Abstract: A type estimation model generation system is a system that generates a type estimation model used for type estimation of estimating one of a plurality of types to which a user belongs, the system including: a learning data acquiring unit configured to acquire learning time series information that is information of a time series about a plurality of used musical pieces and learning type information representing types to which users who have used the plurality of musical pieces belong that are learning data used for machine learning; and a model generating unit configured to generate the type estimation model by performing machine learning using information based on the learning time series information as an input for the type estimation model in units of musical pieces in order of the time series and information based on the learning type information as an output of the type estimation model.

Abstract: A POI (Point Of Interest) information updating device (1) includes an input unit (10) configured to input POI identification information for identifying a POI, an acquisition unit (11) configured to acquire event information on an event performed at a target POI identified by the POI identification information input by the input unit, a search unit (12) configured to search for similar event information similar to the event information acquired by the acquisition unit (11), and an updating unit (13) configured to update information on the target POI on the basis of information on a POI at which an event indicated by the similar event information searched for by the search unit (12) has been performed.

Abstract: An click rate prediction model construction device 1 includes: an image generating unit 13 configured to generate a plurality of similar images S similar to a basic image B which is displayed as an advertisement; a derivation unit 14 configured to derive an estimated value of a click rate of each of the plurality of similar images S on the basis of an actual value and a certainty factor of a click rate of the basic image B; and a model constructing unit 15 configured to learn the actual value and the estimated value of the click rate of the basic image B for each of the plurality of similar images S and to construct a click rate prediction model.

Abstract: Provided is a re-ranking device that enables a re-ranking process with less possibility of decrease in accuracy due to shortage of data. A re-ranking device 100 includes an input unit 101 that receives a search query from one user, a re-ranking model storage unit 106 that stores a plurality of re-ranking models prepared in accordance with common information of a plurality of users, a search unit 102 that performs a search on the basis of the search query and obtains a search result, and a re-ranking processing unit 107 that selects one re-ranking model on the basis of common information of the one user and performs a re-ranking process on the search result using the one re-ranking model.

Abstract: A demand prediction device calculates time-series data of a prediction value of future demand for durable goods belonging to a particular broad division through time-series analysis, constructs a model for calculating a purchase probability based on attribute information, information on types of durable goods purchased by the users, and time information on the purchase, calculates purchase probability data including a purchase probability of the durable goods in each of time periods for each of users and a purchase probability for the broad division and each of the plurality of subdivisions of durable goods for each of the users by inputting the attribute information of the users to the model, and calculates and outputs a prediction value of the total demand for each of the plurality of subdivisions in a particular time period based on the time-series data of the prediction value and the purchase probability data.

Abstract: A price prediction device includes an attribute acquisition unit that acquires data indicating attributes of consumers, a history acquisition unit that acquires data indicating a history of a product purchased by the consumer and a purchase price of the product, a probability acquisition unit that derives a selection probability of a target product being selected from a product group for each consumer based on the attribute and the history, and a derivation unit that derives an optimal price of the target product for each consumer based on the selection probability of the target product and a selling price of the target product, by using a price prediction model constructed by machine learning.

Abstract: An inventory management device (1) includes an acquisition unit (11) that acquires a first learned model related to inventory management of old products, and relevance information related to relevance between the old products and new products, an assessment unit (14) that assesses whether or not the first learned model is applied to inventory management of the new products, based on the relevance information, and a determination unit (15) that applies the first learned model to the inventory management of the new products when the assessment unit (14) assesses that the first learned model is applied, and determines a policy for the inventory management of the new products.

Abstract: An advertisement budget optimization device (1) includes an estimation unit (13) that estimates a cost per click of an advertisement for each predetermined unit period (specifically, one day) included in a delivery period of an advertisement campaign as a delivery target, and a determination unit (14) that determines an advertisement budget for each unit period so that a total number of clicks of the advertisement is maximized, based on the cost per click in each unit period estimated by the estimation unit (13) and a total budget for the advertisement campaign in the delivery period.

Abstract: An information generation apparatus includes a feature information generator that generates feature information on transition to a first screen based on a log including a transition timepoint and identification information, the transition timepoint being a timepoint when a foreground screen transitioned from a second screen to the first screen among a plurality of screens displayed by a plurality of program modules that differ from each other, and the identification information identifying from among the program modules a first program module for displaying the first screen, an index value generator that generates an index value indicative of a degree to which the first screen draws attention from a user based on the feature information, and a determiner that determines based on the index value whether to use the identification information that identifies the first program module in a process of estimating the state of the user.

Abstract: A popularity estimation device includes: an acquisition unit configured to acquire a first map image including a first particular point and to acquire a plurality of second map images including a second particular point which is a particular point; a generation unit configured to generate a first feature vector and a second feature vector by inputting the first map image and the second map images to a geographical feature generation model with a map image as an input and with a feature vector indicating geographical features of the map image as an output; a score calculating unit configured to calculate a popularity score of the first particular point from a popularity score of the second particular point based on a degree of similarity between the first feature vector and the second feature vector; and an output unit configured to output the popularity score of the first particular point.

