As described herein, a system, method, and computer program are provided for generating insights from home network router data. In use, network usage/connection data is collected from a home network router operating in a residential space. Additionally, the network usage/connection data is processed to generate insights for the network usage/connection data. Further, the insights are output for display to one or more users.

A load control system may comprise an electrical load control device and/or a computing device. The electrical load control device may control, for example, motorized window treatments (e.g., shades), lighting controls, and/or sensors (e.g., occupancy, radio window, daylight, etc.). For example, a load control device comprising a motorized window treatment may control the position of a covering material in the window treatment. The computing device may comprise a processor and/or a graphical user interface (GUI). The computing device may be a server and/or a user device, such as a wireless user device (e.g., a cellular phone, tablet, or laptop computer). The computing device may be configured to provide graphical representations that may be displayed on a GUI based on load control information.

A system and method are disclosed that utilizes an artificial intelligence based virtual proxy node. The virtual proxy node includes an intent resolution model and communicates between a smart audio device and at least one secondary device, wherein the at least one secondary device is configured to be controlled by a smart audio device or smart hub. The virtual proxy node tracks interactions between the smart audio device and the at least one secondary device to derive historical and context data from the tracking interactions. The virtual proxy node uses the historical and context data to predict which secondary device will be successful in responding to the user input command and broadcasts the input command to the virtual proxy node associated with one of the at least one secondary device. The virtual proxy node includes an intent resolution model trained by historical and context data.

Example techniques relate to playback queue subscriptions. An example implementation involves a computing system receiving, from a first computing device associated with a first user account, an instruction to enable subscription to a first playback queue associated with a first media playback system. In response to the instruction, the computing system enables second user accounts to subscribe to the first playback queue. The second user accounts are registered with respective second media playback systems in respective second household. The computing system receives, from a particular second media playback system, a request to subscribe to the first playback queue; and in response, sends one or more messages that update a control interface of the first control device to display a subscriber indication and (ii) sends one or more messages that populate a second playback queue of the particular second media playback system with audio tracks of the first playback queue.

The present disclosure relates to a system and method for controlling home appliances that operates home appliances using a user terminal and controls the operation of the home appliances arranged in a space in consideration of the amount of power consumed in the space. In the present disclosure, when a first home appliance is operated, the first home appliance is operated only when the total power consumption of the first home appliance and other home appliances that are already operating is less than or equal to a reference value.

A building monitoring system includes a first sensor configured to detect a first condition in the space, a second sensor configured to detect a second condition in the space, and a robotic sentinel. The robotic sentinel includes a memory for storing one or more rules each configured to identify an alert condition for the space based on the first and/or second conditions in the space, a communications module configured to communicate with a remote device over a network, and a controller operatively coupled to the sensors, the memory, and the communications module. The controller is configured to apply the one or more rules to the first and second detected conditions in the space to identify one or more alert conditions and determine what action is required by the robotic sentinel, and if action is required, command the robotic sentinel to travel to a location of the alert condition.

A method of controlling a home appliance which operates in an Internet of Things environment through a 5G communication network and which is performed using a neural network model generated by machine learning, including determining whether there is a user in the vicinity of the home appliance, capturing a motion of the user using a vision sensor based on a determination that there is a user in the vicinity of the home appliance, identifying an intention of the user based on the captured motion, and activating a speech module of the home appliance based on the intention of the user.

A voice inputting device inputs a voice operation of a user, and transmits voice data based on the voice operation to a first cloud server. The first cloud server receives the voice data from the voice inputting device, analyzes the received voice data, and determines an operational skill level of the user and the details of the voice operation. A second cloud server generates a control command for an air conditioner based on the operational skill level and the details of the voice operation determined by the first cloud server, and transmits the generated control command to the air conditioner.

Method and environment controller for inferring via a neural network one or more commands for controlling an appliance. A predictive model generated by a neural network training engine is stored by the environment controller. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy). The environment controller receives at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level). The environment controller executes a neural network inference engine, which uses the predictive model for inferring the one or more commands for controlling the appliance based on the at least one environmental characteristic value and the at least one set point. The environment controller transmits the one or more commands to the controlled appliance.

An electronic doorbell can include an outer housing configurable to couple to a building. The doorbell can also include a camera coupled to the outer housing and a microphone coupled to the outer housing. The doorbell can also include a button coupled to the outer housing. The electronic doorbell can be configurable to wirelessly communicate with an appliance located within the building.