The present disclosure generally relates to cycle tracking. A notification for a respective recurrence of the recurring event is displayed. Representations of days are displayed with affordances for initiating processes for recording information corresponding to various days.

A system and method for unsupervised training of a text report identification machine learning model, including: labeling a first set of unlabeled text reports using a seed dictionary to identify concepts in the unlabeled text reports; inputting images associated with the first set of seed-labeled text reports into an auto-encoder to obtain an encoded first set of images; calculating a set of first correlation matrices as a dot product of the first encoded images with their associated text report features; determining a distance between the set of first correlation matrices and a filter bank value associated with the auto-encoder; identifying a first set of validated images as the images in the first set of images that have a distance less than a threshold value; and training the text report machine learning model using the labeled text reports associated with the set of first validated images.

A system and method for unsupervised training of a text report identification machine learning model, including: labeling a first set of unlabeled text reports using a seed dictionary to identify concepts in the unlabeled text reports; inputting images associated with the first set of seed-labeled text reports into an auto-encoder to obtain an encoded first set of images; calculating a set of first correlation matrices as a dot product of the first encoded images with their associated text report features; determining a distance between the set of first correlation matrices and a filter bank value associated with the auto-encoder; identifying a first set of validated images as the images in the first set of images that have a distance less than a threshold value; and training the text report machine learning model using the labeled text reports associated with the set of first validated images.

Described herein are heating, ventilation, air conditioning, and refrigeration (HVACR) systems and methods directed to indoor air quality. HVACR systems and methods are based on dynamic people modeling to simulate and/or to monitor airflow impact on pathogen spread in an indoor space. HVACR systems and methods model an indoor space with pathogen killing technology to deploy the pathogen killing technology. HVACR systems and methods are directed to control administration of a pathogen killing technology to an indoor space based on factors that impact airflow including from dynamic analytics, a known input, and/or detection.

In an approach to predictive contact tracing, a computer receives a query associated with contact tracing of a person with an infection. A computer retrieves timestamped location data associated with the person over a period of time. Based on the retrieved data, a computer creates a contact graph associated with the person, where the contact graph depicts one or more other people that were in contact with the person over the period of time. A computer retrieves medical data associated with the person and the one or more other people that were in contact with the person over the period of time. Based on the retrieved data, a computer builds a probabilistic model. A computer runs the probabilistic model to provide a prediction of a probability of infection of the one or more other people over the period of time as a result of being in contact with the person.

In an approach to predictive contact tracing, a computer receives a query associated with contact tracing of a person with an infection. A computer retrieves timestamped location data associated with the person over a period of time. Based on the retrieved data, a computer creates a contact graph associated with the person, where the contact graph depicts one or more other people that were in contact with the person over the period of time. A computer retrieves medical data associated with the person and the one or more other people that were in contact with the person over the period of time. Based on the retrieved data, a computer builds a probabilistic model. A computer runs the probabilistic model to provide a prediction of a probability of infection of the one or more other people over the period of time as a result of being in contact with the person.

A hygienic behavior monitoring system configured to use sensors to detect instances of hygienic behaviors. The hygienic behavior monitoring system includes a presentation unit that presents one or more media items and a processing unit that indexes behavioral instances. An analytic unit calculates instances of one or more hygienic events based at least in part on a fusion of sensor signals including instances of the actions of a sanitary device with instances of detected human presence.

A method, computer program product, and computing system for receiving audio-based content from a user who is reviewing an image on a display screen; receiving gaze information that defines a gaze location of the user; and temporally aligning the audio-based content and the gaze information to form location-based content.

A method, computer program product, and computing system for receiving audio-based content from a user who is reviewing an image on a display screen; receiving gaze information that defines a gaze location of the user; and temporally aligning the audio-based content and the gaze information to form location-based content.

A method and system comprising storing a set of user application programs each requiring an application specific subset of data to perform application processes and generate a respective genomic variant characterization for each of a plurality of patients with cancerous cells and receiving cancer treatment. The method including, obtaining clinical records data including cancer related information, generating genomic sequencing data for the patient's cancerous cells and normal cells, shaping at least a subset of the genomic sequencing data to generate system structured data. Storing the system structured data in a first database, selecting the application specific data from the first database, storing the application specific data in a second database for application program interfacing, receiving the respective genomic variant characterization from the user application program, and storing the genomic variant characterization from the user application program in a third database.