Abstract: A robot control system includes a state candidate generation unit that generates a state candidate that is a state transition destination of a robot at next time, a control amount estimation unit that estimates a control amount for transitioning to the state candidate, a state candidate evaluation unit that calculates a distance between the target state of the robot and the state candidate, calculates a coincidence degree between (i) a state at next time estimated from a state at current time of the robot and the control amount and (ii) the state candidate, and sets a sum of the distance and the coincidence degree to be an evaluation value, and a selection unit that selects a state candidate with a minimum evaluation value from state candidates and generate a motion corresponding to the selected state candidate.

Abstract: To provide a work sequence capable of suppressing reduction of an evaluation value within an allowable range. A work sequence generation apparatus including a processor that executes a program and a storage device that stores therein the program, and generating a work sequence specifying an order of working on a processing object group performs a perturbation process of generating a second work sequence by perturbating a first work sequence, and a learning process of generating a learning model for generating a specific work sequence so that a difference from an evaluation value regarding a work in an input work sequence is within an allowable range by learning that a difference between a first evaluation value regarding a work in the first work sequence and a second evaluation value regarding a work in a second work sequence generated in the perturbation process should be within the allowable range.

Abstract: An error in a demand prediction of items is reduced. A data processing apparatus for processing data on a demand for multiple items includes a model learning unit that generates a prediction model for predicting the demand for the multiple items, and a demand prediction unit that predicts the demand for the multiple items by using the prediction model. The model learning unit inputs actual data of the demand for each item to a first neural network of each item, extracts a feature quantity of each item, and combines the feature quantity of each item to generate a second neural network that is a prediction model.

Abstract: In view of the relevant background art, the present invention is to provide a system or apparatus which enables data in use for learning to be sorted out, thereby reducing the amount of data to be handled. Presenting one example of the control system according to the present invention, the control system including a first processing device to generate a learnt model; and a second processing device to operate employing the generated learnt model and provided with input means, wherein the second processing device transmits information input from the input means to the first processing device; and where the input information as transmitted is information belonging to an area different from an area designated with the learnt model, the first processing device generates an updated learnt model based on the information belonging to the different area as received and the learnt model.

Abstract: A computer system is coupled to a first database and a second database, the first database storing model information defining a model, the second database storing history data indicating a plan developed in a past. The computer system generates, in a case of receiving an evaluation query including evaluation plan data that indicates an evaluation target plan, a plurality of pieces of the pair data each of which is a pair of the evaluation plan data and the history data; obtains a plurality of prediction results by inputting the plurality of pieces of the pair data to the model; select a piece of the history data that has an evaluation index small in difference from the evaluation index of the evaluation target plan based on the plurality of prediction results; and output the selected piece of the history data.

Abstract: To provide an information processing device capable of more efficiently searching for parameters. An optimization portion selects a search area, namely, any of the multiple subspaces contained in a parameter space composed of multiple parameters based on evaluation result information indicating an evaluation value that evaluates a processing result of information processing based on each trial parameter by using the trial parameter. The optimization portion allows a classification model portion to perform information processing using the trial parameter, namely, any one of parameters belonging to the search area. The optimization portion repeats a search process to update the evaluation result information based on the processing result of the information processing.

Abstract: A robot control system includes a state candidate generation unit that generates a state candidate that is a state transition destination of a robot at next time, a control amount estimation unit that estimates a control amount for transitioning to the state candidate, a state candidate evaluation unit that calculates a distance between the target state of the robot and the state candidate, calculates a coincidence degree between (i) a state at next time estimated from a state at current time of the robot and the control amount and (ii) the state candidate, and sets a sum of the distance and the coincidence degree to be an evaluation value, and a selection unit that selects a state candidate with a minimum evaluation value from state candidates and generate a motion corresponding to the selected state candidate.

Abstract: When multiple explanatory variables are automatically created, a huge amount of output suggestions causes a heavy burden on selecting the suggestion. A marketing support system is configured to include a suggestion extraction unit that accepts purchase data and analyzes a correlation between the purchase data to output a composite variable, a restriction filtering unit that accepts the composite variable and a restriction table to exclude the composite variable based on a restriction condition defined in the restriction table, and a result filtering unit that uses a measure result defined in the past to estimate an anticipated effect when a measure based on the composite variable is performed, and selects a plurality of explanatory variables.

Abstract: An error in a demand prediction of items is reduced. A data processing apparatus for processing data on a demand for multiple items includes a model learning unit that generates a prediction model for predicting the demand for the multiple items, and a demand prediction unit that predicts the demand for the multiple items by using the prediction model. The model learning unit inputs actual data of the demand for each item to a first neural network of each item, extracts a feature quantity of each item, and combines the feature quantity of each item to generate a second neural network that is a prediction model.

Abstract: In view of the relevant background art, the present invention is to provide a system or apparatus which enables data in use for learning to be sorted out, thereby reducing the amount of data to be handled. Presenting one example of the control system according to the present invention, the control system including a first processing device to generate a learnt model; and a second processing device to operate employing the generated learnt model and provided with input means, wherein the second processing device transmits information input from the input means to the first processing device; and where the input information as transmitted is information belonging to an area different from an area designated with the learnt model, the first processing device generates an updated learnt model based on the information belonging to the different area as received and the learnt model.

