| CPC G16H 50/20 (2018.01) [G06N 5/04 (2013.01); G10L 25/66 (2013.01); G16H 10/60 (2018.01); H03M 7/3059 (2013.01)] | 38 Claims |
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1. A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising:
obtaining, with a computer system, a trained machine learning model configured to infer whether users have a respiratory illness based on both audio and an image obtained from mobile phones of the users, wherein the trained machine learning model comprises a double parallel feedforward neural network that takes a vector of mel-frequency cepstrum coefficients as an input, and wherein the trained machine learning model is trained by:
obtaining a training set comprising a plurality of training records, wherein:
each training record in the training set includes a plurality of parameters and corresponding values for a respective person;
each training record in the training set includes audio of the respective person's voice and an image of at least part of the respective person; and
each training record in the training set includes an indicator indicating whether or not the respective person has been diagnosed with a respiratory illness; and
training the machine learning model on the training set to infer whether users have the respiratory illness based on both the audio and images;
after obtaining the trained machine learning model, receiving, with the computer system, a first user record of a first user, the first user record comprising an audio file or stream of a voice of the first user and an image of at least part of the first user;
inferring, with the computer system, that the first user has the respiratory illness based on the audio file or stream of a voice of the first user and an image of at least part of the first user; and
storing, with the computer system, an indication that the first user has the respiratory illness in memory.
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