Command Center Call Transcription Dashboard for Resource Allocation
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Solution Overview
Problem
Emergency and non-emergency calls during incidents overwhelm command centers, making it difficult for supervisors to manage resources effectively, leading to delayed responses, misallocation of resources, and increased stress on call takers.
Innovation Solution
A call management system with a supervisory dashboard that transcribes incoming audio and video calls in real-time, cross-references transcripts with keyword databases to identify key information, and displays this information in a call-by-call and all-call manner, enabling supervisors to make informed decisions about resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual call handling is used by call takers and supervisors, then personal judgment and flexibility can be applied, but the system cannot handle increasing call volumes efficiently, leading to delays and information loss
Solution Approach 1:
An automated speech recognition and natural language processing intermediary system is introduced between the incoming calls and the call takers. This intermediary automatically transcribes calls, extracts key information, and prioritizes calls based on predefined criteria, significantly increasing call handling capacity without adding more manual operators and eliminating response delays
Solution Approach 2:
The manual mechanical process of listening to and analyzing calls is replaced with an automated electronic speech recognition system. This substitution enables the system to process multiple calls simultaneously at high speed, dramatically improving productivity while maintaining accurate information capture
2Reliability
If supervisors manually review all calls to allocate resources, then accurate decision-making can be achieved, but the increasing number of calls makes this process unsustainable and causes delays
Solution Approach 1:
Instead of uniformly reviewing all calls, the system applies different levels of analysis to different calls based on their characteristics. High-priority calls identified through automated keyword detection and speech pattern analysis receive focused supervisory attention, while routine calls are handled automatically, maintaining allocation accuracy while improving overall efficiency
Solution Approach 2:
The system performs preliminary analysis of calls before they reach supervisors, automatically extracting key information, identifying priority levels, and pre-sorting calls. This preliminary action prepares calls for supervisory review, enabling accurate resource allocation decisions to be made more quickly and sustainably
3Loss of information
If all call information is captured and analyzed in detail, then complete situational awareness is achieved, but the complexity of processing and presenting this information becomes unmanageable
Solution Approach 1:
The system extracts only the most critical information from calls using automated keyword detection and natural language processing. Key entities, locations, incident types, and priority indicators are extracted and presented in a simplified format, maintaining essential information completeness while reducing processing and presentation complexity to manageable levels
Data Source
AI summary
A process is described for a call management system to provide a call transcription supervisory monitoring interactive dashboard of incoming calls at a command center. A plurality of incoming calls are received including a first call reflecting a first incident and a second call reflecting a second incident. Audio from the first and second calls are transcribed. First and second incoming call objects are displayed in a call-by-call portion of the interactive dashboard. The transcripts are cross-referenced with a call-by-call keyword database to identify call-by-call keywords of interest, and the incoming call objects are populated with the call-by-call keywords of interest. An all-call object is displayed in an all-call portion of the interactive dashboard. The transcripts are cross-referenced with an all-call keyword database to identify all-call keywords of interest, and the all-call object is populated with the all-call keywords of interest.


