Abstract: In one embodiment, the disclosure provides a computer-implemented method comprising: receiving a first labeled training data set comprising a first plurality of images each associated with a set of labels; programmatically training a machine learning neural Teacher model on the first labeled training data set; programmatically applying a machine learning model trained for NLP to an unlabeled data set comprising digital electronic representations of natural language text summaries of a second plurality of images, thereby generating a second labeled training data set comprising the second plurality of images; using the machine learning neural Teacher model, programmatically generating soft pseudo labels; programmatically generating derived labels using the soft pseudo labels; training one or more programmed machine learning neural Student models using the derived labels; receiving a target image; and applying an ensemble of one or more of the Student models to output one or more classifications of the target im

Abstract: A computer-implemented method for detecting one or more emotions of one or more pets is disclosed. The method receiving, by one or more processors, image data from at least one user device, wherein the image data includes one or more frames, detecting, by the one or more processors, at least one pet outline that includes at least one pet in the one or more frames, detecting, by the one or more processors, one or more emotions of the at least one pet based on the at least one pet outline; and displaying, by the one or more processors, the one or more emotions on at least one user interface of a user device.

Abstract: A computer-implemented method for classifying electrocardiogram signals of a canine is disclosed. The method comprises receiving electrocardiogram data of a canine, the electrocardiogram data including at least one electrocardiogram signal, segmenting the electrocardiogram data into one or more data subsets, pre-processing the one or more data subsets, the pre-processing including excluding the one or more data subsets that include a poor electrocardiogram signal, augmenting the one or more data subsets, determining, using a trained machine-learning model, one or more signal classifications for the one or more data subsets, aggregating the one or more signal classifications to determine a result classification, and outputting the result classification to an electronic storage device and/or a display.

Abstract: In one embodiment, the disclosure provides a computer-implemented method comprising: receiving a first labeled training data set comprising a first plurality of images each associated with a set of labels; programmatically training a machine learning neural Teacher model on the first labeled training data set; programmatically applying a machine learning model trained for NLP to an unlabeled data set comprising digital electronic representations of natural language text summaries of a second plurality of images, thereby generating a second labeled training data set comprising the second plurality of images; using the machine learning neural Teacher model, programmatically generating soft pseudo labels; programmatically generating derived labels using the soft pseudo labels; training one or more programmed machine learning neural Student models using the derived labels; receiving a target image; and applying an ensemble of one or more of the Student models to output one or more classifications of the target im

Abstract: Systems, methods, and apparatus are disclosed for analyzing an input image that includes a view of fecal matter. One example method includes: receiving an input image from a client device; determining that the input image comprises a view of fecal matter excreted by an animal; processing at least a portion of the input image comprising the view of the fecal matter using one or more machine learning models to generate a classification of the fecal matter or a health assessment of the animal; generating a recommendation for the animal based on the classification of the fecal matter or the health assessment of the animal; and displaying information related to the recommendation for the animal to a user. Some embodiments involve outputting confidence scores associated with one or more of the other outputs. Some embodiments implement Client-Server architecture and follow a Software as a Service (SaaS) model.