Abstract: A data processing method according to an embodiment acquires data including a plurality of records, divides the data based on external condition identification information such as a user ID to generate data sets Di for respective external conditions, divides each of the data sets Di based on label information indicating whether the record corresponds to a positive label indicating that a predetermined event has occurred or a negative label indicating that the predetermined event has not occurred to generate two data sets Di+ and Di? for the respective label information, generates difference data for a combination of a record included in one data set of the two data sets and a record included in the other data set, combines the generated difference data to generate integrated data Dnew, performs statistical analysis using Dnew, and outputs a result of performing the statistical analysis.

Abstract: A learning-type signal separation method performed using a model formulation unit, which performs learning processing based on a training-use signal including a specific component, and a training-use signal not including the specific component, the training-use signals including a common characteristic. The learning-type signal separation method includes: generating learned data by causing the model formulation unit to perform learning processing based on the training-use signal and information indicating whether or not the specific component is included in the training-use signal, to generate a data series signal in which the specific component has been separated and removed from a data series of the training-use signal; acquiring an arbitrary signal including the common characteristic; and generating, based on the acquired arbitrary signal and the generated learned data, a data series signal in which the specific component has been separated and removed from a data series of the arbitrary signal.

Abstract: An analysis device includes a storage unit that stores input sentences in association with information for distinguishing users, an extraction unit that extracts the input sentences stored in the storage unit on a per-user basis for respective corresponding functions, a classification unit that classifies the input sentences into intra-user similarity groups on the per-user basis for respective corresponding functions so that the input sentences extracted by the extraction unit form the intra-user similarity group consisting of input sentences similar to each other, an aggregation unit that aggregates the intra-user similarity groups among users on the per-function basis so that the intra-user similarity groups form an inter-user similarity group consisting of intra-user similarity groups similar to each other, and an output unit that outputs an aggregation result of the aggregation unit.

Abstract: A retrieval device 10 includes an input unit 11 configured to receive a search query from a user, a retrieval unit 12 configured to calculate a degree of fitness between the search query and each of a plurality of pieces of retrieval target data, a query expansion unit 13 configured to generate an expanded search query, and a policy determination unit 14 configured to determine which of a first process and a second process is to be executed on the basis of the degree of fitness for each piece of the retrieval data calculated by the retrieval unit 12. The first process is presenting the retrieval target data having a high degree of fitness to the user. The second process is proposing to the user that the retrieval unit is caused to calculate the degree of fitness for each piece of the retrieval target data using the expanded search query.

Abstract: An interaction assistance device 10 is configured to: calculate a first use degree representing a degree to which a function is used in accordance with the user speech in a first period for each function by referring to an interaction history DB (10a) storing an interaction history for each user including information representing a function corresponding to the user speech input by one or more users and information representing a time point at which the function was executed; determine a target function on the basis of the first use degree for each function; generate speech examples corresponding to the target function; and determine speech examples to be presented to the user from among the speech examples generated by the speech example generating unit (14).

Abstract: An inference device includes a survival period information input unit configured to acquire survival period information indicating a change in a value of a feature amount over a period of time from a plurality of observation subjects for each feature amount, a feature amount change model construction unit configured to construct a feature amount change model, an attribute learning information input unit configured to acquire attribute learning information, a feature amount change inference unit configured to derive a value of each feature amount for each period, an attribute inference model construction unit configured to construct an attribute inference model, and a model evaluation unit configured to derive accuracy of inference of each attribute inference model in each period.

Abstract: A time-series-data feature extraction device includes: a data processing unit that processes a received unevenly spaced time-series-data group into an evenly spaced time-series-data group including omissions and an omission information group indicating presence or absence of omissions, based on a received input time-series data length and a received minimum observation interval; a model learning unit that learns a weight vector of each layer of a model with a difference between an element not missing in a matrix of the evenly spaced time-series-data group including omissions and an element of an output result of an output layer of the model being taken as an error, and stores the weight vector as a model parameter in a storage unit, the difference being; and a feature extraction unit that receives time-series data of a feature extraction target, calculates a value of the intermediate layer of the model with use of the model parameter stored in the storage unit by inputting the received time-series data of the

Abstract: A monetary risk prediction apparatus according to an embodiment includes: a predictive model recording unit that records a predictive model to predict time-series data showing a future asset amount of a user; and prediction means for receiving evaluation data that is time-series data including an asset amount and a numeric value showing a health condition of the user, inputting the evaluation data to the predictive model recorded on the predictive model recording unit, and outputting the time-series data showing the future asset amount of the user predicted by the predictive model according to the input.

