Abstract: An information processing device configured to: extract, for each of a plurality of pieces of first input information including a target string representing a target and a category string representing a category to which the target belongs, a first substring included in the target string, and extract, from the first substring, a second substring using the category string; determine a second substring associated with the target string included in the first input information; and train first feature information and second feature information to increase a matching rate between a category of the target string estimated from the second feature information and a category represented by the category string, the first feature information representing a feature of the determined second substring, the second feature information representing a feature of the target string including the determined second substrings, and being calculated based on the first feature information.

Abstract: An information processing device includes one or more processors. The one or more processors are configured to: adjust a first distance between first time-series data and a plurality of identification patterns corresponding to a plurality of classes by using a degree of existence indicating a degree by which a plurality of times of the first time-series data are times at which the plurality of respective identification patterns exist; calculate a first feature amount indicating a feature of the first time-series data by using the adjusted first distance; and obtain, by inputting the first feature amount to an estimation model learning a parameter, the similar identification pattern, and the degree of existence to reduce an estimation error, the identification pattern similar to the first time-series data, the class, and the degree of existence.

Abstract: According to one embodiment, an information processing apparatus includes a processor. The processor is configured to learn a local waveform pattern and a state estimator used to estimate a state of a device, based on multiple elements of first sub-time series data divided from first time series data representing the waveform based on a base cycle of the waveform of a physical quantity changing in accordance with an operation of a device.

Abstract: One embodiment of the present provides an apparatus and the like that uses a model that estimates shapelet deformation which is according to variations in an anticipated factor, and thereby suppresses a drop in classification performance even if the circumstances of the anticipated factor are different between training and testing. An information processing apparatus according to one embodiment of the present invention is provided with an adjuster and a feature calculator. The adjuster adjusts the shape of a reference waveform pattern corresponding to time series data and used to classify the time series data, on the basis of the value of a factor parameter corresponding to the time series data. The feature calculator calculates a feature of a waveform of the time series data on a basis of the shape of the reference waveform pattern after the adjustment.

Abstract: An information processing device includes one or more processors. The one or more processors are configured to: adjust a first distance between first time-series data and a plurality of identification patterns corresponding to a plurality of classes by using a degree of existence indicating a degree by which a plurality of times of the first time-series data are times at which the plurality of respective identification patterns exist; calculate a first feature amount indicating a feature of the first time-series data by using the adjusted first distance; and obtain, by inputting the first feature amount to an estimation model learning a parameter, the similar identification pattern, and the degree of existence to reduce an estimation error, the identification pattern similar to the first time-series data, the class, and the degree of existence.

Abstract: An information processing device according to one embodiment includes one or more hardware processors. The hardware processors executes update processing on a designated number of first sections on the basis of a first estimation result and a second estimation result. The first estimation result is obtained by inputting first time-series data to an estimation model. The second estimation result is obtained by inputting second time-series data to the estimation model. The second time-series data is obtained by applying mask processing to partial time-series data of a second section in the first time-series data. The second section is other than the designated number of the first sections.

Abstract: According to one embodiment, an information processing device includes one or more processors. The one or more processors are configured to: detect whether input waveform data is in a first state by using a detection model; acquire a plurality of pieces of second state waveform data in a second state detected in advance by using the detection model when detected to be in the first state; learn a classification model for classifying whether waveform data is in the first state or the second state, by using first state waveform data detected to be in the first state and the plurality of pieces of second state waveform data as learning data, to generate one or more partial waveform patterns serving as a basis for indicating that the first state waveform data is in the first state; and output the generated partial waveform pattern.

Abstract: One embodiment of the present invention provides an information processing apparatus for precisely estimating a state of equipment. An information processing apparatus as one embodiment of the present invention includes: an acquirer; a calculator; and a determiner. The acquirer is configured to acquire first data about a predetermined target. The calculator is configured to calculate distribution of magnitude of a value included in the first data. The determiner is configured to determine a portion of a width of the distribution as a specific range used for estimating a state of the target based on the distribution.

