabling rapid processing of user queries without sacrificing recommendation accuracy
Solution Approach 2:
The patent segments the query processing into distinct stages: comprehension, requirement extraction, and product matching. This segmentation allows each stage to be optimized independently, with the AI engine efficiently extracting only the most relevant requirements from user queries, thereby reducing processing time while maintaining accuracy
3Adaptability or versatility
If natural language processing is used to understand user queries, then the system can handle diverse query formats, but the computational resources required increase significantly
Solution Approach 1:
The patent applies partial action by having the AI engine focus on extracting only the most critical product requirements from user queries rather than analyzing every aspect of the natural language input. This selective approach maintains high adaptability to diverse query formats while significantly reducing computational resource consumption compared to comprehensive natural language analysis
Data Source
AI summary
A system, method, and computer-readable medium for performing a product query operation, comprising: receiving user input from a user; identifying keywords contained within the user input; comparing keywords to vector representations of words and also to a document vector representation of product specifications in a backend product repository, each vector representation of a word being associated with a particular query type; identifying component details based upon the comparing; and, providing a list of products corresponding to the component details to the user in real time.