Application US20200151519 AI Biotech/Diagnostics: Cardio

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Curieai, Inc.

Application US20200151519

Published 2020-05-14

Intelligent Health Monitoring

Embodiments are disclosed for health assessment and diagnosis implemented in an artificial intelligence (AI) system. The AI system takes as input information from a multitude of sensors measuring different biomarkers in a continuous or intermittent fashion. The proposed techniques disclosed herein address the unique challenges encountered in implementing such an AI system.

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9 Independent Claims

1. A method comprising: obtaining a content audio data; obtaining a semantic audio data; obtaining a generated audio data; extracting a content audio feature from the content audio data; extracting a semantic audio feature from the semantic audio data; extracting a generated audio feature from the generated audio data; feeding the extracted semantic, content and generated audio features into a neural network; propagating the features through a neural network iteratively and updating the generated audio feature until convergence; and upon convergence, outputting the generated feature.
5. A method comprising: obtaining, using one or more processors, first data corresponding to a first desired audio data; obtaining, using the one or more processors, second data corresponding to one or more anchored audio events; obtaining, using the one or more processors, third data corresponding to one or more negative audio events; feeding, using the one or more processors, the first, second and third data into a neural network that is trained on an aggregate cost function of two or more cost functions; generating, using the neural network, a feature for the first desired audio data by employing one or more anchor class features and one or more negative class features; training, using the one or more processors, a classifier using the generated feature and the anchor class features as one class and the negative class features as another class; and predicting, using the one or more processors, a class for an arbitrary audio event using the trained classifier.
10. A method comprising: obtaining, using one or more processors of a device, audio data that contains two or more audio events with overlapping; feeding the audio data into a convolutional neural network (CNN), where the CNN is trained on two or more analysis windows; determining, using the one or more processors, boundaries of each audio event in a time-frequency representation of the audio data; and classifying, using the one or more processors, a category of the previously localized audio data.
13. A method of describing the content of an audio recording, comprising: extracting features from a plurality of audio recordings; feeding the features to a pre-trained, recurrent neural network; and generating, using the recurrent neural network, a sentence describing the content of the recording.
16. (canceled)
17. A system comprising: one or more processors; memory storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations comprising: obtaining a content audio data; obtaining a semantic audio data; obtaining a generated audio data; extracting a content audio feature from the content audio data; extracting a semantic audio feature from the semantic audio data; extracting a generated audio feature from the generated audio data; feeding the extracted semantic, content and generated audio features into a neural network; propagating the neural network iteratively and updating the generated audio feature until convergence; and upon convergence, outputting the generated feature that includes the content audio feature and the semantic audio feature.
18. A system comprising: one or more processors; memory storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations comprising: obtaining, using one or more processors, first data corresponding to a first desired audio data; obtaining, using the one or more processors, second data corresponding to one or more anchored audio events; obtaining, using the one or more processors, third data corresponding to one or more negative audio events; feeding, using the one or more processors, the first, second and third data into a neural network that is trained on an aggregate cost function of two or more cost functions; generating, using the neural network, a feature for the first desired audio data by employing one or more anchor class features and one or more negative class features; training, using the one or more processors, a classifier using the generated feature and the anchor class features as one class and the negative class features as another class; and predicting, using the one or more processors, a class for an arbitrary audio event using the trained classifier.
19. A system comprising: one or more processors; memory storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations comprising: obtaining, using one or more processors of a device, audio data that contains two or more audio events with or without overlapping; feeding the audio data into a convolutional neural network (CNN), where the CNN is trained on two or more analysis windows; determining, using the one or more processors, boundaries of each audio event in a time-frequency representation of the audio data; and classifying, using the one or more processors, a category of the audio data.
20. A system comprising: one or more processors; memory storing instructions that when executed by the one or more processors, cause the one or more processors to perform operations comprising: extracting features from a plurality of audio recordings; feeding the features to a pre-trained, recurrent neural network; and generating, using the recurrent neural network, a sentence describing the content of the recording; extracting features from a plurality of audio recordings; feeding the features to a pre-trained, recurrent neural network; and generating, using the recurrent neural network, a sentence describing the content of the recording.