Abstract: A device may receive user equipment (UE) location data identifying a latitude and a longitude of a UE, as reported by the UE, and may receive network location data identifying a latitude and a longitude of the UE, as reported by a core network. The device may process the UE location data and the network location data to determine a confidence score associated with an actual location of the UE. The device may process the UE location data and the network location data, when the confidence score is outside a confidence interval, to determine whether the UE location data is valid. The device may perform one or more actions based on determining whether the UE location data is valid.

Abstract: A device may receive a message identifying an incident associated with a vehicle and may forgo rebroadcast of the message when the message was previously received. The device may store the message when the message was not previously received and may calculate a distance from the device to the vehicle based on a received power associated with the message. The device may determine whether to stop rebroadcasting the message based on a stop condition and based on the distance or a current location of the device and may calculate a particular coverage area of a rebroadcasted message based on coverage areas of the device and the vehicle. The device may determine, based on not determining to stop rebroadcasting the message and based on the distance or the particular coverage area, a delay time to wait before rebroadcasting the message and may rebroadcast the message after the delay time expires.

Abstract: Systems and methods described herein provide location-based routing for edge networks. A network device receives routing rules associating geohash tiles, for a region of interest, with one or more virtual Road Side Units (vRSUs). The network device receives a message from a vehicle client in the region of interest. The message includes a message header that identifies geospatial coordinates of a vehicle. The network device calculates a geohash based on the geospatial coordinates in the message header and associates, based on the routing rules, the geohash with one of the vRSU. The network device routes the message to the one of the vRSUs based on the associating.

Abstract: A device may receive user equipment (UE) location data identifying a latitude and a longitude of a UE, as reported by the UE, and may receive network location data identifying a latitude and a longitude of the UE, as reported by a core network. The device may process the UE location data and the network location data to determine a confidence score associated with an actual location of the UE. The device may process the UE location data and the network location data, when the confidence score is outside a confidence interval, to determine whether the UE location data is valid. The device may perform one or more actions based on determining whether the UE location data is valid.

Abstract: A device may receive user equipment (UE) location data identifying a latitude and a longitude of a UE, as reported by the UE, and may receive network location data identifying a latitude and a longitude of the UE, as reported by a core network. The device may process the UE location data and the network location data to determine a confidence score associated with an actual location of the UE. The device may process the UE location data and the network location data, when the confidence score is outside a confidence interval, to determine whether the UE location data is valid. The device may perform one or more actions based on determining whether the UE location data is valid.

Abstract: A system described herein may provide a technique for the real-time determination of events, objects, focal points, or the like to be captured by one or more cameras in a multi-camera environment. Such determination may be based on “crowdsourced” data from multiple User Equipment (“UEs”). The crowdsourced data may include positioning and/or pose information associated with UEs. The positioning information for a given UE may include location information, and the pose information may include an azimuth angle, magnetic declination, or other suitable information indicating where a particular physical facet of the UE is facing. For example, the pose information may be used to indicate or infer where a camera of the UE is pointed. One or more actuatable cameras may be displaced, rotated, etc. to capture video at one or more identified crowdsourced focal points.

Abstract: A device may receive user equipment (UE) location data identifying a latitude and a longitude of a UE, as reported by the UE, and may receive network location data identifying a latitude and a longitude of the UE, as reported by a core network. The device may process the UE location data and the network location data to determine a confidence score associated with an actual location of the UE. The device may process the UE location data and the network location data, when the confidence score is outside a confidence interval, to determine whether the UE location data is valid. The device may perform one or more actions based on determining whether the UE location data is valid.

Abstract: A device may receive a message identifying an incident associated with a vehicle and may forgo rebroadcast of the message when the message was previously received. The device may store the message when the message was not previously received and may calculate a distance from the device to the vehicle based on a received power associated with the message. The device may determine whether to stop rebroadcasting the message based on a stop condition and based on the distance or a current location of the device and may calculate a particular coverage area of a rebroadcasted message based on coverage areas of the device and the vehicle. The device may determine, based on not determining to stop rebroadcasting the message and based on the distance or the particular coverage area, a delay time to wait before rebroadcasting the message and may rebroadcast the message after the delay time expires.

