Abstract: Techniques are described for component design based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing use of one or more components of the system by one or more individuals. The sensor data from various instances of the component may be aggregated and analyzed to determine update(s) to the design of the component. For example, the aggregate sensor data may be analyzed to identify portions of the component frequently associated with movements by users (e.g., fidgeting, adjustments). The identified portion(s) can be presented graphically in a design view used to specify design modification(s) for the component. In some implementations, the aggregate sensor data is provided as input to a model that is trained, using machine learning, to output design modification(s) for the component based on the input aggregate sensor data.

Abstract: Techniques are described for component design based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing use of one or more components of the system by one or more individuals. The sensor data from various instances of the component may be aggregated and analyzed to determine update(s) to the design of the component. For example, the aggregate sensor data may be analyzed to identify portions of the component frequently associated with movements by users (e.g., fidgeting, adjustments). The identified portion(s) can be presented graphically in a design view used to specify design modification(s) for the component. In some implementations, the aggregate sensor data is provided as input to a model that is trained, using machine learning, to output design modification(s) for the component based on the input aggregate sensor data.

Abstract: Techniques are described for component design based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing use of one or more components of the system by one or more individuals. The sensor data from various instances of the component may be aggregated and analyzed to determine update(s) to the design of the component. For example, the aggregate sensor data may be analyzed to identify portions of the component frequently associated with movements by users (e.g., fidgeting, adjustments). The identified portion(s) can be presented graphically in a design view used to specify design modification(s) for the component. In some implementations, the aggregate sensor data is provided as input to a model that is trained, using machine learning, to output design modification(s) for the component based on the input aggregate sensor data.

Abstract: Techniques are described for component configuration based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing the use of component(s) of the system by individual(s). The sensor data is analyzed (e.g., in real time) to determine an updated configuration for component(s) (e.g., an adjustment to the seat back, lumbar support of a car seat, etc.). The updated configuration may be communicated to the individual as a recommended configuration. In some implementations, the updated configuration may be communicated directly to the component which, on receiving and processing the configuration update, sends signals to various actuators to move the subcomponents of the component into the updated configuration. In some implementations, the sensor data is used to train, through machine learning, a model that provides configuration update(s) for component(s) based on the input sensor data.

Abstract: Techniques are described for component configuration based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing the use of component(s) of the system by individual(s). The sensor data is analyzed (e.g., in real time) to determine an updated configuration for component(s) (e.g., an adjustment to the seat back, lumbar support of a car seat, etc.). The updated configuration may be communicated to the individual as a recommended configuration. In some implementations, the updated configuration may be communicated directly to the component which, on receiving and processing the configuration update, sends signals to various actuators to move the subcomponents of the component into the updated configuration. In some implementations, the sensor data is used to train, through machine learning, a model that provides configuration update(s) for component(s) based on the input sensor data.

Abstract: Techniques are described for component design based on sensor data. Sensor data is collected by sensors in, or proximal to, a system under diagnosis (e.g., a vehicle), the sensor data describing use of one or more components of the system by one or more individuals. The sensor data from various instances of the component may be aggregated and analyzed to determine update(s) to the design of the component. For example, the aggregate sensor data may be analyzed to identify portions of the component frequently associated with movements by users (e.g., fidgeting, adjustments). The identified portion(s) can be presented graphically in a design view used to specify design modification(s) for the component. In some implementations, the aggregate sensor data is provided as input to a model that is trained, using machine learning, to output design modification(s) for the component based on the input aggregate sensor data.