Abstract: According to one embodiment, an information processing apparatus includes processing circuitry configured to acquire transition data representing a transition of a plurality of operations of a monitoring object, divide, based the transition data, a first waveform of a sensor with respect to the monitoring object into a plurality of sections corresponding to the plurality of operations, and generate a first estimation model related to a state of the monitoring object based on partial waveforms of the plurality of sections in the first waveform.

Abstract: A data processing system according to the present disclosure includes an acquisition unit and a production unit. The acquisition unit acquires on-site investigation data related to the space. The production unit changes, with respect to article data of the article to be placed in the space, at least one of a dimension, design, and a function on the basis of the on-site investigation data so as to achieve harmony with the space and produces article processing data for processing the article according to the article data after the change. The on-site investigation data includes space-related data related to the space. The production unit produces the article processing data on the basis of the space-related data.

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 incudes one or more hardware processors. The processors calculate first similarities between a plurality of probabilistic models each modeling a probability of a value, at corresponding time, of time series data whose data length is a specific value, and a plurality of pieces of partial time series data whose data length is the specific value, the plurality of pieces of partial time series data being contained in target time series data to be a target of diagnosis; and determine a plurality of pieces of matching information including positions of the plurality of pieces of partial time series data in the target time series data, first probabilistic models whose first similarities with respect to the plurality of pieces of partial time series data at the positions are larger than other probabilistic models, and the first similarities to the first probabilistic models.

Abstract: An information processing apparatus includes a candidate generating unit and a correcting unit. The candidate generating unit generates, using first log information indicating states of a monitoring target and acquired in a certain time period, a frequently appearing transition path of states of the monitoring target, as a candidate of a first path that is a transition path presumed to represent a first specific condition. The correcting unit obtains the first path by correcting the candidate using a predetermined reference path.

Abstract: An information processing device includes processing circuitry configured to classify a plurality of partial waveform patterns that characterize a plurality of time series data into a plurality of classes based on the plurality of time series data classified into the plurality of classes, update shapes of the partial waveform patterns by fitting the partial waveform patterns to the time series data of the corresponding class, and reclassify the plurality of time series data into the plurality of classes based on the updated partial waveform patterns and difficulty levels that represent degrees of difficulty of classification and interpretation of the time series data.

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: One embodiment of the present invention provides an apparatus and the like for improving the accuracy of deterioration estimation regarding a monitoring target. An information processing apparatus as one embodiment of the present invention includes a detector, a separator, a calculator, and an estimator. The detector detects a normal waveform pattern included in a waveform of time-series data of a target. The separator separates the waveform of the time-series data into components while removing the normal waveform pattern that is detected. The calculator calculates a feature of the time-series data based on at least one of the components. The estimator estimates deterioration of the target based on the feature.

Abstract: One embodiment of the present provides an apparatus and the like that uses a model that estimates shapelet deformation which is according to variations in an anticipated factor, and thereby suppresses a drop in classification performance even if the circumstances of the anticipated factor are different between training and testing. An information processing apparatus according to one embodiment of the present invention is provided with an adjuster and a feature calculator. The adjuster adjusts the shape of a reference waveform pattern corresponding to time series data and used to classify the time series data, on the basis of the value of a factor parameter corresponding to the time series data. The feature calculator calculates a feature of a waveform of the time series data on a basis of the shape of the reference waveform pattern after the adjustment.

Abstract: An information processing apparatus as one embodiment of the present invention includes a feature value calculator, a classifier, an updater, and a detector. The feature value calculator calculates feature values of waveforms of a plurality of time-series data for each of a plurality of reference waveform patterns. The classifier acquires a classification result by inputting the feature values to a classification device. The updater updates a shape of each of the reference waveform patterns, and a plurality of parameters of the classification device. The detector detects reference waveform patterns having a relationship, from the plurality of reference waveform patterns, based on the parameters of the classification device.

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 processing circuitry configured to classify a plurality of partial waveform patterns that characterize a plurality of time series data into a plurality of classes based on the plurality of time series data classified into the plurality of classes, update shapes of the partial waveform patterns by fitting the partial waveform patterns to the time series data of the corresponding class, and reclassify the plurality of time series data into the plurality of classes based on the updated partial waveform patterns and difficulty levels that represent degrees of difficulty of classification and interpretation of the time series data