Abstract: According to one embodiment, a transport service method comprises organizing a vehicle group of a plurality of self-driving vehicles in which a parameter value inherent to vehicles falls within a predetermined range, and controlling cooperative travel of the self-driving vehicles in the vehicle group to perform transport service of baggage, persons, animals, and the like.

Abstract: An information processing device includes an association information acquiring unit, a first position information acquiring unit, and an output control unit. The association information acquiring unit acquires association information in which first identification information used for identifying baggage and second identification information used for identifying an owner of baggage identified by the first identification information are associated with each other. The first position information acquiring unit acquires position information of target baggage that is baggage identified by the first identification information included in the association information. The output control unit causes a mobile terminal held by a target owner who is an owner of the baggage identified by the second identification information associated with the first identification information relating to the target baggage in the association information to output information based on the position information of the target baggage.

Abstract: An information processing device, an information processing system, an information processing method, and a program capable of appropriately reducing the amount of information of an image are provided. An information processing device includes an image acquisitor, an information amount reduction degree determiner, and an information amount reducer. The image acquisitor acquires an image acquired by imaging an actual space. The information amount reduction degree determiner determines a degree of reduction of an amount of information on the basis of an attribute of a subject shown in the image. The information amount reducer generates information reduced data acquired by reducing at least a part of the amount of information of the image in accordance with the degree of reduction of the amount of information.

Abstract: According to the embodiment, an optical inspection method for a surface state of a subject includes acquiring and discriminating. The acquiring includes acquiring a color vector of a color corresponding to a wavelength spectrum in a color coordinate system of n dimensions (n is a natural number equal to or larger than 1), which is equal to or smaller than a number of a plurality of color channels of pixels of an image sensor, with optical imaging using a wavelength spectrum selection portion that selectively allows a plurality of wavelength spectra different from one another from a surface of the subject to pass. The discriminating includes discriminating the surface state of the subject based on a direction of the color vector in the color coordinate system.

Abstract: A communication system includes a communication control section including a first control section configured to broadcast utterance voice data received from one of mobile communication terminals to other mobile communication terminals and a second control section configured to chronologically accumulate a result of utterance voice recognition from voice recognition processing on the received utterance voice data as a user-to-user communication history and to control text delivery such that the communication history is displayed on the mobile communication terminals in synchronization; and a utterance voice evaluation section configured to perform voice quality evaluation processing on the received utterance voice data and to output a result of voice quality evaluation. The communication control section is configured to control text delivery such that the result of voice recognition based on the utterance voice and the result of voice quality evaluation are displayed on the user terminals.

Abstract: An information processing device of an embodiment includes a feature quantity acquiring unit, a range estimating unit, an associating unit, an integrating unit, and an output unit. The feature quantity acquiring unit acquires a feature quantity extracted from data consisting of a plurality of values. The range estimating unit estimates a range of the data in which an element to which a predetermined label is to be assigned may be present from the acquired feature quantity. The associating unit associates each label with at least one of a plurality of feature quantities. The integrating unit performs integration processing of integrating one or more ranges of the data estimated from each of one or more feature quantities that are associated with the label into one range of the data. The output unit outputs a correspondence relationship between the label and the range of the data that has been subjected to the integration processing.

Abstract: According to one embodiment, a recognition device includes storage and a processor. The storage is configured to store a first recognition model, a first data set, and tags, for each first recognition model. The processor is configured to acquire a second data set, execute recognition processing of the second recognition target data in the second data set by using the first recognition model, extract a significant tag of the tags stored in the storage in association with the first recognition model, based on the recognition processing result and the second correct data in the second data set, and create a second recognition model based on the acquired second data set and the first data set stored in the storage in association with the extracted tag.

Abstract: According to the embodiment, an optical inspection method includes: emitting, acquiring, and comparing. The emitting includes emitting light beams having a first wavelength and a second wavelength toward an imaging unit in accordance with light beam directions from a subject, with light beam intensities of the first wavelength and the second wavelength being in a complementary relationship. The acquiring includes acquiring each of information of a first image related to the first wavelength and information of a second image related to the second wavelength with the imaging unit. The comparing includes comparing the information of the first image and the information of the second image to extract unevenness information of the subject.

Abstract: An information processing apparatus according to an embodiment includes one or more hardware processors. The hardware processors obtain a first categorical distribution sequence corresponding to first input data and obtain a second categorical distribution sequence corresponding to second input data neighboring the first input data, by using a prediction model outputting a categorical distribution sequence representing a sequence of L categorical distributions for a single input data piece, where, L is a natural number of two or more. The hardware processors calculate, for each i of 1 to L, an inter-distribution distance between i-th categorical distributions in the first and second categorical distribution sequences. The hardware processors calculate a sum of L inter-distribution distances. The hardware processors update the prediction model's parameters to lessen the sum.

Abstract: A data processing apparatus according to an embodiment includes a data acquisition unit, a setting unit, and a reinforcement learning unit. The data acquisition unit acquires graph-structured data describing a connection relation between nodes. The setting unit sets a first network representing the graph-structured data acquired by the data acquisition unit. The reinforcement learning unit derives a parameter of the first network such that a feature quantity of an output layer of an evaluation target node in the first network approaches a reward and a feature quantity of an output layer of an operation node becomes a feature quantity causing the feature quantity of the output layer of the evaluation target node to approach the reward.

