Dynamic Talkgroup Generation From In-Call Data
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Solution Overview
Problem
In public-safety environments, talkgroups are generated inefficiently, leading to wastage of network resources due to inactive communication devices receiving unnecessary data and simultaneous transmissions causing delays.
Innovation Solution
A system and method that utilizes machine-learning algorithms to generate initial talkgroups based on incident indications and monitors in-call data to suggest changes, optimizing device inclusion and network resource usage through a feedback loop.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If static talkgroups are generated based solely on incident type, then talkgroup generation is simple and fast, but network resources are used inefficiently due to inactive devices receiving unnecessary data
Solution Approach 1:
The patent transitions from static talkgroup generation based solely on incident type to dynamic talkgroup adjustment using machine learning algorithms that process in-call data in real-time. The system continuously monitors communication patterns and automatically modifies talkgroup compositions to include only active devices, thereby improving network resource efficiency while maintaining rapid response capabilities
Solution Approach 2:
The system implements a feedback loop where in-call data from active communication devices is collected and processed by machine learning algorithms. This feedback mechanism enables the system to learn from actual communication patterns and adjust talkgroup assignments dynamically, ensuring that network resources are allocated efficiently based on real-time device activity status
2Reliability
If more communication devices are included in talkgroups, then coverage is improved, but transmission conflicts increase causing delays and failures
Solution Approach 1:
Instead of including all potentially relevant devices in talkgroups (excessive action), the system uses machine learning algorithms to identify and include only the subset of devices that are actually active and need to receive communications (partial action). This approach maintains adequate coverage for incident response while eliminating unnecessary devices that would cause transmission conflicts and delays
3Loss of energy
If machine learning algorithms are used to generate talkgroups, then network resource efficiency is improved, but system complexity increases
Solution Approach 1:
The system employs machine learning algorithms that automatically process in-call data, identify active devices, and adjust talkgroup compositions without requiring manual intervention. The algorithms self-train and self-optimize based on accumulated communication patterns, reducing the need for complex manual configuration and management while improving network resource efficiency
Data Source
AI summary
A device, system and method for training machine-learning algorithms to generate talkgroups based on in-call data is provided. The device generates, via a machine-learning algorithm, an initial talkgroup based on an incident indication of an incident, the initial talkgroup comprising communication devices communicating via channels, the machine-learning algorithm initially trained to: generate talkgroups based on incident indications; and make changes to the talkgroups. The device receives in-call data such as communications in the initial talkgroup and/or call-transmission metadata, and generates, via the machine-learning algorithm, a suggested change to the initial talkgroup based on the in-call data. The device determines a machine-learning score of the initial talkgroup or the suggested change, indicative of a threshold-based positive or negative reinforcement of efficiency of the initial talkgroup or the suggested change, and implements a machine-learning feedback loop that provides the score to the machine-learning algorithm for further training thereof.