1. A data processing method comprising: acquiring a learning data series for learning user operation of a touch panel display, wherein continuous-valued data including at least one-dimensional continuous values are arranged in plural pieces of learning data in the learning data series, wherein a classification result is added to each of the plural pieces of learning data in the learning data series; discretizing the learning data series to generate a second learning data series of discrete-valued data; acquiring the second learning data series in which the plural pieces of learning data are arranged according to a plurality of events; fragmenting the second learning data series of discrete-valued data to obtain a plurality of partial learning data series; estimating whether an abnormal event occurs in each of the plurality of partial learning data series; generating a learning classification result of each of the plurality of partial learning data series based on respective classification results added to the learning data series; learning respective classifications of the plurality of partial learning data series based on respective feature vectors for respective partial learning data series of the plurality of partial learning data series and respective learning classification results; wherein when a classification result indicating a level of an abnormality higher than or equal to a predetermined reference level is added to each data included in each of the plurality of partial learning data series, the method associates the classification result indicating the abnormality to the partial learning data series; obtaining, in response to learning respective classifications, user input to the touch panel display, wherein the user input comprises user operation of the touch panel display; converting the user input into a data series comprising numeric values representing the user operation of the touch panel display; wherein the data series comprises: a plurality of respective sets of coordinates at which a user touches the touch panel display while moving; a distance the user travels on the touch panel display while moving; a first time the user touches the touch panel display while moving; a second time after moving and until the user separates from the touch panel display; and a third time during which no operation is performed on the touch panel display; acquiring the data series in which plural pieces of data are arranged; fragmenting the data series to obtain a plurality of partial data series, wherein respective partial data series comprise a reference interval of time comprising 10 seconds ending at each respective piece of data of the plural pieces of data, and wherein at least one respective partial data series of the plurality of partial data series overlaps with at least one other partial data series of the plurality of partial data series; extracting a plurality of patterns of one or more pieces of data appearing in at least three of the plurality of partial data series; wherein the plurality of patterns comprise at least a first pattern, a second pattern, and a third pattern occurring consecutively in a respective partial data series, wherein the extracting a plurality of patterns further comprises extracting the first pattern, the second pattern, the third pattern, a fourth pattern comprising the first pattern and the second pattern, a fifth pattern comprising the first pattern and the third pattern, a sixth pattern comprising the second pattern and the third pattern, and a seventh pattern comprising the first pattern and the second pattern and the third pattern; generating a feature vector having element values, which vary according to whether to include each of the plurality of patterns, for each of the plurality of partial data series, wherein generating the feature vector further comprises: generating, for each of the plurality of partial data series, the feature vector having an element value indicative of the presence or absence of each of the plurality of patterns, wherein the element value comprises a 1 indicating a presence of a respective pattern in a respective partial data series, wherein the element value comprises a 0 indicating an absence of a respective pattern in a respective partial data series; classifying each of the plurality of partial data series based on the feature vector and learning respective classifications; estimating a state of the user based on the feature vector and learning respective classifications; and providing operating instructions to the user based on the feature vector, the estimated state of the user, and learning respective classifications.