Abstract: An information processing device according to an embodiment estimates a probability distribution of a maximum value or a minimum value of time-series target sensor data in a designated estimation period after a base time. The information processing device includes one or more hardware processors that: calculate a reference feature amount representing a feature of related sensor data at a reference time before the base time based on a preset feature amount calculation model; estimate a reference distribution parameter in a reference period based on a preset parameter estimation model; update the feature amount calculation model and the parameter estimation model; calculate an estimation feature amount representing the feature of related sensor data at the base time based on the updated feature amount calculation model; and estimate an estimation distribution parameter based on the updated parameter estimation model.

Abstract: An information processing device includes a hardware processor. The processor functions to generate first data to be input to a model used for deducing an effect of a case where a measure is executed with input of observation data. The first data represents the observation data obtained without execution of the measure. The processor functions to receive first parameters affecting the effect. Each first parameter is used in an estimation process of estimating the observation data when the measure is executed. The processor functions to execute the estimation process and generate second data representing the observation data. The processor functions to estimate the effect by using the first/second data. The processor functions to learn the model by using the first/second data. The processor functions to evaluate performance of the learned model by comparing the effect estimated by using the first/second data and the effect estimated by the learned model.

Abstract: An information processing apparatus according to one embodiment includes one or more hardware processors connected to a memory. The hardware processors functions to store, in the memory, history information including identification information of a model and a history of updating the model. The model receives input data including variables and outputs output data. The variables are each a variable for which a rate of influence on the output data is calculated. The model has been updated by using first input data. The hardware processors functions to select a target model to be updated by using second input data. The target model is selected from among models identified by their respective identification information. The hardware processors functions to update the target model by performing transfer learning in which updated parameters are estimated by using the second input data.

Abstract: According to an embodiment, an information processing apparatus is configured to set a candidate for a time lag until analysis target data including at least one of a measurement item and a setting item for use in control of a process controller affects an objective variable, and a time-lag number allowed in a regression model; select, as a candidate for an explanatory variable, at least one of the measurement item measured at a time corresponding to the candidate for the time lag and the setting item set at the time; and determine a regularization parameter of the regression model such that a number of the time lag is equal to or less than the time-lag number, based on a regularization path indicating transition of a regression coefficient for the candidate for the explanatory variable, the regression coefficient varying in accordance with a value of the regularization parameter.

Abstract: According to an embodiment, an information processing apparatus is configured to set a candidate for a time lag until analysis target data including at least one of a measurement item and a setting item for use in control of a process controller affects an objective variable, and a time-lag number allowed in a regression model; select, as a candidate for an explanatory variable, at least one of the measurement item measured at a time corresponding to the candidate for the time lag and the setting item set at the time; and determine a regularization parameter of the regression model such that a number of the time lag is equal to or less than the time-lag number, based on a regularization path indicating transition of a regression coefficient for the candidate for the explanatory variable, the regression coefficient varying in accordance with a value of the regularization parameter.

Abstract: An information processing device according to an embodiment estimates a probability distribution of a maximum value or a minimum value of time-series target sensor data in a designated estimation period after a base time. The information processing device includes one or more hardware processors that: calculate a reference feature amount representing a feature of related sensor data at a reference time before the base time based on a preset feature amount calculation model; estimate a reference distribution parameter in a reference period based on a preset parameter estimation model; update the feature amount calculation model and the parameter estimation model; calculate an estimation feature amount representing the feature of related sensor data at the base time based on the updated feature amount calculation model; and estimate an estimation distribution parameter based on the updated parameter estimation model.

Abstract: An electric power demand prediction system has an extractor to select electric power consumption value data included in a certain period out of a consumer's past electric power consumption value data and to extract an outdoor temperature-electric power relation that is a relation between an outdoor temperature and outdoor-temperature-depend electric power varying in accordance with the outdoor temperature out of electric power consumption, a model generator to generate an electric power consumption prediction model which predicts electric power consumption by the consumer in accordance with the outdoor temperature based on the outdoor temperature-electric power relation extracted by the extractor, and a predictor to predict electric power consumption by the consumer at a time subject to prediction based on the electric power consumption prediction model generated by the model generator and the outdoor temperature at the time subject to prediction.

Abstract: According to one embodiment, an information providing device includes; an evaluation value calculator, a profile setter and an information transmission processing unit. The evaluation value calculator calculate an evaluation value on each of a plurality of time periods based on behavior history data which indicates a history of days and times of occurrence of a specific behavior taken by a user, the evaluation value indicating likelihood of the specific behavior being taken by the user. The profile setter sets a profile on the user based on the evaluation values of the time periods, the profile indicating a tendency of time periods at which the specific behavior is taken by the user. The information transmission processing unit determines offer information for providing to the user according to the profile of the user and transmits the offer information to an output device related to the user.

