Incident Scene Reconstruction with Visual Discrepancy Highlighting
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Solution Overview
Problem
In situations where multiple witnesses provide differing or inconsistent recollections of an incident scene, it is difficult for interrogators to quickly identify and prioritize meaningful differences in witness statements.
Innovation Solution
A computer-implemented method and system that utilizes a learning machine to generate a visual representation of an incident scene, distinguishing consistently described elements from inconsistently described elements using visual effects, such as blurring, to highlight discrepancies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If witness statements are analyzed manually to identify differences, then accuracy in detecting inconsistencies can be maintained, but the time and effort required increases significantly
Solution Approach 1:
The patent replaces manual mechanical analysis of witness statements with an automated computer-based system that uses natural language processing and data visualization algorithms to identify inconsistencies, thereby eliminating the time-consuming manual review process while maintaining detection accuracy
Solution Approach 2:
The system creates visual representations (copies) of witness statements in the form of scene reconstructions, allowing interrogators to compare multiple witness accounts by viewing their respective visual representations side-by-side, which accelerates the identification of discrepancies without sacrificing analytical precision
2Loss of information
If detailed analysis of each witness statement is performed, then comprehensive understanding of differences is achieved, but the complexity of the analysis process increases
Solution Approach 1:
The patent segments the complex analysis task into distinct components: automatic extraction of scene elements from witness statements, comparison of elements across multiple statements, identification of consistent versus inconsistent elements, and visual representation of results. This segmentation simplifies the overall process while preserving comprehensive analysis capabilities
Solution Approach 2:
The system introduces an intermediary computational layer that processes witness statements and generates visual representations, acting as a mediator between the raw text data and the interrogator's analysis needs. This intermediary handles the complexity of text analysis while presenting simplified visual outputs
3Loss of information
If visual representations are generated for all scene elements, then complete scene reconstruction is achieved, but the ability to highlight important discrepancies is reduced
Solution Approach 1:
The patent applies local quality by differentiating the visual representation of scene elements based on their consistency status across witness statements. Consistently described elements are displayed with one visual style while inconsistently described elements are highlighted with distinct visual characteristics, allowing important discrepancies to stand out while maintaining complete scene reconstruction
Solution Approach 2:
The system uses color changes and visual effects to distinguish between consistent and inconsistent scene elements in the generated representations, enabling interrogators to quickly identify areas of discrepancy while viewing the complete reconstructed scene
Data Source
AI summary
A method, system and computer program product for visual representation of an incident scene is disclosed. The method includes generating, using an at least one processor, a set of inputs for causing an at least one learning machine to create, for display on a screen, a visual representation of the incident scene corresponding to witness statements. The visual representation includes depictions of a plurality of scene elements. The method also includes providing the set of inputs to the at least one learning machine to cause the at least one learning machine to create the visual representation of the incident scene.