Abstract: Embodiments are directed to a non-transitory computer-readable storage medium comprising instructions that when executed cause processor circuitry to generate a longitudinal dataset of a set of features from tracked physical measurements of a user received from sensor circuitry, apply at least one machine learning model to the longitudinal dataset of the set of features to identify a pattern within the set of features indicative of a probability of the user being in a state of menopause, predict a current state of menopause for the user based on the identified pattern, and communicate a data message indicative of the current state of menopause for the user. In some embodiments, the set of features are compressed to pseudo-features and input to a first ML model using a second ML model.

Abstract: Embodiments in accordance with the present disclosure are directed to systems, devices, and methods involving hot flash (HF) multi-sensor circuits. An example system includes a plurality of sensor circuits and processor circuitry. The sensor circuits obtain a plurality of sensor signals associated with the user. The processor circuitry extracts features from the plurality of sensor signals obtained by the plurality of sensor circuits, aligns the extracted features to a common time point, identifies a HF event for the user using a predictive data model indicative of probability of the HF event occurring for the user at a date and time and based on the aligned extracted features, and communicates a message indicative of the HF event to the user.

Abstract: Methods, computing devices, and computer-program products are provided for implementing a virtual personal assistant. In various implementations, a virtual personal assistant can be configured to receive sensory input, including at least two different types of information. The virtual personal assistant can further be configured to determine semantic information from the sensory input, and to identify a context-specific framework. The virtual personal assistant can further be configured to determine a current intent. Determining the current intent can include using the semantic information and the context-specific framework. The virtual personal assistant can further be configured to determine a current input state. Determining the current input state can include using the semantic information and one or more behavioral models. The behavioral models can include one or more interpretations of previously-provided semantic information.

Abstract: Methods, computing devices, and computer-program products are provided for implementing a virtual personal assistant. In various implementations, a virtual personal assistant can be configured to receive sensory input, including at least two different types of information. The virtual personal assistant can further be configured to determine semantic information from the sensory input, and to identify a context-specific framework. The virtual personal assistant can further be configured to determine a current intent. Determining the current intent can include using the semantic information and the context-specific framework. The virtual personal assistant can further be configured to determine a current input state. Determining the current input state can include using the semantic information and one or more behavioral models. The behavioral models can include one or more interpretations of previously-provided semantic information.

Abstract: Disclosed are machine learning-based technologies that analyze an audio input and provide speaker state predictions in response to the audio input. The speaker state predictions can be selected and customized for each of a variety of different applications.

Abstract: Methods, computing devices, and computer-program products are provided for implementing a virtual personal assistant. In various implementations, a virtual personal assistant can be configured to receive sensory input, including at least two different types of information. The virtual personal assistant can further be configured to determine semantic information from the sensory input, and to identify a context-specific framework. The virtual personal assistant can further be configured to determine a current intent. Determining the current intent can include using the semantic information and the context-specific framework. The virtual personal assistant can further be configured to determine a current input state. Determining the current input state can include using the semantic information and one or more behavioral models. The behavioral models can include one or more interpretations of previously-provided semantic information.

Abstract: Disclosed are machine learning-based technologies that analyze an audio input and provide speaker state predictions in response to the audio input. The speaker state predictions can be selected and customized for each of a variety of different applications.