Abstract: According to one embodiment, a measurement unit periodically measures a usage status of a measurement target, and measures each test item of the measurement target at an arbitrary timing according to a test schedule. A first storage unit stores the usage status and a test result in time series. A second storage unit stores statistic information representing a relationship between the usage status and the test result. A probability calculation unit calculates a first evaluation value representing a load of the measurement target at a recent timing and a second evaluation value representing a load of the measurement target at the arbitrary timing, based on the usage status, and calculates a specific probability of each test item, based on the statistic information, the first evaluation value and the second evaluation value. A test schedule creation unit creates a new test schedule having selected test items, based on the specific probability.

Abstract: According to one embodiment, a behavior estimation apparatus includes a storage unit, a first calculation unit, a second calculation unit, and an estimation unit. The storage unit stores first data collecting power values consumed by a consumer in a period. The first calculation unit calculates second data representing a frequency of each power value by using the first data. The second calculation unit calculates a first threshold to divide a first power value from a second power value which is larger than and next to the first power value, among power values corresponding to maximal values of frequencies included in the second data. The estimation unit obtains a power value consumed by the consumer in an estimating period, and estimates the consumer's behavior status in the estimating period by using the power value and the first threshold.

Abstract: According to one embodiment, a behavior estimation apparatus includes a storage unit, a first calculation unit, a second calculation unit, and an estimation unit. The storage unit stores first data collecting power values consumed by a consumer in a period. The first calculation unit calculates second data representing a frequency of each power value by using the first data. The second calculation unit calculates a first threshold to divide a first power value from a second power value which is larger than and next to the first power value, among power values corresponding to maximal values of frequencies included in the second data. The estimation unit obtains a power value consumed by the consumer in an estimating period, and estimates the consumer's behavior status in the estimating period by using the power value and the first threshold.

Abstract: According to one embodiment, an evaluating apparatus includes an operation data storage unit, a labeling unit, a learning unit, and an evaluating unit. The labeling unit applies a failure label, indicating that a product is broken down, to operation data of the product that is broken down within a designated period of time from the observation date of the operation data, while applies a non-failure label, indicating that the product is not broken down, to the operation data of the product that is not broken down within a designated period of time from the observation date of the operation data. The labeling unit applies neither the failure label nor the non-failure label to the operation data of the product, which is not certain that it is broken down or not within a designated period of time from the observation date of the operation data.

Abstract: There is provided an electronic apparatus system. The system includes: an electronic apparatus; and a server configured to receive usage information about usage status of the electronic apparatus. The server includes: a first database; a second database; a writer configured to store the usage information in the first database and to write a flag value into the second database, wherein the flag value indicates whether or not the usage information is transmitted from the electronic apparatus through a network; a generator configured to generate a first parameter for calculating a failure probability of the electronic apparatus, based on the usage information and repair information of the electronic apparatus, wherein the repair information is stored in the server in advance; and a transmitter configured to transmit the first parameter to the electronic apparatus.

Abstract: According to one embodiment, a first reading unit reads, from a defect database, first identification information corresponding to a first product type. A second reading unit reads, from a monitoring database, defect monitoring information corresponding to the first identification information and non-defect monitoring information that corresponds to the first product'type and that is other than the defect monitoring information. A generating unit generates a defect, model that models a probability of products becoming defective within a predetermined time period with respect to the monitoring information, based on the defect monitoring information and the non-defect monitoring information. A first calculating unit calculates a defect probability of products of a second product type by inputting the monitoring information corresponding to the second product type into the defect model. A second calculating unit calculates a defect rate of the products of the second product type based on the defect probability.

Abstract: According to one embodiment, a quality estimation apparatus includes: a storage module which stores designation information for designating inspection targets to be subjected to sampling inspection in estimation targets including the inspection targets and non-inspection targets, characteristic values obtained by the sampling inspection of the inspection targets and criterion information for determining qualities of the inspection targets based on the characteristic values; a threshold value calculator which calculates threshold values indicating qualities of the inspection targets from the characteristic values of the inspection targets by using the criterion information; and a clustering module which classifies the estimation targets in clusters so that the clusters have probability distributions with the threshold values used as a variable.

Abstract: According to one embodiment, a measurement unit periodically measures a usage status of a measurement target, and measures each test item of the measurement target at an arbitrary timing according to a test schedule. A first storage unit stores the usage status and a test result in time series. A second storage unit stores statistic information representing a relationship between the usage status and the test result. A probability calculation unit calculates a first evaluation value representing a load of the measurement target at a recent timing and a second evaluation value representing a load of the measurement target at the arbitrary timing, based on the usage status, and calculates a specific probability of each test item, based on the statistic information, the first evaluation value and the second evaluation value. A test schedule creation unit creates a new test schedule having selected test items, based on the specific probability.

Abstract: According to one embodiment, an evaluating apparatus includes an operation data storage unit, a labeling unit, a learning unit, and an evaluating unit. The labeling unit applies a failure label, indicating that a product is broken down, to operation data of the product that is broken down within a designated period of time from the observation date of the operation data, while applies a non-failure label, indicating that the product is not broken down, to the operation data of the product that is not broken down within a designated period of time from the observation date of the operation data. The labeling unit applies neither the failure label nor the non-failure label to the operation data of the product, which is not certain that it is broken down or not within a designated period of time from the observation date of the operation data.