Abstract: A wireless tag reading apparatus includes an antenna, a driving mechanism configured to move the antenna to multiple positions, and a controller configured to: upon receipt of a radio wave from a wireless tag via the antenna at each of the multiple positions, obtain tag information stored in the wireless tag and radio wave information related to the radio wave, determine whether the wireless tag is a reading target based on positional information indicating a position of the wireless tag that is output from a model in response to an input of the radio wave information, and issue a notification when a reliability level of the positional information output from the model is less than a predetermined threshold value.

Abstract: A wireless tag reading apparatus includes an antenna, a driving mechanism configured to move the antenna to multiple positions, and a controller configured to: upon receipt of a radio wave from a wireless tag via the antenna at each of the multiple positions, obtain tag information stored in the wireless tag and radio wave information related to the radio wave, adjust the radio wave information based on the tag information, and determine whether the wireless tag is a reading target based on information indicating a position of the wireless tag that is output from a model in response to an input of the adjusted radio wave information.

Abstract: A reading apparatus includes a mechanism to move an antenna between first and second positions, and a controller configured to: (a) control the antenna to transmit radio waves to wireless tags each attached to an object when the antenna is between the first and second positions; acquire, from response waves, information registered in each tag and indicating attributes of the object and the tag, and signal values of the response waves; and calculate an estimated position of each tag and a reliability level thereof based on the values. When the level of the position of a tag is less than a threshold or when the information of a tag indicates an attribute that can affect radio communications, the controller performs step (a) again, acquires signal values of response waves received from the tag, and calculates an estimated position of the tag and a reliability level thereof based on the values.

Abstract: An information processing apparatus includes an acquisition unit that acquires transaction information in which consumer identification information for identifying a consumer is associated with individual item identification information indicating an individual item of a commodity purchased by the consumer in a transaction; a management unit that manages, for an individual consumer, the transaction information acquired by the acquisition unit; a receipt information output unit that outputs, based on the transaction information managed by the management unit, receipt information presented as a receipt to the consumer; a reception unit that receives event information indicating contents of an event targeting a commodity; and a specifying unit that specifies the consumer identification information, corresponding to the individual item identification information of an individual item which is a target of the event information received by the reception unit, with reference to the transaction information managed by

Abstract: A wireless tag reading apparatus for reading tag information stored in a wireless tag attached to an item, includes a stage on which the item can be placed, one or more antennas movable between first and second positions, a camera facing the stage, and a controller configured to control the camera to capture images and detect an object therein, control the antennas to transmit radio waves to a wireless tag and move the antennas, wherein the transmitting and the moving are repeated while the antennas move from the first position to the second position during a tag reading period, acquire tag information from response waves transmitted by the wireless tag in response to the radio waves, based on the captured images, determine whether the object has moved during the tag reading period, and upon determining that the object has moved during the tag reading period, issue a notification.

Abstract: An embodiment of a data pattern analysis optimizer includes a time sequence data memory, an estimator, a grouping unit, and a time sequence pattern extractor. The time sequence data memory stores a plurality of time sequence data made from items in time order. The estimator estimates the upper limit of the total number of types of time sequence patterns present in the time sequence data at a rate higher than a minimum support level, based on a respective rate of presence of each item, wherein each of the time sequence patterns present in the time sequence data is a predefined number of items. In case that the estimated upper limit exceeds an upper limit of the number of types of time sequence patterns as a maximum processing load to a computer, the grouping unit groups a plurality of time sequence data into sub-groups, based on a group of items having the increased number of items and gives the estimator instructions to perform estimation.

Abstract: A sequential data analysis apparatus extracts a pattern of two or more sets of items based on an appearance frequency of each of different sets of items in first sequential data, selects a pattern of two or more sets of items based on an appearance frequency of a sub-pattern formed of a portion of the extracted pattern, creates a related pattern including the same last set of items as and the other sets of items different from the selected characteristic pattern, calculates an evaluation value of the related pattern, creates a prediction model by organizing data of the characteristic pattern and the related pattern, and applies second sequential data to the prediction model to determine a result which the second sequential data is likely to lead to.

Abstract: An evaluation device of an embodiment includes a storage, a data generator, a class definer, a characteristic data divider, and an evaluator. The data generator is configured to generate a set of characteristic data from both a set of first data and at least a set of second data, the at least set of second data being associated in time information with the set of first data, the set of characteristic data representing a plurality of characteristics. The characteristic data divider is configured to divide the plurality of sets of characteristic data into a plurality of groups on the basis of the plurality of classes defined by the class definer and condition of operations included in the set of first data. The evaluator is configured to evaluate a operating state using a first model defined for each of the plurality of groups divided by the characteristic data divider.

