An anomaly determination device for determining a device anomaly of a device , comprising: an input unit configured to receive data output from a sensor that detects a state of the device and a controller configured to acquire a state data of the device output from the sensor via the input unit , determine the device anomaly by performing a second determination by a second determination model configured to classify the state of the device based on the state data, after performing a first determination by a first determination model configured to determine, based on the state data, whether a predetermined anomaly has occurred in the device , and output the device anomaly as an unknown anomaly in a case where the device anomaly is not the predetermined anomaly, wherein the first determination model expresses the correspondence between state data of the device and at least two classes into which the state data should be classified, the classes corresponding to the normal state of the device and to an anomalous state of the device with the predetermined anomaly, the first determination model being generated by supervised machine learning using, as supervised data, feature values calculated based on past state data and the classes as output data corresponding to the feature values, wherein the second determination model is a model configured to classify the state data of the device into at least two groups corresponding to normal and anomalous states of the device , the second determination model being generated by unsupervised machine learning using feature values calculated based on past state data without output data corresponding to the feature values. an input unit configured to receive data output from a sensor that detects a state of the device and a controller configured to acquire a state data of the device output from the sensor via the input unit , determine the device anomaly by performing a second determination by a second determination model configured to classify the state of the device based on the state data, after performing a first determination by a first determination model configured to determine, based on the state data, whether a predetermined anomaly has occurred in the device , and output the device anomaly as an unknown anomaly in a case where the device anomaly is not the predetermined anomaly, wherein the first determination model expresses the correspondence between state data of the device and at least two classes into which the state data should be classified, the classes corresponding to the normal state of the device and to an anomalous state of the device with the predetermined anomaly, the first determination model being generated by supervised machine learning using, as supervised data, feature values calculated based on past state data and the classes as output data corresponding to the feature values, wherein the second determination model is a model configured to classify the state data of the device into at least two groups corresponding to normal and anomalous states of the device , the second determination model being generated by unsupervised machine learning using feature values calculated based on past state data without output data corresponding to the feature values. <EOS>