Abstract: Methods and systems are provided for detecting and processing gestures, expressions (e.g., facial), tone and/or gestures of the user for the purpose of improving the quality and speed of interactions with computer-based systems. Such information may be detected by one or more sensors such as, for example, electromyography (EMG) sensors used to monitor and record electrical activity produced by muscles that are activated. Other sensor types may be used, such as optical, inertial measurement unit (IMU), or other types of bio-sensors. The system may use one or more sensors to detect speech alone or in combination with gestures, expressions (e.g., facial), tone and/or gestures of the user to provide input or control of the system.

Abstract: The present disclosure relates to methods and systems for adjusting a silent speech machine learning model for use with a wearable silent speech device. In some embodiments, a method may include recording speech signals from a user, using a first sensor and a second sensor of a wearable silent speech device. The method may include providing for a silent speech machine learning model for use with the wearable silent speech device, determining whether the silent speech machine learning model is to be adjusted, and in response to determining the silent speech machine learning model is to be adjusted, adjusting the silent speech machine learning model based on at least the speech signals recorded using the first sensor and the second sensor.

Abstract: Methods and systems are provided for detecting and processing gestures, expressions (e.g., facial), tone and/or gestures of the user for the purpose of improving the quality and speed of interactions with computer-based systems. Such information may be detected by one or more sensors such as, for example, electromyography (EMG) sensors used to monitor and record electrical activity produced by muscles that are activated. Other sensor types may be used, such as optical, inertial measurement unit (IMU), or other types of bio-sensors. The system may use one or more sensors to detect speech alone or in combination with gestures, expressions (e.g., facial), tone and/or gestures of the user to provide input or control of the system.

Abstract: Techniques, including a system, device, and method, for controlling a silent speech wearable device are provided. The techniques may record signals using a wearable device, worn by a user. The techniques may analyze the signals recorded by the wearable device to determine an action, word, and/or phrase performed or said by the user. The techniques may comprise using machine learning models to analyze the signals. The techniques may comprise template matching and/or pattern recognition to analyze the signals. The techniques may involve changing a state and/or a mode of the device in response to the signals.

Abstract: Methods and systems are provided for detecting and processing gestures, expressions (e.g., facial), tone and/or gestures of the user for the purpose of improving the quality and speed of interactions with computer-based systems. Such information may be detected by one or more sensors such as, for example, electromyography (EMG) sensors used to monitor and record electrical activity produced by muscles that are activated. Other sensor types may be used, such as optical, inertial measurement unit (IMU), or other types of bio-sensors. The system may use one or more sensors to detect speech alone or in combination with gestures, expressions (e.g., facial), tone and/or gestures of the user to provide input or control of the system.

Abstract: Methods and systems are provided for detecting and processing gestures, expressions (e.g., facial), tone and/or gestures of the user for the purpose of improving the quality and speed of interactions with computer-based systems. Such information may be detected by one or more sensors such as, for example, electromyography (EMG) sensors used to monitor and record electrical activity produced by muscles that are activated. Other sensor types may be used, such as optical, inertial measurement unit (IMU), or other types of bio-sensors. The system may use one or more sensors to detect speech alone or in combination with gestures, expressions (e.g., facial), tone and/or gestures of the user to provide input or control of the system.

Abstract: Techniques, including a system, device and method, for decoding and using silent speech signals are provided. The techniques may record silent speech signals using a wearable device, worn by a user. The techniques may analyze the silent speech signals to determine one or more outputs including words or phrases from the silent speech signals. The techniques may comprise using machine learning models to determine the one or more outputs. The techniques may involve performing one or more actions in response to the one or more outputs. The one or more actions may include natural language processing, executing a command on a device and providing information, among other actions. The techniques may involve generating an audio signal for communication from silent speech signals.

Abstract: Systems and methods are provided for decoding the silent speech of a user. Speech of the user (e.g., silent, vocalized, etc.) may be detected and captured by a speech input device configured to measure signals indicative of the speech muscle activation patterns of the user. A trained machine learning model configured to decode the speech of the user based at least in part on the signal indicative of the speech muscle activation patterns of the user. To improve accuracy of the model, the trained machine learning model may be trained using training data obtained in at least a subset of sampling contexts of a plurality of sampling contexts. At least one processor may be configured to output the decoded speech of the user.

Abstract: Methods and systems are provided for facilitating silent calling over a communication network. Such silent calling may be facilitated using a speech system associated with a first user configured to measure signals associated with the first user's speech muscle activation patterns and a communication interface configured to communicate with a communication device associated with a second user on the communication network. The first user's silent speech may be synthesized based at least in part on the signals associated with the first user's speech muscle activation patterns.

Abstract: Methods and systems are provided for detecting and synthesizing a user's speech, including, for example, vocalized, whispered, and silent speech for the purpose of providing an output substantially in parallel with the user speaking. Such information may be detected by one or more sensors such as, for example, electromyography (EMG) sensors used to monitor and record electrical activity produced by muscles that are activated, for example, speech muscles activated when the user is speaking. Other sensor types may be used, such as audio, optical, inertial measurement unit (IMU), or other types of sensors. The user's speech may be synthesized using one or more machine learning models or a machine learning model in conjunction with other suitable processing devices and methods.

Abstract: The techniques described herein relate to computerized methods and systems for integrating with a knowledge system. In some embodiments, a user interaction system may include a speech input device wearable on a user and configured to receive an electronic signal indicative of a user's speech muscle activation patterns when the user is speaking. In some embodiments, the electronic signal may include EMG data received from an EMG sensor on the speech input device. The system may include at least one processor configured to use a speech model and the electronic signal as input to the speech model to generate a text prompt. The at least one processor may use a knowledge system to take an action or generate a response based on the text prompt. In some embodiments, the system may provide context to the knowledge system.

Abstract: An exemplary system that provides automated business intelligence from business data to improve operations of the business is disclosed. The system extracts signals from any unstructured data source. The system identifies anomalies in customer data and global trends for retail companies that present opportunities and crises to avoid and suggests optimal courses of action and estimated financial impact. The system also alerts individuals with opportunities and predicts customers' needs. The system extracts signals from any data source, structured or not, to alert the user of opportunities and anticipate customers' needs. The system determines the trends, what products are hits, the opportunities to pursue, and when to reach out to customers. This is done by collecting data from a multi-source data collection system.