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: 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: An advertising effect prediction device is a device that predicts an advertising effect of advertising content, and includes: an acquisition unit that acquires advertisement information related to the advertising content; and a calculation unit that calculates the advertising effect based on the advertisement information. The advertisement information includes a plurality of images included in the advertising content, and the calculation unit calculates the advertising effect based on an arrangement order of the plurality of images in the advertising content.

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: 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 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: A recommendation system includes an acquisition unit configured to acquire order history information indicating an order history in the middle of a series of orders placed by a user, and a determination unit configured to predict a value related to a total amount of the series of orders placed by the user on the basis of the order history information acquired by the acquisition unit and determine an item to be recommended to the user on the basis of the order history information and the predicted value.

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.

Abstract: A recommendation device includes: a determination unit that determines a combination of insurance products and insurance premiums to be paid to the insurance products from among a plurality of insurance products based on a risk degree indicating a degree of damage caused to a user due to an event to be compensated for by each of the plurality of insurance products and a compensation degree indicating a degree of compensation by each of the plurality of insurance products; and an output unit that outputs recommendation information indicating the combination and the insurance premiums.

Abstract: A configuration of a time and space-resolved infrared spectroscopic analysis which can be integrated onto a chip is provided. An infrared analysis chip includes a substrate in which a microchannel is formed, at least one of a spectroscope and a photodetector integrated onto a first surface of the substrate in an area where the microchannel is formed, and an infrared light source integrated on a second surface opposite to the first surface of the substrate, the infrared light source being positioned facing said at least one of the photodetector and the spectroscope.

Abstract: A recommendation system is a system that performs recommendation for a user over a plurality of different types of services and includes: an acquisition unit configured to acquire use history information indicating use histories of a user in a time series in the plurality of different types of services; a feature quantity converting unit configured to convert a continuous use history in which use of another service has not intervened and that is temporally continuous for the same service out of use histories indicated by the use history information acquired by the acquisition unit to a feature quantity with a fixed length using the continuous use history as a unit; and a determination unit configured to determine information to be recommended for the user based on the feature quantity with the fixed length acquired through conversion by the feature quantity converting unit.

Abstract: A POI popularity derivation device (10) includes: a dictionary generation unit (11) that assigns a feature word used as a co-occurrence word of a POI name to each popularity-assigned POI name serving as a popularity assignment target to generate a popularity-assigned POI dictionary in which a popularity-assigned POI name and a feature word are associated with each other; an extraction unit (12) that extracts posted data serving as a search target from posted data on the basis of predetermined criteria; and a popularity derivation unit (18) that searches for the posted data on the basis of a predetermined rule regarding feature words while referring to the popularity-assigned POI dictionary, to extract posted data linked to the popularity-assigned POI name, and derives the popularity of each popularity-assigned POI name on the basis of the number of pieces of extracted posted data for each popularity-assigned POI name.

Abstract: A recommendation information provision device includes at least one processor that is configured to acquire a result of scoring relating to a user's singing of a musical piece for each section in time; acquire pitch information representing pitches of notes configuring the musical piece in the section; build a learning model predicting a result of scoring relating to the user's singing of a musical piece from the pitch information using the result of scoring and the pitch information as training data; acquire a result of scoring relating to a user's singing of a target musical piece by inputting the pitch information to the learning model while changing pitches of notes into a plurality of types; and output a setting detail of the pitches of the notes as the recommendation information on the basis of results of scoring for the pitch information of the plurality of types.

Abstract: A learning apparatus 10 of one embodiment includes an acquisition unit 11 for acquiring action history data indicating action history for each of a plurality of users, and a learning unit 13 for learning parameter groups PC, C included in a predictive model M for predicting an action of each of the plurality of users by using the action history data as training data. The parameter group PC is a parameter group related to a membership rate of each user for each of a plurality of clusters. The parameter group C is a parameter group related to an action tendency of each cluster for each of a plurality of actions.

Abstract: An object is to improve accuracy of prediction of a result of scoring relating to user's singing of a new musical piece. A prediction device is a device that acquires a result of scoring relating to a user's singing of a musical piece in the past for each section of the musical piece in time, acquires pitch information representing pitches of notes configuring the musical piece and aligned in a time series in the section, builds a learning model predicting a result of scoring relating to the user's singing of a musical piece from the pitch information using the result of scoring and the pitch information as training data, and acquires a result of scoring relating to the user's singing of a new musical piece on the basis of an output of the learning model by inputting the pitch information about the new musical piece to the learning model.

Abstract: A population extraction device includes: a time-series population acquisition unit acquiring demographic data in daily time slots over a time period in a target area and acquiring a time-series population in a time slot each day by extracting a population at the same time slots from the demographic data in daily time slots; a clustering unit clustering the time-series population in a time slot each day into a plurality of classes based on fluctuation-similarity; a determination unit determining a class on a no-event day based on fluctuation in each class; a stationary population derivation unit deriving a time-series average population of the class on a no-event day as a time-series stationary population of a target area; and a population extraction unit extracting a difference between the time-series population on a target day in a target area and the time-series stationary population as an event-related population of the target area.

Abstract: A configuration of a time and space-resolved infrared spectroscopic analysis which can be integrated onto a chip is provided. An infrared analysis system includes a light source having a nanocarbon material as a luminescent material, and a photodetector configured to detect an infrared light emitted from the light source and transmitted through or reflected from a sample, wherein the nanocarbon material is provided on a surface of a substrate and configured to output a surface-emitted light.

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; a stationary position deriving unit configured to derive a stationary position of the user on the basis of the acquired user position information; 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 the derived stationary position of the user and a predetermined POI position of a POI which is able to be the visiting destination of the user. The stationary position deriving unit derives the stationary position which is expressed by an area on the basis of an error and a degree of dispersion of a series of pieces of the acquired user position information.

Abstract: A system of generating a model for estimation is a system that generates the model for estimation used for estimation based on information about musical pieces, the system including: a learning data acquiring unit configured to acquire learning information in which information about audio data of a plurality of musical pieces having sequences is aligned in a dimension of the sequences in order of the sequences that is learning data used for machine learning; and a model generating unit configured to generate the model for estimation by performing machine learning using the learning information as an input to the model for estimation performing convolution using a kernel in the dimension of the sequences.

Abstract: A determination device 1 includes: a history storage unit 101 configured to accumulate and store use history information in which application identification information, position information indicating a position of a user, and user identification information are correlated with each other; a use region UU number totaling unit 105 configured to total a UU number for each of a plurality of regions on the basis of the use history information for a specific application; a use region number totaling unit 106 configured to total the number of regions on the basis of the use history information for the specific application; a score calculating unit 107 configured to calculate a regionality score for each of the plurality of regions on the basis of the UU number and the number of regions for the specific application; and a determination unit 108 configured to determine whether there is regionality for the specific application on the basis of the regionality score.