Abstract: An information processing apparatus includes: a parameter estimation unit that estimates, as parameters indicating a temporal change in interest of a user, a first parameter, a second parameter, and a third parameter based on data including a past behavior of the user and an evaluation value of the behavior, the first parameter being a parameter indicating inherent constant interest of the user, the second parameter being a parameter indicating an effect that the user is influenced by a past behavior and is attracted to a behavior option, and the third parameter being a parameter indicating an effect that the user loses interest due to boredom by a past behavior; a social welfare calculation unit that calculates a value indicating social welfare of the user based on the estimated parameters; and an optimum behavior selection unit that selects an optimum behavior of the user based on the calculated value indicating social welfare and outputs data indicating the selected optimum behavior.

Abstract: A robot system (1) includes the robot (10), a motion sensor (11), a surrounding environment sensor (12, 13), an operation apparatus (21), a learning control section (41), and a relay apparatus (30). The robot (10) performs work based on an operation command. The operation apparatus (21) detects and outputs an operator-operating force applied by the operator. The learning control section (41) outputs a calculation operating force. The relay apparatus (30) outputs the operation command based on the operator-operating force and the calculation operating force. The learning control section (41) estimates and outputs the calculation operating force by using a model constructed by performing the machine learning of the operator-operating force, the surrounding environment data, the operation data, and the operation command based on the operation data and the surrounding environment data outputted by the sensors (11 to 13), and the operation command outputted by the relay apparatus (30).

Abstract: An observation streamlining apparatus includes one or more computers each including a memory and a processor configured to discriminate between an observation-necessary time slot and an observation-unnecessary time slot with an intervention measure including at least a time when a predetermined intervention is performed on a user as an input, the observation-necessary time slot indicating a time slot when a user's action or state needs to be observed, and the observation-unnecessary time slot indicating a time slot when the user's action or state does not need to be observed; and execute predetermined processing for observing the user's action or state when the observation-necessary time slot arrives.

Abstract: A parameter estimation device for estimating a plurality of parameters used for calculating high-resolution data from aggregated data aggregated to coarse granularity, the parameter estimation device comprising: a parameter estimation unit configured to estimate a plurality of parameters that are unknown variables in a model so as to maximize a marginal likelihood based on the assumption that actually observed aggregated data is generated from the model based on a multivariate Gaussian process in which a plurality of latent Gaussian processes for a plurality of types of aggregated data in a plurality of domains are represented by linear mixing; and a storage unit configured to store the plurality of parameters, wherein the plurality of parameters include a hyperparameter of a prior distribution to a mixing coefficient used in the linear mixing.

Abstract: A computer executes a first calculation procedure of calculating a first score regarding personality characteristics of a participant based on a questionnaire result for the participant in a group dialogue, a second calculation procedure of calculating a second score regarding an activity level of the participant in the group dialogue based on data in which contents of the group dialogue is recorded, and a third calculation procedure of calculating a third score indicating evaluation on the group dialogue by the participant based on the first score and the second score, thereby improving estimation accuracy of evaluation by each participant for the group dialogue.

Abstract: A prediction method executed by a computer including a memory and a processor, the method includes: optimizing a parameter of a second function that outputs parameters of a first function from covariates, and optimizing a parameter of a kernel function of a Gaussian process, by using a series of observation values observed in a past and a series of the covariates observed simultaneously with the observation values, wherein values obtained by non-linearly transforming the observation values by the first function follow the Gaussian process; and calculating a prediction distribution of observation values in a period in future to be predicted by using the second function and the kernel function having parameters optimized in the optimizing, and a series of covariates in the period.

Abstract: A computer evaluates, from a first behavior history including, for each of a plurality of behaviors of a person, a time of the behavior and a numerical value indicating the person's state after the behavior, a first feature indicating a first amount of effort the person makes until the numerical value exceeds a threshold at a certain point in time; evaluates, from the first behavior history, a second feature indicating a degree of the person's habituation to a state indicated by the threshold by the certain point in time; and trains a prediction model, in which the first and second features are used as explanatory variables and a time interval from a behavior at the certain point in time to a next behavior in the first behavior history is used as an explained variable, based on the first and second features and the time interval.

Abstract: An information processing apparatus executes transmitting information related to a question for a user to a terminal of the user at a timing according to information indicating a characteristic of the user, information indicating experience of the user, and a history of responses by the user.

Abstract: According to an embodiment, a point process learning method executed by a computer includes: an input procedure of inputting a learning data set including at least first event data representing a series of occurrences of first events; a division procedure of dividing the first event data included in the learning data set by using a prediction time observation area including at least a time series when predicting future event occurrence; and a learning procedure of learning a model parameter including a parameter of an intensity function of a predetermined point process model by using a divided learning data set divided in the division procedure.

Abstract: An estimation method according to an embodiment is an estimation method that estimates a MAP solution of a CGM on a path graph, in which a computer executes: an input procedure that receives, as inputs, aggregate data and potentials of the CGM on the path graph; an estimation procedure that uses the aggregate data and the potentials to solve a MAP estimation problem of the CGM by a technique based on discrete DC programming and calculates a MAP estimation solution; and an output procedure that outputs the MAP estimation solution.