Abstract: When multiple explanatory variables are automatically created, a huge amount of output suggestions causes a heavy burden on selecting the suggestion. A marketing support system is configured to include a suggestion extraction unit that accepts purchase data and analyzes a correlation between the purchase data to output a composite variable, a restriction filtering unit that accepts the composite variable and a restriction table to exclude the composite variable based on a restriction condition defined in the restriction table, and a result filtering unit that uses a measure result defined in the past to estimate an anticipated effect when a measure based on the composite variable is performed, and selects a plurality of explanatory variables.

Abstract: For the purpose of reducing the processing amount in evaluating an effect of a measure with a KPI, a measure evaluation system is configured to have: a basic indicator calculation module grouping data into a subject and a non-subject and calculating basic indicators of the subject and the non-subject before measure execution; a basic indicator estimation module creating, for each basic indicator, an estimation model representing a relationship between the basic indicators of the subject and the non-subject and estimating an estimated basic indicator, which is exhibited when the measure is not executed on the subject, based on a basic indicator of the subject after measure execution and on the estimation model; and a KPI evaluation calculation module receiving a KPI definition formed of arithmetic calculation of basic indicators and calculating an estimated KPI value corresponding to the KPI definition with the KPI definition and the estimated basic indicator.

Abstract: A verification assistance system comprises: a first computation unit which, based on the name of a concept defined by an arbitrary word, searches a database to extract a plurality of behavior history data linked with the concept; a second computation unit which calculates the number of data items that are common among the plurality of extracted behavior history data to calculate the ratio by which a certain concept is linked with another concept; and a third computation unit which, based on the ratio, determines whether the certain concept and the other concept have an abstract relationship of any of upper/lower, identical, and no-relation.

Abstract: The present invention aims at providing a technique of helping to quantitatively evaluate a purchasing preference of customers, and design the purchasing preference high in the degree of matching with an actual merchandise purchasing history. The customer analysis system according to the present invention calculates the degree of matching indicative of how much a purchasing preference type of the customer matches a merchandise group in a correspondence relationship on the basis of a merchandise purchasing history of the customer, and evaluates the purchasing preference type on the basis of the calculation result (refer to FIG. 3).

Abstract: The present invention aims to extract change factors that raise and lower evaluation with respect to a product based on a purchase history of an individual. A preference analyzing system according to the present invention learns, from a purchase history, a preference model that evaluates purchase preference of an individual, calculates correlation between a feature quantity representing an attribute of a product and a mixed attribute that can raise and lower evaluation of the product, and extracts other product attributes that change the mixed attribute (see FIG. 6).

Abstract: Provided is a technology for extracting a user behavior pattern from history data accumulating personal behaviors, and enabling exhaustive and efficient analysis of a user behavior tendency or features from various aspects, such as location and time, using the pattern. A behavioral characteristics analysis device according to the present invention expresses a behavior pattern by scene vectors describing behaviors of a set of persons as scene values in each time band, extracts a life pattern included in all of the set of persons by clustering the scene vectors, and performs classification based on to which life pattern each person belongs (see FIG. 1).

Abstract: A data item name constituting a source data schema is extracted (1051), a data item name constituting a target data schema is extracted (1052), element concepts included in the extracted data item names of the source data schema and the target data schema are extracted (1053), a distinctive relation between the data item names is set based on the extracted element concepts to be stored in an ontology (1061), the data item names of the source data schema and the target data schema are read, a correlation is set between the two read data item names based on the distinctive relation to be stored in a mapping definition file (1071).

Abstract: A data item name constituting a source data schema is extracted (1051), a data item name constituting a target data schema is extracted (1052), element concepts included in the extracted data item names of the source data schema and the target data schema are extracted (1053), a distinctive relation between the data item names is set based on the extracted element concepts to be stored in an ontology (1061), the data item names of the source data schema and the target data schema are read, a correlation is set between the two read data item names based on the distinctive relation to be stored in a mapping definition file (1071).

Abstract: Aids in creating axes from the bottom up using a huge volume of document data and, during the process, aids the user to discover an analytical point of view. The following processing is performed: (1) the system extracts search formula candidates for categories (referred to as category candidates) and the user selects from among the extracted category candidates; (2) the system creates axes from the category candidates selected by the user; and (3) the user determines a name of each axis (i.e., name of analytical point of view). Of these steps, the system aids in the step (1).

Abstract: For each word occurring in Japanese text, a set of words co-occurring with it and their co-occurrence frequencies are extracted, where two words are regarded as co-occurring with each other when they occur in the same sentence. Likewise, for each word occurring in an English text that corresponds to the Japanese text, a set of words co-occurring with it and their co-occurrence frequencies are extracted. A correlation is calculated between a Japanese word and an English word based upon the co-occurrent word set of the Japanese word and that of the English word, with the assistance of a Japanese-English bilingual dictionary of basic words. The correlation is defined as the ratio of the number of possible correspondences between the two co-occurrent word sets to the total of the co-occurrence frequencies in the two co-occurrent word sets. Pairs of words having a mutually maximum correlation are selected as candidate translation pairs of words, and displayed on a display device.