Voice Recognition System for Aligning Customer Needs with Product Parameters
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Customers face difficulties in selecting products due to a disconnect between their needs and the available product parameters, leading to overwhelming search results and misalignment in automated searches, which can cause disengagement and inaccurate representation of product needs.
Innovation Solution
The use of voice recognition and machine learning to determine contextual information from human speech, segmenting it into discrete conceptual units, identifying keywords, and applying analysis processes to generate accurate product recommendations by aligning customer needs with available product parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If customers are required to set product parameters to narrow down search results, then the quantity of search results is reduced, but the accuracy of representing customer needs deteriorates
Solution Approach 1:
The patent introduces an intermediary system that translates natural language customer descriptions into structured search parameters. This intermediary layer (the translation system) mediates between the customer's natural expression and the rigid parameter structure required by search systems, allowing accurate representation of customer needs without requiring them to manually set parameters.
Solution Approach 2:
The patent replaces the mechanical system of manual parameter setting with an automated natural language processing system. Instead of customers directly interacting with parameter fields (mechanical interaction), their speech is automatically processed and converted into search parameters, substituting the manual mechanical process with an automated linguistic system.
2Ease of operation
If traditional keyword matching is used to search for products, then the simplicity of the search process is maintained, but the accuracy of understanding customer intent deteriorates
Solution Approach 1:
The patent changes the parameters of the search process by moving from simple keyword matching to a multi-dimensional analysis that includes sentiment analysis, emphasis detection, and contextual understanding. These parameter changes in the search algorithm enable accurate intent recognition while maintaining ease of operation through voice-based input.
3Measurement precision
If multiple analysis processes are applied to determine contextual information, then the accuracy of product recommendations is improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex analysis process into distinct modular components: speech-to-text conversion, sentiment analysis, emphasis detection, and keyword association. Each component performs a specific function and can be independently optimized or adjusted, reducing overall system complexity while maintaining high accuracy in product recommendations.
Data Source
AI summary
A computer-implemented method for providing product recommendations to a user may include receiving audio data from a user device. The audio data may include human speech from a user associated with the user device. The method may further include: performing a speech-to-text process on the audio data to determine text; segmenting the text into discrete conceptual units; identifying keywords that correspond words in the text; associating keywords with the conceptual units having the corresponding words; performing a first analysis process on the conceptual units to determine a first score; performing a second analysis process portions of the human speech correlated with the conceptual units to determine a second score; determining a keyword score for each keyword based on the first and second scores of the conceptual units; determining a product recommendation based on the keywords associated with conceptual units and the corresponding keyword scores; and causing the user device to output the recommendation.