Emergency Call Keyword Extraction With Visual Scene Correlation
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Solution Overview
Problem
Existing emergency communication systems struggle to accurately identify objects and locations in emergency calls due to imprecise caller descriptions and discrepancies between the location of the caller and the actual emergency site, leading to inefficiencies in emergency response.
Innovation Solution
A system that combines voice recognition and visual content analysis to extract key words and phrases from emergency calls, correlates them with spatial and temporal data, and annotates relevant visual content for emergency responders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If voice recognition is used to extract keywords from emergency calls, then information extraction speed is improved, but accuracy of identifying objects and locations deteriorates due to imprecise caller descriptions
Solution Approach 1:
The patent introduces an intermediary system that bridges voice recognition output and visual content identification. The system uses extracted keywords as queries to search visual content databases, acting as a mediator that translates imprecise verbal descriptions into precise visual identifications. This intermediary process maintains the speed benefit of automated voice recognition while improving accuracy through visual verification.
Solution Approach 2:
The patent replaces the mechanical process of manual emergency scene assessment with an automated system combining voice recognition, image processing, and database searching. This substitution enables rapid information extraction while using computational algorithms to improve identification accuracy beyond what is possible through voice alone.
2Measurement precision
If manual analysis of emergency calls is used, then accuracy of identifying objects and locations is improved, but response time deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-organizing visual content into databases with metadata tags and by setting up automated voice recognition systems ready to process calls immediately. When an emergency call is received, the system can quickly query pre-processed visual databases using extracted keywords, eliminating the need for manual analysis while maintaining high accuracy through pre-established data structures.
Solution Approach 2:
The system performs self-service by automatically extracting keywords from emergency calls, querying visual content databases, and generating identification results without human intervention. This automated self-service process maintains the accuracy benefits of careful analysis while dramatically reducing response time.
3Productivity
If automated systems are used to process emergency calls, then response speed is improved, but complexity of the system increases
Solution Approach 1:
The patent applies universality by designing a multi-functional system that handles voice recognition, image processing, database searching, and result integration through a single integrated platform. This universal system processes different types of emergency information (objects, locations, people) using the same core architecture, which manages complexity through functional consolidation rather than separate specialized systems.
Solution Approach 2:
The patent merges multiple processing functions into a unified system architecture. The voice recognition output, visual content analysis, database querying, and result generation are combined into an integrated workflow. This merging reduces system complexity by eliminating interfaces between separate systems while maintaining high response speed through optimized internal processing.
4Measurement precision
If visual content analysis is performed on all emergency scenes, then identification accuracy is improved, but processing time and computational resources worsen
Solution Approach 1:
The patent extracts only the relevant visual information needed for emergency response by using keyword-based queries to search visual content databases. Instead of analyzing all visual content, the system extracts and processes only those images and video frames that contain the objects, locations, or people mentioned in the emergency call, dramatically reducing processing time while maintaining identification accuracy.
Solution Approach 2:
The patent applies local quality by directing full visual analysis resources only to specific regions or frames of visual content that are most relevant to the emergency situation. The system identifies key areas of interest based on extracted keywords and concentrates processing power on those local regions, rather than uniformly analyzing all visual content, thus improving efficiency without sacrificing accuracy.
Data Source
AI summary
A method includes receiving visual content of a physical location, the visual content identified by a content timestamp, the physical location identified by a spatial identifier; receiving audio content of a call; performing voice recognition on the call to extract a first audio symbol; receiving a first timestamp of the call, the first timestamp indicating a time at which the call was initiated or a time extracted from the call by voice recognition; and determining a feature in the visual content, at least in part based on the first audio symbol, the feature defined by a person, object, or situation.