Automated Suggestion Classification Database System
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Solution Overview
Problem
Conventional information exchange strategies in cloud computing environments, such as the suggestion box model, lead to overwhelmed product managers and frustrated customers due to the sheer volume of unsorted suggestions, resulting in many good ideas going unnoticed.
Innovation Solution
Implementing a database system that allows users to submit and automatically classify suggestions directly in the user interface, enabling voting mechanisms to prioritize suggestions and ensure they are routed to the appropriate product managers, with features like region selection and user validation to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a simple suggestion box model is used for information exchange, then the system is easy to operate and implement, but the volume of unsorted suggestions overwhelms product managers and causes good ideas to go unnoticed
Solution Approach 1:
The patent segments the suggestion processing workflow into distinct automated stages: submission through user interface, classification by machine learning models, routing to appropriate product managers, and voting mechanism implementation. This segmentation automates the sorting and routing functions that previously overwhelmed product managers, maintaining ease of submission while dramatically improving processing efficiency.
Solution Approach 2:
The patent introduces an intermediary classification system that acts as a mediator between suggestion submission and product manager review. The machine learning-based classification model automatically sorts suggestions by relevance and priority before presenting them to product managers, filtering out noise and ensuring that only the most valuable suggestions require human attention.
2Measurement precision
If manual sorting of suggestions is performed, then suggestions can be reviewed personally, but the sheer volume of suggestions makes it impossible to review them all
Solution Approach 1:
The patent applies preliminary action by automatically classifying and prioritizing suggestions before they reach product managers. The system pre-processes the suggestion volume using machine learning models to identify high-value suggestions, so that when product managers do review suggestions, they are already filtered and ranked, maximizing their time efficiency while maintaining evaluation accuracy.
Solution Approach 2:
The patent replaces the manual mechanical sorting process with automated machine learning-based classification systems. These systems analyze suggestion content, user profiles, and historical data to automatically rank suggestions by potential value, substituting human sorting effort with computational analysis that can handle large volumes without time constraints.
3Reliability
If all suggestions are routed to product managers, then comprehensive review is possible, but product managers become overwhelmed and frustrated
Solution Approach 1:
The patent applies local quality by routing suggestions to specific product managers based on their expertise areas and the classification of each suggestion. Instead of uniformly distributing all suggestions to all managers, the system matches suggestions to the appropriate local expert, reducing overall system complexity while ensuring comprehensive coverage through specialized routing.
Solution Approach 2:
The patent changes the routing parameters from a simple uniform distribution to a multi-dimensional classification system that considers suggestion topic, user feedback, voting scores, and product manager expertise. This parameter transformation enables intelligent routing that maintains completeness while reducing the burden on individual managers by distributing work based on relevance and capacity.
4Productivity
If automated classification is implemented, then suggestion routing efficiency improves, but classification accuracy must be validated
Solution Approach 1:
The patent implements feedback mechanisms where product managers can validate or correct automated classifications. Their decisions are fed back into the system to continuously improve the machine learning models. This closed-loop feedback ensures that automated classification maintains high accuracy while preserving routing speed, as the system learns from human corrections without requiring manual review of every suggestion.
Data Source
AI summary
An information exchange environment may be maintained. The information exchange environment may be configurable to allow users of an application or service to exchange suggestion data with product development. A user interface may be displayed on a device of a first user of the application or service. A request from the first user to submit first suggestion data may be processed. The first suggestion data may be classified. The first suggestion data may be provided to a first product development entity.