Abstract: A data processing device that makes effective use of a data group containing missing data is provided. A series of learning data containing missing data is acquired, and a representative value of data and a validity ratio representing a proportion of valid data being present are calculated from the series of learning data according to a predefined unit of aggregation. Then, learning of an estimation model is performed so as to minimize an error which is based on a difference between an output resulting from inputting the representative value and the validity ratio to the estimation model, and the representative value. Also, a series of estimation data containing missing data is acquired, and a representative value of data and a validity ratio representing a proportion of valid data being present are calculated from the series of estimation data according to a predefined unit of aggregation.

Abstract: A data processing apparatus according to a first aspect of the present invention includes: a first generation section that generates first input data in which first data related to a first phenomenon and second data related to a second phenomenon that is relevant to the first phenomenon are combined with first auxiliary data that is based on a missing data status in at least one of the first data and the second data; and a learning section that learns a model parameter of a prediction model, based on an error according to the first auxiliary data between output data outputted from the prediction model when the first input data is inputted into the prediction model, and each of the first data and the second data.

Abstract: An object of an aspect of the present invention is to enable estimation of more effective intervention content in order to make a person's health state approximate to an ideal health state, and in a learning phase, measurement values and target values of a health state for a plurality of days in the past are sequentially input to a learning machine configured by a multilayer neural network, and the learning machine is caused to perform learning such that a target achievement expectation value obtained by using a success rate that allows the user's health state to approximate to an ideal health state, continuity that allows the health state approximate to the ideal health state to be maintained, and a target value of the health state to be subsequently recommended and the target achievement expectation value thereof that reflect a temporal change in the health state and a history of interventions until a present time are output.

Abstract: According to one embodiment of the present invention, sets of medical record data that have a common development order of diseases to be focused on and different times until the diseases develop are selected from medical record data, a feature indicating a health state of a user is extracted from each piece of medical record data constituting the set for each of the sets of the medical record data, the extracted feature is set as training data, a risk score for a co-occurrence or an occurrence of a complication of each of the diseases is calculated based on examination data of a first-year examination and a time until each of the diseases occur, and the risk score is set as correct answer data. At this time, the development risk score is calculated such that a user having a short elapsed time until development has a larger value than a user having a long elapsed time until development.

Abstract: Statistical analysis taking into account potential features that affect an occurrence of a predetermined event is enabled. A data processing method according to an embodiment acquires data including a plurality of records, divides the data based on external condition identification information such as a user ID to generate data sets Di for respective external conditions, divides each of the data sets Di based on label information indicating whether the record corresponds to a positive label indicating that a predetermined event has occurred or a negative label indicating that the predetermined event has not occurred to generate two data sets Di+ and Di? for the respective label information, generates difference data for a combination of a record included in one data set of the two data sets and a record included in the other data set, combines the generated difference data to generate integrated data Dnew, performs statistical analysis using Dnew, and outputs a result of performing the statistical analysis.

Abstract: A learning-type signal separation method performed using a model formulation unit, which performs learning processing based on a training-use signal including a specific component, and a training-use signal not including the specific component, the training-use signals including a common characteristic. The learning-type signal separation method includes: generating learned data by causing the model formulation unit to perform learning processing based on the training-use signal and information indicating whether or not the specific component is included in the training-use signal, to generate a data series signal in which the specific component has been separated and removed from a data series of the training-use signal; acquiring an arbitrary signal including the common characteristic; and generating, based on the acquired arbitrary signal and the generated learned data, a data series signal in which the specific component has been separated and removed from a data series of the arbitrary signal.

Abstract: A time-series-data feature extraction device includes: a data processing unit that processes a received unevenly spaced time-series-data group into an evenly spaced time-series-data group including omissions and an omission information group indicating presence or absence of omissions, based on a received input time-series data length and a received minimum observation interval; a model learning unit that learns a weight vector of each layer of a model with a difference between an element not missing in a matrix of the evenly spaced time-series-data group including omissions and an element of an output result of an output layer of the model being taken as an error, and stores the weight vector as a model parameter in a storage unit, the difference being; and a feature extraction unit that receives time-series data of a feature extraction target, calculates a value of the intermediate layer of the model with use of the model parameter stored in the storage unit by inputting the received time-series data of the