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US 2020/0349448 A1Published application
Systems, methods, and devices for biophysical modeling and response prediction
AssigneeJanuary, Inc.
InventorsParin Bhadrik DALAL, Salar RAHILI, Solmaz Shariat TORBAGHAN, Mehrdad YAZDANI
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Abstract
Various systems and methods are disclosed. One or more of the methods disclosed uses machine learning algorithms to predict biophysical responses from biophysical data, such as heart rate monitor data, food logs, or glucose measurements. Biophysical responses may include behavioral responses. Additional systems and methods extract nutritional information from food items by parsing strings containing names of food items.
Claims21
A method, comprising:
- training a neural network with time series training data of a first modality and time series training data of a second modality to create a first model that generates time series data of the second modality from time series data of the first modality;
- training a second model with the generated time series of the second modality, time series training data of a third modality, and time series data of a fourth modality to generate time series data of the fourth modality;
- until a convergence condition is reached, iteratively testing the second model on the time series data of the first modality and the time series data of the third modality;
- and
- responsive to reaching the convergence condition, predicting second modality data by testing the second model with data of the first modality.
2 more elements
A system, comprising:
- an initial model section that includes a first model trained to generate time series data of a second modality from time series data of a first modality with M sets of training data;
- a training section that includes:
- a second model derived from the first model and configured to generate time series data of at least a third modality from at least time series data of a fourth modality with N sets of training data, and
- a testing section configured to test the second model with the M and N sets of training data, and update the second model in response to test error values;
- and
- an inference model that is the second model with the lowest test error value, configured to infer time series data of the second modality from time series data of the first modality.
3 more elements
A method, comprising:
- training a neural network with time series training data of a first modality and time series training data of a second modality to create a first model that generates time series data of the second modality from time series data of the first modality;
- until a convergence condition is reached:
- using a second model to generate estimated time series data of the first modality from a mixture of time series data from a third modality and a fourth modality, wherein the second model is initiated as an inverse model of the first model;
- using the estimated time series data of the first modality and time series data of the third modality, training the second model to estimate linear fitting parameters;
- using the estimated linear fitting parameters to generate analogous time series data of the first modality from the time series data of the third modality;
- linearly mapping the analogous time series data of the first modality to the time series data of the third modality;
- training a third model using the linearly mapped analogous time series data from the first modality mixture of time series data of the third modality and time series data of the fourth modality to generate a mixture of time series data from the third modality and time series data from the fourth modality, wherein the third model is an inverse of the second model;
- modifying the second model to be an inverse model of the third model;
- and
- evaluating whether the convergence condition has been reached.
7 more elements
A method for training a neural network to calibrate time series data, comprising:
- receiving calibrated time series data for a biophysical response and corresponding raw time series data for the biophysical response;
- training, on the calibrated time series data and the corresponding raw time series data for the biophysical response, a neural network to generate calibrated time series data, which training comprises updating parameters of the neural network based on a difference between (i) an output of the neural network for a given raw time series and (ii) a corresponding calibrated times series;
- receiving raw input time series data generated by a biophysical sensor;
- and
- generating calibrated time series data by applying the raw input time series data to the neural network.
2 more elements
Dependencies are inferred from the claim text.