Automated Incident Report Review via ML Narrative Analysis
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Solution Overview
Problem
The existing public safety incident reporting systems require multiple days for incident reports to be reviewed and finalized due to manual checks by human reviewers, leading to inefficiencies and inaccuracies.
Innovation Solution
An automated system that uses machine learning models to review unstructured narrative text in incident reports, identifying potential corrections before human review, by applying trained models specific to incident types and incorporating supplemental information from audio or video files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review by human reviewers is used, then data integrity and accuracy are checked, but the review process takes multiple days
Solution Approach 1:
The patent segments the review process into two distinct phases: automated preliminary review using machine learning models to check basic data integrity and completeness, followed by human reviewer focus on more complex analytical assessment. This segmentation allows the time-consuming automated checks to be performed quickly by computers while human reviewers concentrate on higher-level judgment, thereby reducing overall review time while maintaining reliability.
Solution Approach 2:
The patent implements preliminary automated review actions before human reviewers examine the incident reports. The machine learning models perform initial screening, data completeness verification, and obvious error detection in advance, so that when human reviewers receive the reports, many basic issues have already been identified and can be addressed more efficiently, reducing the total review time.
2Reliability
If multiple human reviewers are used, then comprehensive review is achieved, but the process becomes more complex
Solution Approach 1:
The patent introduces an automated machine learning review system as an intermediary between incident report submission and human reviewer assessment. This intermediary layer handles routine verification tasks, data completeness checks, and preliminary analysis, thereby simplifying the workflow that human reviewers must navigate while maintaining comprehensive review quality through the combination of automated and human expertise.
3Productivity
If automated review is implemented, then processing speed increases, but handling of unstructured narrative text becomes challenging
Solution Approach 1:
The patent replaces manual mechanical reading and analysis of unstructured narrative text with automated machine learning models trained to process and interpret narrative content. These models use natural language processing techniques to analyze incident descriptions, identify key elements, and assess narrative quality, thereby enabling automated high-speed review of unstructured text that would be time-consuming for human reviewers while maintaining analysis capability.
Data Source
AI summary
A system for automated review of public safety incident reports include receiving structured incident data for an incident report from a submitting public safety officer including incident type information for the incident, receiving unstructured incident narrative text describing the incident, accessing an unstructured incident narrative feedback checking model applicable to incidents of the incident type, applying the model to the narrative text in light of supplemental information in the structured incident data or obtained from another source, identifying, by application of the model, matters in the narrative text likely to be flagged for correction by a human reviewer during a subsequent review, and providing feedback notifying the officer of the identified matters. The model may be retrained based on feedback or corrections provided by the officer in response to the notification of the identified matters or in response to requests for correction subsequently received from human reviewers.