Abstract: A device, system and method for providing indications of a discrepancy between a video and a textual description of an incident. A public-safety computing device determines a discrepancy between a video of an incident and a textual description of the incident, the textual description being a component of a public-safety incident report associated with the incident. The device, in response to determining the discrepancy, controls a notification device to provide an indication of the discrepancy.

Abstract: Methods and systems for determining an action to be executed in response to a user query as a function of pre-query context information. One system includes an electronic computing device that includes a microphone, a transceiver, and an electronic processor. The electronic processor is configured to receive audio data from the microphone prior to receiving a user query, and determine, at predetermined time intervals, context information from the audio data. The electronic processor is further configured to store the context information in a memory. The electronic processor is further configured to receive second audio data from the microphone, and determine that the second audio data corresponds to the user query. The electronic processor is further configured to determine an action to be executed as a function of the second audio data and the context information, and execute the action in response to the user query.

Abstract: A device, system and method for providing indications of a discrepancy between a video and a textual description of an incident. A public-safety computing device determines a discrepancy between a video of an incident and a textual description of the incident, the textual description being a component of a public-safety incident report associated with the incident.

Abstract: An example method and apparatus for processing accidental emergency calls is provided herein. The example method includes monitoring an emergency call with an active learning artificial intelligence (AI) bot implemented by a processor. The example method further includes determining, by the AI bot, that the emergency call was disconnected prior to a call taker resolving the emergency call. The example method additionally includes providing, by the AI bot, a score indicative of the likelihood that the emergency call was an accidental call, wherein the call may be based on real-time and historical data. The example method also includes recommending, by the AI bot, a response to the emergency call based upon the score.

Abstract: An example method and apparatus for processing accidental emergency calls is provided herein. The example method includes monitoring an emergency call with an active learning artificial intelligence (AI) bot implemented by a processor. The example method further includes determining, by the AI bot, that the emergency call was disconnected prior to a call taker resolving the emergency call. The example method additionally includes providing, by the AI bot, a score indicative of the likelihood that the emergency call was an accidental call, wherein the call may be based on real-time and historical data. The example method also includes recommending, by the AI bot, a response to the emergency call based upon the score.

Abstract: Methods and systems for determining an action to be executed in response to a user query as a function of pre-query context information. One system includes an electronic computing device that includes a microphone, a transceiver, and an electronic processor. The electronic processor is configured to receive audio data from the microphone prior to receiving a user query, and determine, at predetermined time intervals, context information from the audio data. The electronic processor is further configured to store the context information in a memory. The electronic processor is further configured to receive second audio data from the microphone, and determine that the second audio data corresponds to the user query. The electronic processor is further configured to determine an action to be executed as a function of the second audio data and the context information, and execute the action in response to the user query.

Abstract: A process for improving dispatch of different types of law enforcement patrols (LEPs) for achieving a desired deterrent effect includes first receiving patrol route information indicating patrol routes taken by first LEPs, and then determining, as a function of a type of each of the first LEPs, a threshold level of deterrence reached for each of the patrol routes taken. Subsequently, a threshold level of deterrence desired (TLoDD) for each of the plurality of patrol routes is determined as a function of historical and/or real-time crime information. A first patrol route having a threshold level of deterrence reached (TLoDR) that is less than a TOoDD is identified and, as a function of the difference, a particular type of second LEP having a sufficient deterrent effect factor to compensate for the difference is identified. An available responding second LEP of the particular type is then identified for dispatch.

Abstract: A process for displaying crime deterrent effects includes first receiving patrol route information indicating a patrol route taken by a law enforcement patrol and determining a type of the law enforcement patrol. Subsequently, a deterrent effect parameter associated with the law enforcement patrol is determined as a function of the determined type of the law enforcement patrol. Finally, an electronic display is caused to display a cartographic map of a geographic area at least partially including the patrol route taken by the law enforcement patrol and, along the patrol route taken, to display a deterrent effect indicator having a radius, width, size, and/or fade time that varies as a function of the type of the determined deterrent effect parameter.

Abstract: A process for improving dispatch of different types of law enforcement patrols (LEPs) for achieving a desired deterrent effect includes first receiving patrol route information indicating patrol routes taken by first LEPs, and then determining, as a function of a type of each of the first LEPs, a threshold level of deterrence reached for each of the patrol routes taken. Subsequently, a threshold level of deterrence desired (TLoDD) for each of the plurality of patrol routes is determined as a function of historical and/or real-time crime information. A first patrol route having a threshold level of deterrence reached (TLoDR) that is less than a TOoDD is identified and, as a function of the difference, a particular type of second LEP having a sufficient deterrent effect factor to compensate for the difference is identified. An available responding second LEP of the particular type is then identified for dispatch.

Abstract: A process for displaying crime deterrent effects includes first receiving patrol route information indicating a patrol route taken by a law enforcement patrol and determining a type of the law enforcement patrol. Subsequently, a deterrent effect parameter associated with the law enforcement patrol is determined as a function of the determined type of the law enforcement patrol. Finally, an electronic display is caused to display a cartographic map of a geographic area at least partially including the patrol route taken by the law enforcement patrol and, along the patrol route taken, to display a deterrent effect indicator having a radius, width, size, and/or fade time that varies as a function of the type of the determined deterrent effect parameter.