Abstract: A health condition of a person may be assessed from a thermal sensor signal. By increasing performance indices of a thermal camera (for example, resolution, frame rate, sensitivity), operation may be extended to identification verification, biometric data extraction and health condition analysis, and so forth. Prediction may be carried out by monitoring a time sequence of thermal images, and consequently early warning of the health condition may be provided. The apparatus may be used for, but not limited to, personalization of smart home devices through supervised and reinforcement learnings. The application of the apparatus may be, but not limited to, smart homes, smart buildings and smart vehicles, and so forth.

Abstract: A home computing cloud (HCC) supports one or more Internet of Things (IoT) devices, possibly with different connectively protocols, in a local environment. The HCC often reduces the amount of data traffic sent to a public computing cloud (PCC) by locally processing collected device data rather than by sending the device data to the PCC for processing. This approach reduces the amount of data traffic sent over the network, improves data privacy and helps to maintain a desired quality of service level. In order to do so, the HCC may download an appropriate data analytic model from the PCC, train the model, execute the trained model to obtain prediction information from collected IoT device data, and upload the trained model to the PCC. Alternatively, the HCC and PCC may execute sub-models of the analytic model and exchange the outputs of the sub-models with each other.

Abstract: A home computing cloud (HCC) supports one or more Internet of Things (IoT) devices, possibly with different connectively protocols, in a local environment. The HCC often reduces the amount of data traffic sent to a public computing cloud (PCC) by locally processing collected device data rather than by sending the device data to the PCC for processing. This approach reduces the amount of data traffic sent over the network, improves data privacy and helps to maintain a desired quality of service level. In order to do so, the HCC may download an appropriate data analytic model from the PCC, train the model, execute the trained model to obtain prediction information from collected IoT device data, and upload the trained model to the PCC. Alternatively, the HCC and PCC may execute sub-models of the analytic model and exchange the outputs of the sub-models with each other.

Abstract: A home computing system (cloud) integrates a protocols gateway, WiFi router, cloud server, and mass storage device to support one or more Internet of Things (IoT) devices, possibly with different connectively protocols, in a local environment such as a residential home. The home computing cloud often reduces the amount of data traffic sent to a public computing cloud by locally processing collected device data rather than by sending the device data to the public computer cloud for processing. The home computing cloud may download an appropriate data analytic model from the public computing cloud, locally train (for example, reinforcement learning) the model, and locally execute the trained model to obtain prediction information from collected IoT device data. The home computing cloud also allows direct access of the connected IoT devices by user applications via the internet, through a protocols gateway and an IoT message translator.

Abstract: An aspect of the embodiments provides a compensation for the temperature rise effect on the printed circuit board (PCB) of an electronic thermostat to obtain better precision and performance. Once the measurements from the temperature sensors have stabilized, the compensated ambient temperature may be used by an associated system (for example, a Heating, Ventilation, and Air Conditioning (HVAC) system).

Abstract: Electronic thermostats are expanding to a higher integration of services that includes, for example, the integration of a home Internet-of-Things (IoT) gateway, local rule control, local machine learning capability, and higher resolution thin-film transistor (TFT) display. However, the required incorporation of higher processing power control units and/or large display typically results in a higher heat dissipation of electronic components, thus increasing the temperature around the components of an electronic thermostat. The system heat dissipation consequently degrades the accuracy of the on-board temperature sensors. An aspect of the embodiments provides a compensation for the temperature rise effect on the printed circuit board (PCB) of the electronic thermostat to obtain better precision and performance. Once the measurements from the temperature sensors have stabilized, the compensated ambient temperature may be used by an associated system (for example, a HVAC system).

