AI Feature Recommendation from User Content
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Solution Overview
Problem
Manufacturers face challenges in capturing and comprehending user-generated content from various platforms, making it difficult to gather valuable feedback for product improvements, as they need to manually access and analyze reviews across multiple sites, which is time-consuming and error-prone.
Innovation Solution
An artificial intelligence system that uses machine learning to identify and process user-generated content, distinguishing between feature-related and non-feature related feedback, and provides recommended features based on topic modeling and weighting, allowing developers to automatically extract insights without manual access to multiple platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manufacturers manually access and analyze user-generated content from multiple platforms, then they can gather valuable feedback for product improvements, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent introduces an intermediary system comprising web crawlers, natural language processing modules, and machine learning models that act as a mediator between user-generated content on various platforms and manufacturers. This intermediary automatically collects, processes, and analyzes feedback from multiple sources, delivering insights to manufacturers without requiring their direct involvement in the manual collection and analysis process, thereby resolving the contradiction between comprehensive feedback gathering and time consumption
Solution Approach 2:
The patent replaces the mechanical manual process of accessing, reading, and analyzing user-generated content with an automated computational system. Web crawlers programmatically scrape content from platforms, NLP algorithms automatically parse and understand the text, and ML models generate insights without human intervention. This substitution eliminates the time-consuming and error-prone manual analysis while preserving comprehensive feedback collection
2Loss of information
If manufacturers monitor all user-generated content across multiple platforms, then they can capture comprehensive feedback, but the complexity of accessing and processing content increases
Solution Approach 1:
The patent creates a universal multi-functional system that consolidates multiple capabilities into a single platform: web crawlers that can access various platforms, NLP modules that process different content types, and ML models that generate multiple insight types. This universal system handles diverse feedback sources and formats through standardized processes, reducing the operational complexity that would arise from managing separate tools for each function while maintaining comprehensive feedback capture
Solution Approach 2:
The intermediary system serves as a centralized mediator that manages the complexity of multi-platform monitoring. It provides a unified interface for manufacturers to access all feedback regardless of source platform, abstracting away the underlying complexity of different website structures, access methods, and content formats into a single streamlined process
3Measurement precision
If users manually read and comprehend each review to extract feedback, then they can understand detailed user experiences, but the process is not practical for acquiring feedback from many different users
Solution Approach 1:
The patent replaces the mechanical human reading and comprehension process with automated NLP and ML systems. These systems use sophisticated language understanding algorithms to parse review text, identify sentiment, extract key themes, and generate structured insights with high precision. The automation maintains the depth of understanding that manual reading provides while increasing productivity by processing thousands of reviews simultaneously without fatigue or error
Solution Approach 2:
The patent transforms the feedback analysis process by changing the parameters of processing capacity and speed. While maintaining high precision through advanced NLP techniques that capture nuanced user experiences, the system processes feedback at scales impossible for manual analysis. The ML models adjust processing parameters dynamically to optimize both understanding depth and acquisition rate based on input volume and complexity
Data Source
AI summary
Provided is a system and method for automated recommendation of new features for addition to an item based on user-generated feedback. In one example, the method may include receiving, via a user interface, a search query associated with an object, retrieving user-generated content that describes the object based on the received search query, identifying, via a machine learning model, one or more features to be added to the object based on the retrieved user-generated content, and outputting identifiers of the one or more features to be added to the object via the user interface.