Abstract: A method and data processing device for detecting a communication between a first and second entity. The method includes identifying whether a previous communication between the first and second entity has been detected. In response to identifying that the previous communication between the first and second entity has been detected, the method determines an elapsed time since detection of the previous communication. The method predicts a topic of the communication, in part based on the determined elapsed time. The topic corresponds to a specific domain from among a plurality of available domains for automatic speech recognition (ASR) processing. The method triggers selection and activation of a first domain specific (DS) ASR engine from among a plurality of available DS ASR engines to utilize a smaller resource footprint than a general ASR engine and facilitate recognition of specific vocabulary and context, in part, based on the elapsed time since the previous communication.

Abstract: Systems and methods are provided for managing a flashlight function on a mobile electronic communications device having a curved display surface that includes a front screen area, one or more edge screen areas and a back flashlight. By way of example, the disclosed technique entails detecting that the user has turned on the flashlight function, determining whether the back flashlight is facing toward the user, and enabling the back flashlight when it is determined that the back flashlight is not facing toward the user. When it is determined that the back flashlight is facing toward the user, a flashlight on a screen edge facing away from the user is enabled.

Abstract: An electronic device includes a touch-sensitive display, one or more processors, and one or more motion sensors. The touch-sensitive display detects touch input at a surface of the touch-sensitive display, with the touch input identifying a user input command requesting performance of a control operation. The one or more motion sensors determine whether the electronic device moves in three-dimensional space while the touch input is occurring. The one or more processors preclude execution of the control operation in response to the touch input where the electronic device moved in the three-dimensional space while the touch input was occurring, thereby preventing control operations in response to inadvertent touching when the electronic device is being picked up or moved.

Abstract: In aspects of generating action suggestions based on a change in user mood, a computing device implements an action generation module that generates action suggestions. The action generation module analyzes sensor data from sensors to determine a current user state associated with a user. The action generation module also analyzes environment data received from Internet-of-Things (IoT) devices to determine a current environment context associated with the user. The action generation module can detect a change in a user mood based on a comparison of the current user state with user history data that includes user state parameters and environment context parameters that indicate the user mood. The action generation module can analyze the user history data to determine the user state parameters and/or the environment context parameters that correlate to the change in the user mood, and generate an action suggestion that is intended to modify the current user state.

Abstract: A system receives a first voice input from a first user, such as a baby or a person who has had a stroke. Although the first user intends to communicate a particular meaning, the first voice input is not in a language that is known to the system and thus the system does not know the particular meaning that the first user intended. After receiving the first voice input, a second voice input is received from a second user. This second voice input is in a language that is known to the system. The system determines a meaning of the second voice input, associates this meaning with the first voice input, and uses this association to train a machine learning system. This machine learning system is used to attempt to understand the meaning of subsequent voice inputs received from the first user.

Abstract: An Internet-of-things device can detect, using one or more sensors, a condition exceeding a predefined condition threshold. If the condition is occurring at a location outside of an area monitored by the sensors of the Internet-of-things device, a communication device can transmit an investigation request to an unmanned aircraft. The investigation request can include a request to monitor an environment at the location. The Internet-of-things device can then receive an event report identifying whether a class of event is or was occurring at the location from the unmanned aircraft. The event report can include one or more event labels. The Internet-of-things device can transform, with an artificial intelligence engine, the one or more event labels into machine learned knowledge in an Internet-of-things knowledge domain.

Abstract: A Dynamic Vision Sensor (DVS) pose-estimation system includes a DVS, a transformation estimator, an inertial measurement unit (IMU) and a camera-pose estimator based on sensor fusion. The DVS detects DVS events and shapes frames based on a number of accumulated DVS events. The transformation estimator estimates a 3D transformation of the DVS camera based on an estimated depth and matches confidence-level values within a camera-projection model such that at least one of a plurality of DVS events detected during a first frame corresponds to a DVS event detected during a second subsequent frame. The IMU detects inertial movements of the DVS with respect to world coordinates between the first and second frames. The camera-pose estimator combines information from a change in a pose of the camera-projection model between the first frame and the second frame based on the estimated transformation and the detected inertial movements of the DVS.

Abstract: A method includes detecting, by sensors, a current context associated with an electronic device. The method includes dynamically loading a neural network and selected features into a phrase-spotting audio front-end (AFE) processor. The neural network is configured, based on the current context, with at least one domain having an associated set(s) of trigger words. The method includes detecting, audio content that matches a trigger word from among the sets of trigger words associated with the at least one selected domain. The method includes in response to detecting audio content that matches the trigger word, outputting a wake-up signal to an application processor (AP). The AFE processor utilizes less computational resources than the AP. The method includes, in response to receiving the wake-up signal, the AP waking up and performing additional computation based on the matching trigger word. The method includes outputting results of the additional computation to an output device.

Abstract: Methods and systems for attaching an unmanned aircraft suspension perch to a surface with an unmanned aircraft are provided. One method includes retrieving, with the unmanned aircraft, the unmanned aircraft suspension perch. The method can include selecting, with one or more processors carried by the unmanned aircraft, an attachment location for the unmanned aircraft suspension perch. A flight engine responsive to the one or more processors can navigate the unmanned aircraft to the attachment location. The unmanned aircraft can attach the unmanned aircraft suspension perch to the surface at the attachment location. The unmanned aircraft can then suspend itself from the unmanned aircraft suspension perch, or alternatively release, with a perch interface of the unmanned aircraft, the unmanned aircraft suspension perch while the unmanned aircraft suspension perch remains attached to the surface at the attachment location.