Abstract: Apparatuses and methods detect a health condition of a user that may be assessed from a thermal sensor signal and/or radar sensor signal. One or more resultant biometric vectors may be generated from biometric vectors based on the thermal and radar signals, where the resultant biometric vectors contain resultant information about one or more biometric features for a user. Hazard information about the user is obtained from the one or more resultant biometric vectors, where the hazard information is indicative of a health event for the user. Consequently, an appropriate action on behalf of the user may be performed to ameliorate the health condition. The one or more resultant biometric vectors may include additional biometric features and/or a time sequence of the resultant biometric vectors to enhance hazard prediction. Moreover, the apparatuses and methods may support the user in different settings including a home, business, or vehicle.

Abstract: A home computing system (cloud) integrates a protocols gateway, WiFi router, cloud server, and mass storage device to support one or more Internet of Things (IoT) devices, possibly with different connectively protocols, in a local environment such as a residential home. The home computing cloud often reduces the amount of data traffic sent to a public computing cloud by locally processing collected device data rather than by sending the device data to the public computer cloud for processing. The home computing cloud may download an appropriate data analytic model from the public computing cloud, locally train (for example, reinforcement learning) the model, and locally execute the trained model to obtain prediction information from collected IoT device data. The home computing cloud also allows direct access of the connected IoT devices by user applications via the internet, through a protocols gateway and an IoT message translator.

Abstract: A home computing cloud (HCC) supports one or more Internet of Things (IoT) devices, possibly with different connectively protocols, in a local environment. The HCC often reduces the amount of data traffic sent to a public computing cloud (PCC) by locally processing collected device data rather than by sending the device data to the PCC for processing. This approach reduces the amount of data traffic sent over the network, improves data privacy and helps to maintain a desired quality of service level. In order to do so, the HCC may download an appropriate data analytic model from the PCC, train the model, execute the trained model to obtain prediction information from collected IoT device data, and upload the trained model to the PCC. Alternatively, the HCC and PCC may execute sub-models of the analytic model and exchange the outputs of the sub-models with each other.

Abstract: An apparatus supports smart assistant services with a plurality of smart service providers. The apparatus includes an audio device that receives a speech signal having a user utterance, captures the user utterance when the user utterance includes a user wake word, and sends the captured utterance to a backend computing device. The backend computing device replaces the user wake word with specific wake words associated with different smart service providers. The processed utterances are then sent to selected smart service providers. The backend computing device subsequently constructs feedback to the user utterance based on voice responses from the different smart service providers. The backend computing device then passes a digital representation of the feedback to the audio device, and the audio device converts the digital representation to an audio reply to the user utterance.

Abstract: An apparatus supports smart assistant services with a plurality of smart service providers. The apparatus includes an audio device that receives a speech signal having a user utterance, captures the user utterance when the user utterance includes a user wake word, and sends the captured utterance to a backend computing device. The backend computing device replaces the user wake word with specific wake words associated with different smart service providers. The processed utterances are then sent to selected smart service providers. The backend computing device subsequently constructs feedback to the user utterance based on voice responses from the different smart service providers. The backend computing device then passes a digital representation of the feedback to the audio device, and the audio device converts the digital representation to an audio reply to the user utterance.

Abstract: A health condition of a person may be assessed from a thermal sensor signal. By increasing performance indices of a thermal camera (for example, resolution, frame rate, sensitivity), operation may be extended to identification verification, biometric data extraction and health condition analysis, and so forth. Prediction may be carried out by monitoring a time sequence of thermal images, and consequently early warning of the health condition may be provided. The apparatus may be used for, but not limited to, personalization of smart home devices through supervised and reinforcement learnings. The application of the apparatus may be, but not limited to, smart homes, smart buildings and smart vehicles, and so forth.

Abstract: Building blocks for a smart device such as a thermostat include a user interface (UI) unit and a terminal (TML) unit. A UI unit may support one or more input data from a user and/or sensors and/or one or more control terminals. The UI unit may process each input datum or a combination of the input data, generate a control signal to one or more control terminals based on the processing, and send the control signal to one or more control terminals over a communication channel. A terminal unit, which may consist of one or more control terminals, transforms the received control signal into one or more controls to one or more associated environmental generators. One or more UI units may control one or more controlled apparatuses in conjunction with a mobile app to allow a unified user experience.