Abstract: A learning device includes: a psychological state data preprocessing unit that calculates a duration of a psychological state from psychological state sequence data, and generates preprocessed psychological state sequence data including the psychological state and the duration; and a learning unit that learns a psychological state sequence prediction model, using input sequence data including behavior sequence data and the preprocessed psychological state sequence data, and correct sequence data that is preprocessed psychological state sequence data at a time later than the input sequence data.

Abstract: Disclosed herein is a touch panel including: a sensor substrate and a cover substrate stuck to each other. The sensor substrate includes a sensor electrode, and plural signal wirings electrically connected to the sensor electrode and extending along a periphery of the sensor electrode. The cover substrate includes one or plural conductive layers extending along the periphery of the sensor electrode and the plural signal wirings within an area not facing the sensor electrode and the plural signal wirings.

Abstract: An estimation apparatus includes a memory; and a processor configured to execute: receiving spatiotemporal population data and a probability of movement between areas as input; constructing a collective graphical model (CGM) in a path graph for estimating a number of people who have moved between areas from the spatiotemporal population data and the probability of movement between areas; generating an instance of a minimum cost flow problem for performing MAP estimation on the constructed CGM; solving the instance of the minimum cost flow problem to estimate the number of people who have moved between areas at individual time steps; and outputting the estimated number of people who have moved between the areas at the individual time steps.

Abstract: Disclosed is a time-specific area population estimation method executed by a computer. The method includes estimating a time-specific interareal movement probability, based on observed time-specific population in an area and a set of candidate areas for a movement from the area in a unit time; and estimating a population in the area at a time at which no observation is performed by using a cost function learned in the estimating of the time-specific interareal movement probability.

Abstract: An optimization method according to one embodiment which is executed by a computer, the method including: an input procedure of inputting an acceptance probability function indicating a probability that each participant participating in a crowdsourcing market will accept a price, and a matching value indicating a value when each resource in the crowdsourcing market is allocated to each participant; a formulation procedure of formulating a first optimization problem for determining an optimal price that maximizes a profit of a provider of the crowdsourcing market using the acceptance probability function and the matching value; and an optimization procedure of calculating the optimal price by solving the first optimization problem according to a feature of the acceptance probability function.

Abstract: An optimization method according to an embodiment is executed by a computer, the method including: receiving a participation probability function of each of groups participating in a two-sided market, the two-sided market including a first side and a second side, a total number of participants included in the group, a maximum number of participants on the second side with whom the participants can perform a transaction, a set of combinations of groups that can perform a transaction between the two sides, formulating, using the participation probability function, the total number, the maximum number, and the set of combinations, a first optimization problem for determining an optimal price for a participation fee that maximizes a profit of an intermediary of the two-sided market and a number of transactions between the participants, and calculating the optimal price by solving the first optimization problem according to a characteristic of the participation probability function.

Abstract: Disclosed herein is a touch panel including: a sensor substrate and a cover substrate stuck to each other. The sensor substrate includes a sensor electrode, and plural signal wirings electrically connected to the sensor electrode and extending along a periphery of the sensor electrode. The cover substrate includes one or plural conductive layers extending along the periphery of the sensor electrode and the plural signal wirings within an area not facing the sensor electrode and the plural signal wirings.

Abstract: It is possible to improve the accuracy of estimation of a psychological state of a person by causing a computer to execute calculating probabilities of a plurality of mood transitions based on first time-series data on a mood of a first person in a first period, calculating average persistence times of the plurality of mood transitions based on the first time-series data, and performing learning of a neural network that estimates a psychological state of a person based on a vector including the probabilities of the plurality of mood transitions and a vector including the average persistence times of the plurality of mood transitions, based on the probability calculated for each of the plurality of mood transitions, the average persistence time calculated for each of the plurality of mood transitions, and data indicating, per time interval, the psychological state of the first person in the time interval that is obtained by dividing the first period into a plurality of parts.

Abstract: The present invention can forecast a future-mood and to present the forecasting and a behavior that is the basis of the forecasting by causing a computer to execute: a first-training procedure for training a first-neural network in accordance with behavior time-series data and mood time-series data in each time interval, the first-neural network using behavior time-series data and mood time-series data in a first-time interval as input to output a forecasted-value of behavior time-series data in a time interval following the first-time interval; and a second-training procedure for training a second-neural network in accordance with behavior time-series and mood time-series data per time interval, the second-neural network using the behavior time-series data in the following time interval and the mood time-series data in the first-time interval as input to output a forecasted-value of mood time-series data in the following time interval.

Abstract: A learning apparatus includes a memory including a first model and a second model, and a processor configured to execute causing the first model to accept a plurality of frame images included in a video as input, and output a feature vector for each frame image; causing the second model to accept the feature vector for each frame image as input, and output a temporal interval between a frame image treated as a reference and each of the frame images other than the frame image treated as the reference; and updating parameters of the first and second models such that each of the temporal intervals output from the second model approaches each temporal interval computed from time-related information pre-associated with each frame image.