Abstract: In one aspect, a playback deice is configured to identify in an audio stream, via a second wake-word engine, a false wake word for a first wake-word engine that is configured to receive as input sound data based on sound detected by a microphone. The first and second wake-word engines are configured according to different sensitivity levels for false positives of a particular wake word. Based on identifying the false wake word, the playback device is configured to (i) deactivate the first wake-word engine and (ii) cause at least one network microphone device to deactivate a wake-word engine for a particular amount of time. While the first wake-word engine is deactivated, the playback device is configured to cause at least one speaker to output audio based on the audio stream. After a predetermined amount of time has elapsed, the playback device is configured to reactivate the first wake-word engine.

Abstract: Systems and methods for optimizing network microphone devices using noise classification are disclosed herein. In one example, individual microphones of a network microphone device (NMD) detect sound. The sound data is analyzed to detect a trigger event such as a wake word. Metadata associated with the sound data is captured in a lookback buffer of the NMD. After detecting the trigger event, the metadata is analyzed to classify noise in the sound data. Based on the classified noise, at least one performance parameter of the NMD is modified.

Abstract: Systems and methods for localizing portable devices are illustrated. One embodiment includes a method for locating a portable device in a network that includes several reference devices. The method measures characteristics of signals transmitted via signal paths between reference devices and a portable device, normalizes the measurements to estimate characteristics of the signal paths, and estimates the likelihood that the portable device is in a particular location. Systems and methods for training prediction models include a method that includes steps for receiving context data for a portable device in a system, wherein the context data includes localization data that describes a location of the portable device, identifying a predicted stationary device based on the context data using a prediction model, identifying a target stationary device from the several stationary devices, training the prediction model based on based on the predicted stationary device and the received input.

Abstract: In one aspect, a playback deice is configured to identify in an audio stream, via a second wake-word engine, a false wake word for a first wake-word engine that is configured to receive as input sound data based on sound detected by a microphone. The first and second wake-word engines are configured according to different sensitivity levels for false positives of a particular wake word. Based on identifying the false wake word, the playback device is configured to (i) deactivate the first wake-word engine and (ii) cause at least one network microphone device to deactivate a wake-word engine for a particular amount of time. While the first wake-word engine is deactivated, the playback device is configured to cause at least one speaker to output audio based on the audio stream. After a predetermined amount of time has elapsed, the playback device is configured to reactivate the first wake-word engine.

Abstract: Systems and methods for generating training data are described herein. Pieces of metadata captured by a plurality of networked sensor systems can be captured, where each piece of metadata is associated with a specific set of sensor data captured by one of the plurality of networked sensor systems and includes a set of characteristics for the specific set of captured sensor data. A probabilistic model can be generated based on the received metadata and simulations can be performed based upon a training corpus by generating multiple scenarios, and, for each scenario, a scenario specific version of a particular annotated sample is generated by performing a simulation using the particular annotated sample. The scenario specific versions of annotated samples from the training corpus can be stored as a training data set on the at least one network device.

Abstract: Systems and methods for optimizing network microphone devices using noise classification are disclosed herein. In one example, individual microphones of a network microphone device (NMD) detect sound. The sound data is analyzed to detect a trigger event such as a wake word. Metadata associated with the sound data is captured in a lookback buffer of the NMD. After detecting the trigger event, the metadata is analyzed to classify noise in the sound data. Based on the classified noise, at least one performance parameter of the NMD is modified.

Abstract: In one aspect, a network microphone device includes a plurality of microphones and is configured to detect sound via the one or more microphones. The network microphone device may capture sound data based on the detected sound in a first buffer, and capture metadata associated with the detected sound in a second buffer. The network microphone device may classify one or more noises in the detected sound and cause the network microphone device to perform an action based on the classification of the respective one or more noises.

Abstract: The present invention is an HVAC fan array wiring system that provides a quick connect box that is provided within the air stream of an HVAC air handler. Because the wiring for each fan within the fan array is to be connected to the quick connect box, the wiring materials and placement of the wiring can be predetermined at the time the fan array is designed. Accordingly, the novel wiring system and quick connect box reduces redundancies in manufacturing and installation of the fan array. Furthermore, the quick connect box and wiring system allows for easy installation of the HVAC fan array at a job site and improves a customer's experience with the control panel. This is primarily because, with the use of the claimed invention, an electrician need only link a main control and power line to the quick connect box and therefore, can install the control panel in a location that provides ease of access to the customer.

Abstract: An example portable washing station may include a reservoir, a lid removably attached to the reservoir, an opening for receiving an extendable nozzle, a submersible pump positioned within the reservoir, a hose connecting the extendable nozzle and the submersible pump, and a battery electrically coupled to the submersible pump via a detachable power cord.

Abstract: The present invention is an HVAC fan array wiring system that provides a quick connect box that is provided within the air stream of an HVAC air handler. Because the wiring for each fan within the fan array is to be connected to the quick connect box, the wiring materials and placement of the wiring can be predetermined at the time the fan array is designed. Accordingly, the novel wiring system and quick connect box reduces redundancies in manufacturing and installation of the fan array. Furthermore, the quick connect box and wiring system allows for easy installation of the HVAC fan array at a job site and improves a customer's experience with the control panel. This is primarily because, with the use of the claimed invention, an electrician need only link a main control and power line to the quick connect box and therefore, can install the control panel in a location that provides ease of access to the customer.

