Interactive Submission Review System With ML Feedback
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Solution Overview
Problem
Current user interfaces for submitting updates to content related to a point of interest often result in a limited and unengaging post-submission experience, as users are not provided with adequate feedback or opportunities to correct or supplement their submissions, leading to frustration and reduced willingness to contribute.
Innovation Solution
A system utilizing a machine learning model to analyze submissions, determine their associability with a point of interest, and provide notifications on workflow status, requesting additional information if necessary, while combining submissions for review and updating the model with new content types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a machine learning model is used to analyze submissions and provide detailed feedback, then user engagement and submission quality improve, but system complexity and processing time increase
Solution Approach 1:
The system implements a feedback mechanism where the machine learning model analyzes submissions and provides specific feedback to users about what additional information is needed or what issues prevent approval. This feedback loop engages users by showing them exactly what to improve, transforming the submission process from a black-box rejection/approval system into an interactive guidance system that helps users succeed.
Solution Approach 2:
The machine learning model acts as an intermediary between the user submission and the final approval/rejection decision. Instead of direct human review or simple automated filtering, the ML model mediates by analyzing submissions, determining associability with points of interest, and providing structured feedback that bridges the gap between user intent and system requirements.
2Productivity
If submissions are rejected without feedback, then processing speed is maintained, but user frustration increases and contribution willingness decreases
Solution Approach 1:
Instead of providing complete manual review feedback for all submissions (which would slow processing), the system applies partial automation: the ML model quickly processes all submissions to identify issues, providing feedback only when needed. This partial action approach maintains high processing speed while delivering targeted feedback that improves user satisfaction without requiring full manual intervention for every submission.
3Loss of information
If multiple submissions are combined for review, then information completeness improves, but analysis time and computational resources increase
Solution Approach 1:
The system merges multiple submissions related to the same point of interest into a single combined analysis. By consolidating submissions before ML model processing, the system achieves complete information assessment while avoiding redundant analysis. The merging occurs at the data aggregation level, and the ML model processes the unified submission set efficiently, reducing total analysis time compared to evaluating each submission separately.
Data Source
AI summary
The present disclosure provides systems and methods for an interactive user interface that allows for one or more submissions of update information related to a point of interest to be reviewed. The system may receive the submission from a computing device. The system may analyze the submission to determine the type of content, such as the name, address, website, photo, etc. related to the point of interest. The type of content may be classified using a machine learning model. The model may compare the content of the submission to a model of the type of content to determine whether the submission is approved for publishing or whether additional information is needed. The system may transmit one or more notifications to the computing device. The notifications may include updates on the workflow status of the submission.