Abstract: An object is to predict a more accurate demand. A demand prediction device (1) includes an information storing unit (10) configured to store a plurality of demand prediction models, each of the demand prediction models being a prediction model configured to predict a demand in a demand state relating to demand and differing between each demand prediction model and a state prediction model that is a prediction model predicting a state degree that is a degree to which a designated timing is applicable to each demand state; and a demand predicting unit (15) configured to predict a demand on the basis of the demands predicted by the plurality of demand prediction models stored by the information storing unit (10) and the state degree predicted by the state prediction model stored by the information storing unit (10).

Abstract: A feature extraction device is a device that extracts a feature quantity to be input to a state estimation model for estimating a state related to a terminal where the activity is executed or a user of the terminal, which includes an acquisition unit that acquires activity history information indicating an execution history of the activity in a specified period of time and including time-series activity logs, an extraction unit that extracts co-occurrence relationship information indicating a co-occurrence relationship of activities in a specified period of time based on the activity history information, a generation unit that aggregates activity feature quantities indicating a feature of each activity based on the co-occurrence relationship information and generates an activity history feature quantity indicating a feature of the activity history information, and an output unit that outputs the activity history feature quantity as a feature quantity to be input to the state estimation model.

Abstract: An object is to forecast a demand more flexibly. A demand forecasting device 1 that forecasts a demand in a first area, which is a predetermined area, includes a storage unit (10) configured to store area information regarding a second area, the second area being a predetermined area relevant to a demand of the first area, an acquisition unit (11) configured to acquire information on the number of people in the second area indicated by the area information stored in the storage unit (10), and a forecasting unit (12) configured to forecast a demand in the first area on the basis of the information on the number of people in the area acquired by the acquisition unit (11).

Abstract: A stress estimation system which estimates stress of a user includes a walking speed acquisition unit configured to acquire walking speed information indicating walking speeds of a user in each of the morning and the afternoon at a preset place, an estimation unit configured to calculate a comparison value between the walking speeds in each of the morning and the afternoon indicated by the acquired walking speed information, and to estimate stress of the user based on the calculated comparison value, and an output unit configured to output information in accordance with the estimated stress of the user.

Abstract: A state estimation device includes a sensor information acquisition unit that acquires time-series sensor data, a state allocation unit that provisionally allocates a state label to sensor data, a first model learning unit that calculates a feature parameter of sensor data to which the same state label is allocated and performs learning of a state estimation model that classifies the sensor data at each time into any one of states, a second model learning unit that performs learning of a state transition model whose input is transition of state labels before a transition reference time and whose output is a state label at a time subsequent to the transition reference time, a state allocation update unit that updates a state label allocated to each time based on the state at each time estimated, and an output unit that outputs the state estimation model and the state transition model.

Abstract: An object is to provide an information processing device capable of reducing power consumption for acquiring sensor values and preventing a loss of an important sensor value. A power saving model server (100) is an information processing device for controlling a plurality of sensors (acceleration sensor 201, etc.) that respectively detect a plurality of terminal states. The power saving model server (100) includes a sensor control unit (104) that stops an operation related to one or a plurality of sensors among the plurality of sensors on the basis of the plurality of terminal states.

Abstract: A server includes a first statistic processor configured to calculate, for each of different types of screens displayed on a display; a first statistic value of display time of a screen of each type display on the display, to generate first statistic values corresponding to the different types of screens; a first determiner configured to determine, for each type of the screens, whether a user is concentrating on a screen of each type, based on, from among the first statistic values, a first statistic value calculated for the screen of each type; and a generator configured to generate a list including screen identification information for identifying, from among the different types of screens, a type of screen for which a corresponding determination result from the first determiner is affirmative.

Abstract: A profile generation device 10 includes the region profile estimation unit 103 configured to estimate a region profile indicating the features of a region related to a visit history of a user who has a user profile indicating the user's features on the basis of the visit history and the user profile and the user profile generation unit 107 configured to generate a user profile of a user who does not have a user profile on the basis of a visit history of the user and the region profile.

Abstract: The at-home prediction device is a device that makes a prediction relating to a resident in a household being at home, and the at-home prediction device includes: a household information acquiring unit configured to acquire household information according to the number of households of each type in an area in which the household are located; a prediction information acquiring unit configured to acquire prediction information of a plurality of types other than the household information in the area that is used for the prediction; a dimension compressing unit configured to perform dimension compression on the prediction information together with the household information for each type of the prediction information; a prediction unit configured to make a prediction relating to the resident in the household being at home on the basis of information that is compressed in dimensions; and an output unit configured to output information representing a prediction result.

Abstract: A visiting destination prediction device includes: a user position information acquiring unit configured to acquire user position information indicating a position of a user; an action history acquiring unit configured to acquire information on an action history of the user; and a visiting POI estimating unit configured to estimate a visiting POI which is a visiting destination of the user on the basis of a relationship between an acquired user position and a previously stored POI position and a relationship between the acquired action history of the user in a predetermined period and previously stored POI relevant information.