An in-vehicle apparatus is connectable to a device that includes a voice assistant function. The in-vehicle apparatus includes: a voice detector that performs voice recognition of an audio signal input from a microphone and that controls functions of the in-vehicle apparatus based on a result of the voice recognition; and an interface that communicates with the device. When being informed of a detection of a predetermined word in the audio signal as the result of the voice recognition of the audio signal performed by the voice detector, the interface sends to the device, not via the voice detector, the audio signal input from the microphone. The predetermined word is for activating the voice assistant function of the device.

An artificial intelligence device includes a microphone configured to receive a speech command, a speaker, a communication unit configured to perform communication with an external artificial intelligence device, and a processor configured to receive a wake-up command through the microphone, acquire a first speech quality level of the received wake-up command, receive a second speech quality level of the wake-up command input to the external artificial intelligence device from the external artificial intelligence device through the communication unit, output a notification indicating that the artificial intelligence device is selected as an object to be controlled through the speaker, when the first speech quality level is larger than the second speech quality level, receive an operation command through the microphone, acquire an intention of the received operation command and transmit the operation command to an external artificial intelligence device which will perform operation corresponding to the operation command according to the acquired intention through the communication unit.

An artificial intelligence device includes a microphone configured to receive a speech command, a speaker, a communication unit configured to perform communication with an external artificial intelligence device, and a processor configured to receive a wake-up command through the microphone, acquire a first speech quality level of the received wake-up command, receive a second speech quality level of the wake-up command input to the external artificial intelligence device from the external artificial intelligence device through the communication unit, output a notification indicating that the artificial intelligence device is selected as an object to be controlled through the speaker, when the first speech quality level is larger than the second speech quality level, receive an operation command through the microphone, acquire an intention of the received operation command and transmit the operation command to an external artificial intelligence device which will perform operation corresponding to the operation command according to the acquired intention through the communication unit.

Embodiments described herein provide a dynamic topic tracking mechanism that tracks how the conversation topics change from one utterance to another and use the tracking information to rank candidate responses. A pre-trained language model may be used for response selection in the multi-party conversations, which consists of two steps: (1) a topic-based pre-training to embed topic information into the language model with self-supervised learning, and (2) a multi-task learning on the pretrained model by jointly training response selection and dynamic topic prediction and disentanglement tasks.

Embodiments described herein provide a dynamic topic tracking mechanism that tracks how the conversation topics change from one utterance to another and use the tracking information to rank candidate responses. A pre-trained language model may be used for response selection in the multi-party conversations, which consists of two steps: (1) a topic-based pre-training to embed topic information into the language model with self-supervised learning, and (2) a multi-task learning on the pretrained model by jointly training response selection and dynamic topic prediction and disentanglement tasks.

A method, computer program product, and computing system for proactive encounter scanning is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is proactively processed to determine if the encounter information is indicative of one or more medical conditions and to generate one or more result set. The one or more result sets are provided to the user.

Provided is a robot including a microphone configured to acquire a sound signal corresponding to a sound generated near the robot, a camera, an output interface including at least one of a display configured to output a wake-up screen or a speaker configured to output a wake-up sound when the robot wakes up, and a processor configured to recognize whether the acquired sound includes a voice of a person, activate the camera when the sound includes a voice of a person, recognize whether a person is present in an image acquired by the activated camera, set a wake-up word recognition sensitivity based on a recognition result as to whether a person is present, and recognize whether a wake-up word is included voice data of a user acquired through the microphone based on the set wake-up word recognition sensitivity.

Provided is a robot including a microphone configured to acquire a sound signal corresponding to a sound generated near the robot, a camera, an output interface including at least one of a display configured to output a wake-up screen or a speaker configured to output a wake-up sound when the robot wakes up, and a processor configured to recognize whether the acquired sound includes a voice of a person, activate the camera when the sound includes a voice of a person, recognize whether a person is present in an image acquired by the activated camera, set a wake-up word recognition sensitivity based on a recognition result as to whether a person is present, and recognize whether a wake-up word is included voice data of a user acquired through the microphone based on the set wake-up word recognition sensitivity.

Provided is an in-ear device and associated computational support system that leverages machine learning to interpret sensor data descriptive of one or more in-ear phenomena during subvocalization by the user. An electronic device can receive sensor data generated by at least one sensor at least partially positioned within an ear of a user, wherein the sensor data was generated by the at least one sensor concurrently with the user subvocalizing a subvocalized utterance. The electronic device can then process the sensor data with a machine-learned subvocalization interpretation model to generate an interpretation of the subvocalized utterance as an output of the machine-learned subvocalization interpretation model.

Provided is an in-ear device and associated computational support system that leverages machine learning to interpret sensor data descriptive of one or more in-ear phenomena during subvocalization by the user. An electronic device can receive sensor data generated by at least one sensor at least partially positioned within an ear of a user, wherein the sensor data was generated by the at least one sensor concurrently with the user subvocalizing a subvocalized utterance. The electronic device can then process the sensor data with a machine-learned subvocalization interpretation model to generate an interpretation of the subvocalized utterance as an output of the machine-learned subvocalization interpretation model.