Machine Learning Crime Scene Analysis for Firearm Identification

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Solution Overview

Problem

Manual crime scene analysis is prone to subjective interpretations and inefficiencies, relying heavily on human experts despite advancements in electronic data capture, which can lead to disputes and inaccuracies in identifying firearms and explosives used in crimes.

Innovation Solution

Implementing a machine learning-based system that receives and processes audio, video, and other data to identify firearms and explosives by querying catalogued information, using techniques such as supervised learning, neural networks, and audio fingerprinting to determine the type, model, and origin of firearms and explosive materials.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual analysis by experts is used, then human judgment and interpretation are applied, but subjectivity and disputes in court arise leading to reduced reliability

Engineering Contradiction:

Improvereliability of crime scene analysisVSAvoidautomation level in analysis process

Core Design Contradiction:

ReliabilityVSExtent of automation

Solution Approach 1:

The patent replaces the manual mechanical analysis process performed by human experts with an automated machine learning system. The system uses audio processing, image processing, and machine learning models to automatically identify firearms and explosives, eliminating human subjectivity while maintaining high reliability through objective, reproducible analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables self-service analysis where the machine learning model autonomously processes crime scene data without requiring continuous human intervention. The automated system performs classification, identification, and analysis tasks independently, reducing reliance on manual expert review while improving consistency and reliability.

Inventive Principle:

Principle #25Self-service

2Productivity

If manual processes are used for crime scene analysis, then expert knowledge is applied, but inefficiency and time consumption increase

Engineering Contradiction:

Improveanalysis speed and efficiencyVSAvoidtime required for crime scene analysis

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces time-consuming manual analysis with automated machine learning processing. The system rapidly processes audio recordings, images, and other crime scene data using pre-trained models, significantly reducing analysis time while improving productivity through parallel processing and automated workflows.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system performs preliminary analysis by pre-processing and automatically analyzing crime scene data immediately upon receipt. Machine learning models are pre-trained on extensive datasets, enabling rapid classification and identification without requiring time-consuming manual review, thus reducing overall analysis time and improving efficiency.

Inventive Principle:

Principle #10Preliminary action

3Loss of information

If electronic data capture devices are deployed, then more crime scene information is collected, but data processing complexity and system complexity increase

Engineering Contradiction:

Improvecompleteness of crime scene dataVSAvoidcomplexity of analysis system

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent segments the complex analysis system into specialized modules: audio processing module for firearm identification, image processing module for evidence analysis, and machine learning classification module. Each module handles specific data types independently, reducing overall system complexity while maintaining comprehensive data processing capability and preventing information loss.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system employs universal machine learning models that can process multiple types of crime scene data (audio, images, videos) through a common architecture. This multi-functional approach reduces system complexity by using a unified processing framework rather than separate specialized systems for each data type, while still capturing and analyzing all relevant information.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS10509988B2Crime scene analysis using machine learning

Publication Date: 2019.12.17 MICROSOFT TECHNOLOGY LICENSING LLC

AI summary

Technologies are provided for automated crime scene analysis using machine learning. Firearm models, types, or even specific firearms may be automatically detected from captured audio files or continuous audio streams (e.g., recording microphones) using machine learning techniques. The detection may also be based on (or enhanced by) captured still images or video files/streams. Further information such as crime scene layout, wound types and locations, and similar information may be provided to the analysis service through manual input or automated capture (e.g., through analysis of image/video data). A number of firearms used in the commission of the crime may also be detected. Specific firearm types may be associated with specific crime types. Similar techniques may also be used to detect and classify types and quantity of explosive material.