Abstract: A method and data processing device for receiving, at a data processor, data that includes at least one personalized phrase. The method includes extracting a personalized phrase from received data. The method includes tracking, via an assigned phrase counter, each occurrence of the personalized phrase in the received data and subsequently received data. The method includes periodically comparing a value of the assigned phrase counter to pre-established count thresholds to determine when a count of the personalized phrase reaches at least one of the pre-established count thresholds. The method includes storing the personalized phrase to a phrase database and linking the personalized phrase to the one or more general phrases in the phrase database. The method includes selectively triggering a contextual response to the data and executing, by the data processor, the corresponding operation. The method includes outputting the contextual response to an output device.

Abstract: Systems and methods for providing event media distribution associated with an event to a user include receiving an electronic invitation to the event having a time and location associated therewith and displaying the electronic invitation, including a response option and one or more mode options. The mode options are associated with one or more rules regarding automatic sharing of media associated with the event. The user may give an affirmative response that they will attend, as well as a selection of one or more mode options. When user of the mobile communications device is present at the event, the user device shares information to and from the devices of other attendees in accordance with the selected mode option(s).

Abstract: Systems and methods for providing smart internet of things (IOT) menus utilize a camera of an electronic communications device and entail capturing an image of an IOT device of interest using a camera of the electronic communications device and identifying the IOT device of interest. The IOT of interest is displayed and may be selected by the device user. The user of the electronic communications device is identified and their historical behavior with respect to the selected IOT device is analyzed. A customized, rather than full, IOT device control menu is then generated and presented to the user.

Abstract: A device includes a wireless communication circuit and one or more processors operable with the wireless communication circuit. The wireless communication circuit receives one or more local area wireless communication signals identifying one or more external electronic devices operating within an environment of the device. The one or more processors select a local area wireless communication signal having a power magnitude experiencing a highest amount of change from the one or more local area wireless communication signals and perform a pairing operation with an external device identified by the local area wireless communication signal.

Abstract: A device includes a wireless communication circuit and one or more processors operable with the wireless communication circuit. The wireless communication circuit receives one or more local area wireless communication signals identifying one or more external electronic devices operating within an environment of the device. The one or more processors select a local area wireless communication signal having a power magnitude experiencing a highest amount of change from the one or more local area wireless communication signals and perform a pairing operation with an external device identified by the local area wireless communication signal.

Abstract: A central console unit includes a communication device and one or more processors. The communication device is in communication with one or more Internet-of-things devices. An artificial intelligence engine receives inputs from the Internet-of-things devices and transforms the inputs into machine-learned knowledge stored in an Internet-of-things knowledge domain. The central console unit detects at least one mobile device having other machine learned knowledge about an authorized user of the mobile device in a user knowledge domain, stored in the at least one mobile device, communicating with the communication device. When this occurs, the central console unit causes the at least one mobile device to deliver an exchange data.

Abstract: An interface accessory includes one or more sensors and one or more processors operable with the one or more sensors. An output connector can be mechanically and electrically connectable to a media consumption device. The sensor monitor one or more persons within a predefined media consumption environment about the media consumption device. The one or more processors deliver control signals to the output connector. The control signals alter a content presentation characteristic of content being presented by the media consumption device as a function of one or more personal characteristics corresponding to the one or more persons when the output connector is coupled to the media consumption device.

Abstract: A central console unit includes a communication device and one or more processors. The communication device is in communication with one or more Internet-of-things devices. An artificial intelligence engine receives inputs from the Internet-of-things devices and transforms the inputs into machine-learned knowledge stored in an Internet-of-things knowledge domain. The central console unit detects at least one mobile device having other machine learned knowledge about an authorized user of the mobile device in a user knowledge domain, stored in the at least one mobile device, communicating with the communication device. When this occurs, the central console unit causes the at least one mobile device to deliver an exchange data.

Abstract: Systems and methods for authenticating a user of a mobile communications device entail scanning an environment of the mobile device with a low power sensor and detecting a nearby person via the first sensor. In response, a higher power sensor is activated and the person is authenticated via the second sensor using a first authentication mechanism. A first level of access is granted to the person at this point. When the person moves to within a first threshold distance of the device, the person is authenticated using a second more secure authentication mechanism, and a second, higher, level of access is granted to the person.

Abstract: A Dynamic Vision Sensor (DVS) pose-estimation system includes a DVS, a transformation estimator, an inertial measurement unit (IMU) and a camera-pose estimator based on sensor fusion. The DVS detects DVS events and shapes frames based on a number of accumulated DVS events. The transformation estimator estimates a 3D transformation of the DVS camera based on an estimated depth and matches confidence-level values within a camera-projection model such that at least one of a plurality of DVS events detected during a first frame corresponds to a DVS event detected during a second subsequent frame. The IMU detects inertial movements of the DVS with respect to world coordinates between the first and second frames. The camera-pose estimator combines information from a change in a pose of the camera-projection model between the first frame and the second frame based on the estimated transformation and the detected inertial movements of the DVS.

Abstract: Systems and methods for authenticating a user of a mobile communications device entail scanning an environment of the mobile device with a low power sensor and detecting a nearby person via the first sensor. In response, a higher power sensor is activated and the person is authenticated via the second sensor using a first authentication mechanism. A first level of access is granted to the person at this point. When the person moves to within a first threshold distance of the device, the person is authenticated using a second more secure authentication mechanism, and a second, higher, level of access is granted to the person.