Audio Narrative Transcription for Rapid Person-of-Interest Identification
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Solution Overview
Problem
Conventional CAD systems face significant delays in combining and analyzing witness statements to identify suspects, allowing potential suspects to flee before appropriate action can be taken.
Innovation Solution
A CAD system that automates the processing of audio information by converting narrative statements to text, determining incriminating scores based on publicly known and unknown information, and triggering automated responsive actions when a cumulative score exceeds a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional CAD systems manually combine and analyze witness statements, then analysis accuracy can be maintained, but processing time is significantly delayed allowing suspects to flee
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated electronic processing. Audio recordings are automatically transcribed to text, and the system automatically compares witness statements against incident details to generate incriminating scores, eliminating the time-consuming manual combination and analysis of statements while maintaining accuracy through structured comparison algorithms
Solution Approach 2:
The system performs self-service by automatically processing witness statements without requiring continuous human intervention. The automated transcription service converts audio to text, the comparison algorithm independently evaluates incriminating information, and the system generates cumulative scores and identifies persons of interest autonomously, freeing officers from manual analysis tasks
2Productivity
If automated processing is implemented to reduce delays, then processing speed increases, but system complexity and automation requirements increase
Solution Approach 1:
The patent segments the complex automated processing task into distinct manageable modules: audio recording capture, automatic transcription to text, comparison of statements against incident details, incriminating score generation, cumulative score calculation, and person of interest identification. This segmentation allows the system to achieve high processing speed through automated electronic operations while managing complexity through modular design
Solution Approach 2:
The system transforms audio data into text data through transcription, changing the parameter form from acoustic signals to readable text. This parameter change enables automated textual comparison and analysis, dramatically increasing processing speed while the structured parameter transformation manages the complexity of automation through standardized data formats
3Loss of information
If multiple officers interview multiple witnesses in parallel, then information gathering is comprehensive, but combining and analyzing the information takes significant time
Solution Approach 1:
The patent merges multiple witness statements and incident details into a unified automated analysis process. The system simultaneously processes all recorded statements, automatically transcribes them to text, and compares each statement against the incident details to generate incriminating scores. This merging of information streams into a single automated workflow maintains complete information from all witnesses while eliminating the time required for manual combination and analysis
Data Source
AI summary
Methods and systems for processing audio information to identify persons of interest in regard to a public-safety incident. An example method includes receiving human-generated narrative statements as audio and converting the narrative statements into corresponding digital texts. The method further includes determining incriminating scores for each of the narrative statements by identifying, in the digital texts, incident information classified as not being publicly known. The method further includes determining a cumulative score for an individual based on the incriminating scores tagged to the individual, making a determination of whether the individual is a person of interest based on a comparison of the cumulative score with a fixed threshold, and performing at least one automated responsive action based on the determination.