Abstract: A smart electrical plug supports one or more electrical outlets and one or more universal serial bus (USB) outlets for charging electrical devices. Electrical power consumed through the one or more electrical outlets may be measured individually or in combination and reported via a wireless communication channel. The smart electrical plug may be implemented by a plurality of printed circuit board assemblies and distributed within a housing to reduce the effects of heat dissipation. The smart electrical plug may further reduce heat dissipation by utilizing one or more electrical circuit approaches.

Abstract: A health condition of a person may be assessed from a thermal sensor signal. By increasing performance indices of a thermal camera (for example, resolution, frame rate, sensitivity), operation may be extended to identification verification, biometric data extraction and health condition analysis, and so forth. Prediction may be carried out by monitoring a time sequence of thermal images, and consequently early warning of the health condition may be provided. The apparatus may be used for, but not limited to, personalization of smart home devices through supervised and reinforcement learnings. The application of the apparatus may be, but not limited to, smart homes, smart buildings and smart vehicles, and so forth.

Abstract: Building blocks for a smart device such as a thermostat include a user interface (UI) unit and a terminal (TML) unit. A UI unit may support one or more input data from a user and/or sensors and/or one or more control terminals. The UI unit may process each input datum or a combination of the input data, generate a control signal to one or more control terminals based on the processing, and send the control signal to one or more control terminals over a communication channel. A terminal unit, which may consist of one or more control terminals, transforms the received control signal into one or more controls to one or more associated environmental generators. One or more UI units may control one or more controlled apparatuses in conjunction with a mobile app to allow a unified user experience.

Abstract: Apparatuses and methods detect a health condition of a user that may be assessed from a thermal sensor signal and/or radar sensor signal. One or more resultant biometric vectors may be generated from biometric vectors based on the thermal and radar signals, where the resultant biometric vectors contain resultant information about one or more biometric features for a user. Hazard information about the user is obtained from the one or more resultant biometric vectors, where the hazard information is indicative of a health event for the user. Consequently, an appropriate action on behalf of the user may be performed to ameliorate the health condition. The one or more resultant biometric vectors may include additional biometric features and/or a time sequence of the resultant biometric vectors to enhance hazard prediction. Moreover, the apparatuses and methods may support the user in different settings including a home, business, or vehicle.

Abstract: A health condition of a person may be assessed from a thermal sensor signal. By increasing performance indices of a thermal camera (for example, resolution, frame rate, sensitivity), operation may be extended to identification verification, biometric data extraction and health condition analysis, and so forth. Prediction may be carried out by monitoring a time sequence of thermal images, and consequently early warning of the health condition may be provided. The apparatus may be used for, but not limited to, personalization of smart home devices through supervised and reinforcement learnings. The application of the apparatus may be, but not limited to, smart homes, smart buildings and smart vehicles, and so forth.

Abstract: An apparatus supports smart assistant services with a plurality of smart service providers. The apparatus includes an audio device that receives a speech signal having a user utterance, captures the user utterance when the user utterance includes a user wake word, and sends the captured utterance to a backend computing device. The backend computing device replaces the user wake word with specific wake words associated with different smart service providers. The processed utterances are then sent to selected smart service providers. The backend computing device subsequently constructs feedback to the user utterance based on voice responses from the different smart service providers. The backend computing device then passes a digital representation of the feedback to the audio device, and the audio device converts the digital representation to an audio reply to the user utterance.

Abstract: Building blocks for a smart device such as a thermostat include a user interface (UI) unit and a terminal (TML) unit. A UI unit may support one or more input data from a user and/or sensors and/or one or more control terminals. The UI unit may process each input datum or a combination of the input data, generate a control signal to one or more control terminals based on the processing, and send the control signal to one or more control terminals over a communication channel. A terminal unit, which may consist of one or more control terminals, transforms the received control signal into one or more controls to one or more associated environmental generators. One or more UI units may control one or more controlled apparatuses in conjunction with a mobile app to allow a unified user experience.