Abstract: In one aspect, a network microphone device includes a plurality of microphones and is configured to detect sound via the one or more microphones. The network microphone device may capture sound data based on the detected sound in a first buffer, and capture metadata associated with the detected sound in a second buffer. The network microphone device may classify one or more noises in the detected sound and cause the network microphone device to perform an action based on the classification of the respective one or more noises.

Abstract: Systems and methods for training prediction models are illustrated. One embodiment includes a method for training a prediction model in a network. The method includes steps for receiving context data for a portable device in a system, wherein the context data includes localization data that describes a location of the portable device, identifying a predicted stationary device from several stationary devices that is predicted based on the context data using a prediction model, receiving input identifying a target stationary device from the several stationary devices, generating training data based on the predicted stationary device and the received input, updating the prediction model based on the generated training data.

Abstract: Systems and methods for localizing portable devices are illustrated. One embodiment includes a method for locating a portable device in a network that includes several reference devices. The method measures characteristics of signals transmitted via signal paths between reference devices and a portable device, normalizes the measurements to estimate characteristics of the signal paths, and estimates the likelihood that the portable device is in a particular location. Systems and methods for training prediction models include a method that includes steps for receiving context data for a portable device in a system, wherein the context data includes localization data that describes a location of the portable device, identifying a predicted stationary device based on the context data using a prediction model, identifying a target stationary device from the several stationary devices, training the prediction model based on based on the predicted stationary device and the received input.

Abstract: A pair of clothing bottoms with attachable weights is provided to intensify a workout. The pair of clothing bottoms with attachable weights includes a pair of clothing bottoms, at least one left weight assembly, and at least one right weight assembly. The pair of clothing bottoms may be any type of athletic garments such as, but not limited to, a pair of shorts, a pair of leggings, or a pair of sweatpants. The at least one left weight assembly allows weights to be selectively attached to the pair of clothing bottoms to increase the load to the left hip side of a user during a workout. Similarly, the at least one right weight assembly allows weights to be attached to the pair of clothing bottoms to increase the load to the right hip side of the user during a workout.

Abstract: Systems and methods for optimizing network microphone devices using noise classification are disclosed herein. In one example, individual microphones of a network microphone device (NMD) detect sound. The sound data is analyzed to detect a trigger event such as a wake word. Metadata associated with the sound data is captured in a lookback buffer of the NMD. After detecting the trigger event, the metadata is analyzed to classify noise in the sound data. Based on the classified noise, at least one performance parameter of the NMD is modified.

Abstract: Systems and methods for localizing portable devices are illustrated. One embodiment includes method for locating a portable device in a network that includes several reference devices. The method measures characteristics of signals transmitted via signal paths between reference devices and a portable device, normalizes the measurements to estimate characteristics of the signal paths, and estimates the likelihood that the portable device is in a particular location. Systems and methods for training prediction models include a method that includes steps for receiving context data for a portable device in a system, wherein the context data includes localization data that describes a location of the portable device, identifying a predicted stationary device based on the context data using a prediction model, identifying a target stationary device from the several stationary devices, training the prediction model based on based on the predicted stationary device and the received input.

Abstract: Systems and methods for training prediction models are illustrated. One embodiment includes a method for training a prediction model in a network. The method includes steps for receiving context data for a portable device in a system, wherein the context data includes localization data that describes a location of the portable device, identifying a predicted stationary device from several stationary devices that is predicted based on the context data using a prediction model, receiving input identifying a target stationary device from the several stationary devices, generating training data based on the predicted stationary device and the received input, updating the prediction model based on the generated training data.

Abstract: Systems and methods for optimizing network microphone devices using noise classification are disclosed herein. In one example, individual microphones of a network microphone device (NMD) detect sound. The sound data is analyzed to detect a trigger event such as a wake word. Metadata associated with the sound data is captured in a lookback buffer of the NMD. After detecting the trigger event, the metadata is analyzed to classify noise in the sound data. Based on the classified noise, at least one performance parameter of the NMD is modified.

Abstract: An airfoil assembly for a turbine engine that includes a platform, a dovetail, an airfoil. The airfoil assembly can further include a cooling air circuit that fluidly connects to a platform cooling passage. The cooling air circuit and platform cooling passage can be contained within or housed at least in part by the platform, the dovetail, or the airfoil. The cooling air circuit can also include a deflector.

Abstract: Systems and methods for optimizing voice detection via a network microphone device (NMD) based on a selected voice-assistant service (VAS) are disclosed herein. In one example, the NMD detects sound via individual microphones and selects a first VAS to communicate with the NMD. The NMD produces a first sound-data stream based on the detected sound using a spatial processor in a first configuration. Once the NMD determines that a second VAS is to be selected over the first VAS, the spatial processor assumes a second configuration for producing a second sound-data stream based on the detected sound. The second sound-data stream is then transmitted to one or more remote computing devices associated with the second VAS.