Abstract: A process for tagging incident data captured by non-public-safety agency controlled cameras. In operation, an electronic computing device detects an occurrence of an incident and obtains an incident identifier associated with the incident. The electronic computing device determines that a non-public-safety agency controlled camera is available for capturing incident data corresponding to the incident. The electronic computing device then controls a public-safety agency controlled internet-of-things (IoT) infrastructure deployed at an incident location to emit a signal including one of an audible signal, visible signal, or a combination of audible and visible signals to enable the non-public-safety agency controlled camera to tag incident data using the emitted signal. The electronic computing device links a unique tag representing the emitted signal to the incident identifier.

Abstract: A device, system, and method for implementing role-based machine learning models is provided. A implements a supervisory machine learning model that detects a first role associated with a first object in images received from a camera. The device, from machine learning models, selects and implements a first machine learning model associated with the first role to perform analysis on the images, that is reported to one or more communication devices. After implementing the first machine learning model, the device uses the supervisory machine learning model to determine a second role associated with a second object in the images that is different from the first role, and selects, from the machine learning models, a second machine learning model associated with the second role. The device implements the second machine learning model to perform analysis on the images to change the analysis reported to the one or more communication device.

Abstract: A method and system for improving prediction of an incident affecting public safety is described. At a machine learning analytics system, the method includes obtaining a real-time video for a location of a public safety emergency call and predicting a public safety incident therefrom. At a learning processor, the method includes: receiving information related to the public safety emergency call; identifying a public safety incident type based thereon; receiving the prediction of the public safety incident; using a first public safety incident machine learning model based on the identified public safety incident type and comparing the public safety incident prediction with the information related to the public safety emergency call and identifying an incident prediction mismatch therefrom. The learning processor identifies an artifact in the real-time video that is a contributing factor for the incident prediction mismatch; and implements a model improvement action associated with the artifact.

Abstract: Techniques for improving admissibility of electronic evidence are provided. An indication that a piece of evidence has been presented to and rejected by a court may be received. The indication includes a reason the piece of evidence was rejected. Metadata related to the piece of evidence is updated in the evidence management system to indicate the reason why the piece of evidence was rejected. Analytics are used to locate additional incidents similar to the incident in the evidence management system. Additional rejected evidence associated with the located incidents that are associated with the piece of evidence that has been rejected by the court based on metadata associated with the additional rejected evidence are identified. Artificial intelligence is used to analyze the rejected evidence and the additional rejected evidence to determine commonalities between the reasons for rejection. A recommended corrective action to prevent future rejection of later gathered evidence is output.

Abstract: A process of notifying users about publicly available data. In operation, an authorization server receives a request from a user to access a data item. The authorization server then determines a restriction status assigned to the data item. When the restriction status indicates that the data item is not publicly available for access, the authorization server identifies access rights associated with the user to the data item. If access rights indicate that the user does not have access to the data item, the authorization server denies user access to the data item, and responsively monitors for a reclassification of the restriction status assigned to the data item. The authorization server transmits a notification to the user indicating that the data item is publicly available for access by the user in response to detecting a reclassification of the restriction status indicating that the data item is publicly available.

Abstract: A process for tagging incident data captured by non-public-safety agency controlled cameras. In operation, an electronic computing device detects an occurrence of an incident and obtains an incident identifier associated with the incident. The electronic computing device determines that a non-public-safety agency controlled camera is available for capturing incident data corresponding to the incident. The electronic computing device then controls a public-safety agency controlled internet-of-things (IoT) infrastructure deployed at an incident location to emit a signal including one of an audible signal, visible signal, or a combination of audible and visible signals to enable the non-public-safety agency controlled camera to tag incident data using the emitted signal. The electronic computing device links a unique tag representing the emitted signal to the incident identifier.