CPC Class H04W

A method of reality augmentation, including:

Multiple application devices (such as multiple application modules (MAMs) and multiple application units (MAUs) for receiving of signals in wireless distribution systems (WDSs), including but not limited to distributed antenna systems (DASs), and providing a variety of network services are disclosed. The multiple application devices are wireless telecommunication circuitry associated with wireless distribution components in a WDS. By associating multiple application devices into components of a WDS, network services and applications within the WDS can be provided. A multiple application device includes a multiple applications processor and is configured to: receive at least one of downlink and uplink signals; determine that a request has been received in one of the downlink and uplink for a service from another device; execute, via the at least one multiple applications processor, an application layer application corresponding to the requested service; and communicate application level information sufficient to perform the requested service.

The invention relates to a method for deactivating the locking of at least one door of an accommodating device, said method comprising: effecting the unlocking of the at least one door, measuring the time since at least one event detected by an access-monitoring device of the accommodating device has occurred, and effecting, if the at least one door is open and the measured time exceeds a specified time threshold value, the deactivation of the locking of the at least one door in such a way that the at least one door cannot be locked. The invention further relates to an access-monitoring device, a computer program, an accommodating device, a system, and a use of the accommodating device.

A mobile electronic device such as a smartphone is used in conjunction with a deep learning system to detect and respond to personal danger. The deep learning system monitors current information (such as location, audio, biometrics, etc.) from the smartphone and generates a risk score by comparing the information to a routine profile for the user. If the risk score exceeds a predetermined threshold, an alert is sent to the smartphone which presents an alert screen to the user. The alert screen allows the user to cancel the alert (and notify the deep learning system) or confirm the alert (and immediately transmit an emergency message). Multiple emergency contacts can be designated, e.g., one for a low-level risk, another for an intermediate-level risk, and another for a high-level risk, and the emergency message can be sent to a selected contact depending upon the severity of the risk score.