Abstract: Predicting a first mental state of a first user. Predicting, based on the first mental state of the first user and one or more machine learning models, one or more therapeutic matches between the first user and one or more second users of a plurality of second users. Facilitating presentation, via a graphical user interface (GUI), of the one or more therapeutic matches. Receiving, in response to the first user interacting with the GUI, a user selection of a particular second user of the one or more second users of the plurality of second users. Automatically connecting, in response to receiving the user selection of the particular second user of the one or more second users of the plurality of second users, the first user with each of the one or more second users of the plurality of second users.

Abstract: Determining input data for at least one machine learning model based on electronic data of a user. Predicting, based on the input data and the at least one machine learning model, a mental state of the user, the mental state comprising mood values, uncertainty values, and magnitude values, each mood value being associated with a corresponding uncertainty value of the uncertainty values and a corresponding magnitude value of the magnitude values, the magnitude value indicating a relative strength or weakness of the associated mood value. Selecting and arranging, based on the predicted mental state, a subset of graphical elements, each graphical element being associated with a corresponding mood value of the set of mood values, and each graphical element of the subset of graphical elements being associated with the predicted mental state of the user.

Abstract: Determining input data for at least one machine learning model based on electronic data of a user. Predicting, based on the input data and the at least one machine learning model, a mental state of the user, the mental state comprising mood values, uncertainty values, and magnitude values, each mood value being associated with a corresponding uncertainty value of the uncertainty values and a corresponding magnitude value of the magnitude values, the magnitude value indicating a relative strength or weakness of the associated mood value. Selecting and arranging, based on the predicted mental state, a subset of graphical elements, each graphical element being associated with a corresponding mood value of the set of mood values, and each graphical element of the subset of graphical elements being associated with the predicted mental state of the user.

Abstract: Determining input data for at least one machine learning model based on electronic data of a user. Predicting, based on the input data and the at least one machine learning model, a mental state of the user, the mental state comprising mood values, uncertainty values, and magnitude values, each mood value being associated with a corresponding uncertainty value of the uncertainty values and a corresponding magnitude value of the magnitude values, the magnitude value indicating a relative strength or weakness of the associated mood value. Selecting and arranging, based on the predicted mental state, a subset of graphical elements, each graphical element being associated with a corresponding mood value of the set of mood values, and each graphical element of the subset of graphical elements being associated with the predicted mental state of the user.

Abstract: Predicting a first mental state of a first user. Predicting, based on the first mental state of the first user and one or more machine learning models, one or more therapeutic matches between the first user and one or more second users of a plurality of second users. Facilitating presentation, via a graphical user interface (GUI), of the one or more therapeutic matches. Receiving, in response to the first user interacting with the GUI, a user selection of a particular second user of the one or more second users of the plurality of second users. Automatically connecting, in response to receiving the user selection of the particular second user of the one or more second users of the plurality of second users, the first user with each of the one or more second users of the plurality of second users.

Abstract: Use of spoken input for user devices, e.g. smartphones, can be challenging due to presence of other sound sources. Blind source separation (BSS) techniques aim to separate a sound generated by a particular source of interest from a mixture of different sounds. Various BSS techniques disclosed herein are based on recognition that providing additional information that is considered within iterations of a nonnegative tensor factorization (NTF) model improves accuracy and efficiency of source separation. Examples of such information include direction estimates or neural network models trained to recognize a particular sound of interest. Furthermore, identifying and processing incremental changes to an NTF model, rather than re-processing the entire model each time data changes, provides an efficient and fast manner for performing source separation on large sets of quickly changing data. Carrying out at least parts of BSS techniques in a cloud allows flexible utilization of local and remote sources.

Abstract: The present disclosure relates generally to improving acoustic source tracking and selection and, more particularly, to techniques for acoustic source tracking and selection using motion or position information. Embodiments of the present disclosure include systems designed to select and track acoustic sources. In one embodiment, the system may be realized as an integrated circuit including a microphone array, motion sensing circuitry, position sensing circuitry, analog-to-digital converter (ADC) circuitry configured to convert analog audio signals from the microphone array into digital audio signals for further processing, and a digital signal processor (DSP) or other circuitry for processing the digital audio signals based on motion data and other sensor data. Sensor data may be correlated to the analog or digital audio signals to improve source separation or other audio processing.