Emergency Communication Contextualization via Emotional State Analysis
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Solution Overview
Problem
Current emergency management systems lack the ability to contextualize communications effectively during emergency events, failing to consider the emotional state and contextual information of users, which can lead to miscommunication and inefficiencies in response efforts.
Innovation Solution
A method and system that receive input communications from users, incorporate their emotional and contextual information, and modify output communications to enhance understandability and relevance, using machine logic algorithms to tailor messages for optimal impact during emergency situations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If emergency management systems transmit raw communications without contextualization, then communication speed is maintained, but communication effectiveness and understandability deteriorate
Solution Approach 1:
The patent introduces a contextualization module as an intermediary component between the communication receiver and the output generator. This module receives raw communications, user contextual information, and recipient profile information, then processes them to generate contextualized output communications. The intermediary handles the complexity of contextual analysis without requiring changes to the core emergency management system architecture.
Solution Approach 2:
The system segments the communication processing into distinct functional components: receiving raw communication, obtaining user contextual information, obtaining recipient profile information, analyzing the communication with contextual data, and generating contextualized output. This segmentation allows each component to be optimized independently while maintaining overall system effectiveness.
2Loss of information
If contextual information processing is added to emergency communications, then communication clarity improves, but response time increases
Solution Approach 1:
The system performs preliminary actions by pre-obtaining and storing user contextual information and recipient profile information before communications need to be processed. This advance preparation ensures that when a communication requires contextualization, the necessary background data is already available, reducing the time penalty for adding contextual analysis.
Solution Approach 2:
The contextualization process operates continuously in the background, maintaining readiness to process communications without interruption. The system continuously monitors and analyzes communications, ensuring that the useful action of information transmission continues without significant breaks or delays, even while contextual processing occurs.
3Adaptability or versatility
If emotional state analysis is incorporated into communication processing, then communication relevance improves, but processing complexity increases
Solution Approach 1:
The contextualization module serves multiple functions simultaneously: it analyzes emotional states, assesses cognitive states, evaluates communication content, and generates appropriate contextualized responses. By consolidating these diverse functions into a single multi-functional module, the system achieves high adaptability without proportionally increasing overall processing complexity.
Solution Approach 2:
The system changes the parameters of communication processing by incorporating emotional state parameters and cognitive state parameters into the analysis. Instead of processing only the literal content of communications, the system transforms the processing to include psychological and contextual parameters, enabling adaptive response generation based on the complete state of the communicating parties.
Data Source
AI summary
Software that contextualizes communications during an event by performing the following steps: (i) receiving an input communication from a first user, where the input communication includes input information relating to the event; (ii) receiving first user contextual information, where the first user contextual information pertains to an emotional state of the first user at the time the input communication was received; (iii) determining an output communication based, at least in part, on the received first user contextual information, where the output communication includes output information relating to the event; and (iv) sending the output communication to a first recipient.