Automated Feature Combination Generation via Sentiment Analysis
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Solution Overview
Problem
The product design process is hindered by scalability, efficiency, and throughput issues, as it relies on human intuition and traditional methods like consumer surveys and market research, which are inefficient and not scalable.
Innovation Solution
A design engine utilizing machine learning models, such as recurrent neural networks and multi-armed bandit algorithms, to analyze historical data from consumer reviews, social media, and articles to identify and rank sentiment phrases related to product features, generating potential feature combinations for new products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated design techniques are implemented, then productivity and scalability improve, but device complexity increases
Solution Approach 1:
The design system is divided into separate functional modules: a sentiment analysis module that processes consumer feedback, a feature extraction module that identifies product attributes, and a combination generation module that creates design options. This modular architecture improves productivity while managing complexity through clear separation of concerns.
Solution Approach 2:
The patent introduces automated software agents and algorithms as intermediaries between consumer feedback data and design decisions. These intermediaries process and interpret unstructured consumer sentiments, transforming them into structured design recommendations, thereby reducing the need for direct human analysis while maintaining design quality.
2Reliability
If human designers rely on intuition and tribal knowledge, then design quality may be maintained, but loss of information increases and scalability deteriorates
Solution Approach 1:
The system implements continuous feedback loops where consumer reviews, social media mentions, and market research data are systematically collected, analyzed, and fed back into the design process. This automated feedback mechanism ensures that valuable consumer information is captured and utilized without loss, while scaling to handle large volumes of data that would be impossible for human designers to process individually.
Solution Approach 2:
The patent replaces the mechanical process of human intuition and manual analysis with automated computational algorithms. Machine learning models and natural language processing systems substitute for human cognitive processes, enabling comprehensive analysis of consumer feedback while preserving all information without the limitations of human memory and processing capacity.
3Loss of information
If traditional market research methods are used, then design insights can be obtained, but loss of time increases and productivity decreases
Solution Approach 1:
The system performs preliminary analysis of consumer feedback and market data continuously, before formal design decisions are required. By pre-processing and pre-analyzing consumer sentiments and trends, the system prepares design recommendations in advance, reducing the time needed for actual design development while ensuring comprehensive information is available.
Solution Approach 2:
The automated design system operates continuously, constantly monitoring and analyzing consumer feedback from multiple sources simultaneously. Unlike traditional periodic market research, this continuous operation ensures that design insights are always up-to-date and available immediately when needed, eliminating delays associated with scheduled research cycles and manual analysis processes.
Data Source
AI summary
Systems and methods are described herein for generating potential feature combinations for a new item. A neural network may be utilized to identify positive and/or negative sentiment phrases from textual data. Each sentiment phrase may correspond to particular features of existing items. A machine-learning model may utilize the sentiment phrases and their corresponding features to generate a set of potential feature combinations for a new item. The potential feature combinations may be scored, for example, based on an amount by which a potential feature combination differs from known feature combinations of existing items. One or more potential feature combinations may be provided in a feature recommendation. Feedback (e.g., human feedback, sales data, page views for similar items, and the like) may be obtained and utilized to retrain the machine-learning model to better identify subsequent feature combinations that may be desirable and/or practical to manufacture.