Motorola Solutions had 26 patents in artificial intelligence during Q2 2024. Motorola Solutions Inc filed patents in Q2 2024 for training machine-learning algorithms to generate talkgroups based on in-call data, summarizing search results through clustering, improving voice-controlled channel change on two-way radios, tracking emotional state of callers during call center communications using AI, and providing inter-agency recommended courses of action based on historical incident responses. GlobalData’s report on Motorola Solutions gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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Motorola Solutions had no grants in artificial intelligence as a theme in Q2 2024.
Recent Patents
Application: Device, system, and method for training machine learning algorithms to generate talkgroups based on in-call data (Patent ID: US20240214779A1)
Motorola Solutions Inc. has developed a device, system, and method for training machine-learning algorithms to create talkgroups based on in-call data. The device generates an initial talkgroup using a machine-learning algorithm trained to generate talkgroups based on incident indications and make changes to them. In-call data, such as communications and call-transmission metadata, is used to suggest changes to the initial talkgroup. A machine-learning score is determined for the initial talkgroup or suggested changes, indicating positive or negative reinforcement of efficiency, and a feedback loop is implemented to further train the machine-learning algorithm based on these scores. The method also involves receiving acceptance or denial of suggested changes, identifying keywords in communications, removing inactive devices, monitoring active and inactive devices, analyzing call-transmission criteria, tracking usage time, and incorporating user input for changes to the talkgroup.
The device includes a communication interface and a controller that generates initial talkgroups, receives in-call data, suggests changes, determines machine-learning scores, and implements a feedback loop to train the machine-learning algorithms. Similar to the method, the device can receive acceptance or denial of changes, analyze keywords, remove inactive devices, monitor active and inactive devices, evaluate call-transmission criteria, track usage time, and incorporate user input for changes to the talkgroup. Additionally, the device can generate a plurality of machine-learning scores, weight them, and generate a final machine-learning score based on the weighted scores. Overall, the device and method aim to optimize talkgroup formation and efficiency through machine-learning algorithms trained on in-call data.
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GlobalData Patent Analytics tracks bibliographic data, legal events data, point in time patent ownerships, and backward and forward citations from global patenting offices. Textual analysis and official patent classifications are used to group patents into key thematic areas and link them to specific companies across the world’s largest industries.
