Abstract: Transcribed text and physiological data of a remote video conference participant are transmitted to a local device separately from the video data, which depicts the remote party during a time interval. An image of the video data is captured at a time instant within the time interval. A value of a remote party feature is determined remotely using the video data. The remote party feature can be the remote party's heart rate at the time instant. The value of the feature is received onto the local device. Audio data captures sounds spoken by the remote party and is converted by the remote device into words of text. The audio data converted into a particular word was captured at the time instant. The particular word is received onto the local device. The particular word and the value of the feature are displayed in association with one another on the local device.

Abstract: A method for determining a user's stress level is performed by a smartphone app. Touch and motion feature values are generated, the feature values are weighted by regression parameters, and a stress score is generated based on the weighted touch and motion feature values. The touch feature values indicate how the user's finger moves over the smartphone and are generated from touch data points including X positions, Y positions and associated touch timestamp values. The motion feature values indicate movement of the smartphone and are generated from motion data points including X movements, Y movements, Z movements and associated motion timestamp values. The regression parameters are generated using touch and motion data identified by other users as being acquired while those other users were experiencing various perceived levels of stress. The app indicates to the user whether the stress score is higher or lower than a previously generated stress score.

Abstract: An autocomplete function for textual input uses situational parameters to predict the next words the user is intending to type. Situational and temporal parameters are based on textual input and sensor data of the user. A past time window is based on the situational and temporal parameters. Historical textual input and sensor data during the time window relating to the situational parameters are retrieved from a storage device and aggregated. A pre-existing model that relates the situational parameter to the time window is used to select a situational value based on the textual input and sensor data. Words relating to the situational parameter are listed that the user is likely to input next based on the selected situational value. The words are ranked by the probability that the user is intending to type each of the words. The highest ranked word is displayed to the user on a user interface.

Abstract: A method for administering an exposure treatment of a cognitive behavioral therapy (CBT) uses a mobile app and a server application. A user state of a patient undergoing a first step of the CBT based on the patient's condition during the first step is detected by sensors of the patient's smartphone. A situational state of the patient's surroundings during the first step is detected by the smartphone sensors. The mobile app determines whether the patient has made progress performing the first step. A user prompt is generated based on the user state and situational state. A next step of the CBT is configured based on the user state and situational state. The characteristics of the user prompt are generated using machine learning based on past task completions by the patient and other users so as to increase the likelihood that the patient will complete the next step of the CBT.