Abstract: A communication system includes a management apparatus and an agent apparatus. The management apparatus broadcasts the voice of an utterance of one of users of mobile communication terminals to the mobile communication terminals of the other users and to chronologically accumulate the result of utterance voice recognition of the utterance voice as a user-to-user communication history to control text delivery such that the communication history is displayed on the mobile communication terminals in synchronization. The agent apparatus produces an agent utterance text based on detection information output from a state detection device provided for a monitoring target and transmitting the agent utterance text to the management apparatus.

Abstract: According to an embodiment, an image processing device includes one or more processors. The one or more processors are configured to: acquire an image; detect a first repeated pattern from the image; detect an object included in the first repeated pattern; and output the object as a second repeated pattern.

Abstract: An information processing apparatus according to one embodiment includes a memory and one or more hardware processors. The memory stores order information in which an order of pieces of meta-information for a character to be recognized is defined. The one or more hardware processors are connected to the memory and function as a recognition unit and an update unit. The recognition unit serves to perform character recognition on an image including a character string by using first meta-information specified from the pieces of the meta-information. The update unit serves to update the first meta-information to second meta-information. in accordance with the order information in a case when a confidence score of the character recognition satisfies a predetermined condition. The character recognition is performed by using the second meta-information.

Abstract: A label assigning device of an embodiment includes one or more hardware processors configured to function as a label candidate generation unit, a feature amount pair detection unit, and a label assigning unit. The label candidate generation unit generates a label candidate from association data associated with a content. The feature amount pair detection unit detects a feature amount pair that is a combination of feature amounts having a highest similarity among combinations of a feature amount extracted from a first content and a feature amount extracted from a second content. The label assigning unit assigns, as a label, a common label candidate generated from both first association data associated with the first content and second association data associated with the second content to each feature amount constituting the feature amount pair.

Abstract: A model maintenance device according to an embodiment performs maintenance of a model for pattern recognition used in label estimation of target data for recognition. The model maintenance device includes a generating unit, an evaluating unit, and an updating determining unit. The generating unit generates a new model using learning data. The evaluating unit evaluates the performance of the new model using evaluation data classified into a first group, from among evaluation data classified into a plurality of groups, and calculates a first performance evaluation value; and evaluates performance of the new model using evaluation data classified into a second group, from among the evaluation data classified into a plurality of groups, and calculates a second performance evaluation value. Based on the first performance evaluation value and the second performance evaluation value, the updating determining unit determines whether or not the existing model should be updated with the new model.

Abstract: An anomaly detection device according to the embodiment includes a prediction unit and an anomaly score calculation unit. The prediction unit performs a process to obtain, at each time step of the time series data of m dimensions, distribution parameters required to express a continuous probability distribution representing a distribution state of predicted values that can be obtained at a time step t of the time series data of m dimensions. The anomaly score calculation unit performs a process to calculate, using distribution parameters obtained by the prediction unit, an anomaly score corresponding to an evaluation value representing evaluation of a magnitude of anomaly in an actual measurement value at the time step t of time series data of m dimensions.

Abstract: A cooperative learning system usable for process monitoring in which a monitoring model is provided for each of plural processes arranged in chronological order at predetermined transition time period intervals. The system stores, in chronological order, first monitoring data in a first process, second monitoring data in a second process, and at least one monitoring result from the first process output from a first monitoring model using the first monitoring data as an input parameter. The system performs parent model learning processing for the first monitoring model using the first monitoring data and the monitoring result from the first monitoring model, and performs child model learning processing for a second monitoring model using a monitoring result from the first monitoring model at a first time as teacher data and using the second monitoring data at a second time shifted from the first time by a transition period as an input parameter.

Abstract: A mold temperature control system includes: an inference model generating portion configured to, based on a plurality of pieces of thermo image data of a mold acquired at predetermined intervals and pieces of teaching data associated with the plurality of pieces of thermo image data, learn a predetermined number of consecutive pieces of time-series image data extracted from the plurality of pieces of thermo image data as one piece of sample data to generate an inference model for detecting a sign of a temperature anomaly of the mold; a mold temperature anomaly degree inference portion configured to detect occurrence of the sign of the temperature anomaly of the mold the predetermined number ahead, using the inference model, based on the predetermined number of pieces of time-series image data of the mold; and a warning lamp request transmitting portion configured to control lighting-up of a warning lamp.

Abstract: An abnormality detection device according to an embodiment includes a detector, a remover, and a learner. The detector detects first abnormal data in detection target data which is an abnormality detection target by inputting the detection target data to a first autoencoder which performed learning based on first learning target data which is a learning target. The remover removes data associated with the first abnormal data from the first learning target data to generate second learning target data by inputting the first learning target data to a second autoencoder which performed learning based on the first abnormal data detected by the detector. The learner causes the first autoencoder to perform learning based on the second learning target data generated by the remover.

Abstract: An evaluation device according to an embodiment includes an acquirer, a first evaluator, a second evaluator, and a display controller. The acquirer acquires a training model that is an evaluation target and evaluation data. The first evaluator evaluates a functional quality of the training model based on output data acquired by inputting the evaluation data to the training model. The second evaluator evaluates a non-functional quality of the training model based on the output data. The display controller outputs an evaluation result screen including a first evaluation result according to the first evaluator and a second evaluation result according to the second evaluator to cause a display device to display the evaluation result screen.