Abstract: Systems and processes for using display representations are provided. For example, at a first time, a first plurality of objects is displayed, and a first plurality of relationships associated with the first plurality of objects is identified. A display representation is obtained, including a plurality of first object representations as displayed at the first time and a plurality of first relationship representation as identified as the first time. At a second time, an input is received from the user. In response to receiving the input from the user, the display representation is retrieved, the retrieved display representation including a plurality of second object representations corresponding to a second plurality of displayed object as displayed at the second time and a plurality of second relationship representations as identified at the second time. An output responsive to the input is provided based on the retrieved display representation.

Abstract: An exemplary computer vision system implemented by a multi-access edge computing (“MEC”) server integrated within a provider network accesses an image captured by an image capture device coupled with the MEC server via the provider network. The image depicts a product that is to comply with a predefined product standard. The computer vision system identifies a criterion that is associated with the predefined product standard, and a region of the image that is associated with a product condition that is to be verified for compliance with the criterion. The computer vision system determines whether the product condition complies with the identified criterion for the identified region of the image, and, based on this determination, generates compliance verification data indicative of whether the product complies with the predefined product standard. The computer vision system also provides the compliance verification data to a monitoring system separate from the MEC server.

Abstract: Systems and processes for operating an intelligent automated assistant are provided. An example process for performing a task includes, at an electronic device having one or more processors and memory, determining whether a shortcut criteria is met and in accordance with a determination that the shortcut criteria is met: displaying a first affordance corresponding to a first task of a shortcut over a user interface, determining whether the first task can be completed without displaying an application interface corresponding to the first task, in accordance with a determination that the first task can be completed without displaying the application interface, performing the first task while displaying the first affordance, and in accordance with a determination that the first task cannot be completed without displaying the application interface, displaying the application interface, and completing the first task using the application interface.

Abstract: A system described herein may provide a technique for the real-time determination of events, objects, focal points, or the like to be captured by one or more cameras in a multi-camera environment. Such determination may be based on “crowdsourced” data from multiple User Equipment (“UEs”). The crowdsourced data may include positioning and/or pose information associated with UEs. The positioning information for a given UE may include location information, and the pose information may include an azimuth angle, magnetic declination, or other suitable information indicating where a particular physical facet of the UE is facing. For example, the pose information may be used to indicate or infer where a camera of the UE is pointed. One or more actuatable cameras may be displaced, rotated, etc. to capture video at one or more identified crowdsourced focal points.

Abstract: A system described herein may provide a technique for the real-time determination of events, objects, focal points, or the like to be captured by one or more cameras in a multi-camera environment. Such determination may be based on “crowdsourced” data from multiple User Equipment (“UEs”). The crowdsourced data may include positioning and/or pose information associated with UEs. The positioning information for a given UE may include location information, and the pose information may include an azimuth angle, magnetic declination, or other suitable information indicating where a particular physical facet of the UE is facing. For example, the pose information may be used to indicate or infer where a camera of the UE is pointed. One or more actuatable cameras may be displaced, rotated, etc. to capture video at one or more identified crowdsourced focal points.

Abstract: Systems and processes for operating an intelligent automated assistant are provided. An example process for performing a task includes, at an electronic device having one or more processors and memory, determining whether a shortcut criteria is met and in accordance with a determination that the shortcut criteria is met: displaying a first affordance corresponding to a first task of a shortcut over a user interface, determining whether the first task can be completed without displaying an application interface corresponding to the first task, in accordance with a determination that the first task can be completed without displaying the application interface, performing the first task while displaying the first affordance, and in accordance with a determination that the first task cannot be completed without displaying the application interface, displaying the application interface, and completing the first task using the application interface.

Abstract: An exemplary computer vision system implemented by a multi-access edge computing (“MEC”) server integrated within a provider network accesses an image captured by an image capture device coupled with the MEC server via the provider network. The image depicts a product that is to comply with a predefined product standard. The computer vision system identifies a criterion that is associated with the predefined product standard, and a region of the image that is associated with a product condition that is to be verified for compliance with the criterion. The computer vision system determines whether the product condition complies with the identified criterion for the identified region of the image, and, based on this determination, generates compliance verification data indicative of whether the product complies with the predefined product standard. The computer vision system also provides the compliance verification data to a monitoring system separate from the MEC server.