Abstract: A pattern finding device of embodiments includes a pattern generator, a pattern storage, a pattern selector, a pattern deleter. The pattern generator generates a group of patterns from data including a plurality of items. The pattern storage stores the group of patterns generated by the pattern generator. The pattern selector selects at least one pattern to be deleted from the group of patterns with reference to an evaluated value indicating a change of an item distribution condition in the group of patterns when at least one pattern which is selected as a candidate is excluded from the group of patterns stored in the pattern storage. The pattern deleter deletes the at least one pattern that is selected by the pattern selector from the pattern storage.

Abstract: An evaluation target of interest extraction apparatus of one embodiment extracts a plurality of items for each piece of text information. When the items include an item which matches an evaluation target expression or related expression, the apparatus allocates the evaluation target expression or that of the related expression to the text information as an evaluation target item. The apparatus distinguishes a class from a change in numeric information related to the evaluation target item, and allocates the class to the text information. The apparatus generates a class-attached transaction including the evaluation target item, the items, and the class. The apparatus discovers a pattern from a set of the transactions.

Abstract: A data analysis supporting apparatus according to an embodiment comprises a variable-type determining device, a variable-variation generating device, and a variable-contribution rate determining device. The variable-type determining device determines the type of each variable. The variable-variation generating device generates variations of each variable in accordance with the variable type determined and with a variable variation rule that defines a method of generating the variations of each variable of any type. The variable-contribution rate determining device calculates the rate at which the variations and analysis data contribute to the objective variable, and determines, from the rate, whether the variations should be used or deleted.

Abstract: A data analyzing apparatus of an embodiment generates a format variation, analytical algorithm name, and analytical parameter not stored in a first storage device, and executes analysis. The data analyzing apparatus determines whether the application accuracy is lower than the knowledge model accuracy. If the determination result is “not lower”, the data analyzing apparatus reactivates a format variation generating device and an analytical parameter generating device. If the determination result is “lower”, the data analyzing apparatus reads out a format variation and knowledge model name associated with the highest priority order in the first storage device, and executes analysis.

Abstract: Provided is a knowledge extracting apparatus for extracting knowledge information related to a knowledge-extraction target from an electronic document distributed continually in a state where the electronic document is not associated with the knowledge-information extraction target. A knowledge extracting apparatus according to one embodiment is a knowledge extracting apparatus including: an information receiving section for receiving an electronic document; a knowledge extracting section for extracting a concept from the electronic document based on a target word to extract knowledge information and a clue word to extract knowledge information and forming knowledge information in which the concept thus extracted and the target word are associated with each other; a storage section for storing the knowledge information thus extracted; and an information analysis section for, after the knowledge information is stored, analyzing the electronic document based on the knowledge information in the storage section.

Abstract: An evaluation device of an embodiment includes a storage, a data generator, a class definer, a characteristic data divider, and an evaluator. The data generator is configured to generate a set of characteristic data from both a set of first data and at least a set of second data, the at least set of second data being associated in time information with the set of first data, the set of characteristic data representing a plurality of characteristics. The characteristic data divider is configured to divide the plurality of sets of characteristic data into a plurality of groups on the basis of the plurality of classes defined by the class definer and condition of operations included in the set of first data. The evaluator is configured to evaluate a operating state using a first model defined for each of the plurality of groups divided by the characteristic data divider.

Abstract: A sequential data analysis apparatus extracts a pattern of two or more sets of items based on an appearance frequency of each of different sets of items in first sequential data, selects a pattern of two or more sets of items based on an appearance frequency of a sub-pattern formed of a portion of the extracted pattern, creates a related pattern including the same last set of items as and the other sets of items different from the selected characteristic pattern, calculates an evaluation value of the related pattern, creates a prediction model by organizing data of the characteristic pattern and the related pattern, and applies second sequential data to the prediction model to determine a result which the second sequential data is likely to lead to.

Abstract: A pattern extracting apparatus includes a first storing section storing plural target information pieces, a candidate pattern producing section producing candidate patterns each including two or more items different from each other based on each of the items included in the plural target information pieces, a candidate evaluation value calculating section calculating an extraction evaluation value of the candidate pattern based on a frequency of appearance at which the produced candidate pattern appears in the plural target information pieces, a pattern extracting section determining and extracting any of the candidate patterns having the calculated extraction evaluation value satisfying a predetermined threshold value, and a second storing section storing an association degree between the items.

Abstract: A pattern finding device of embodiments includes a pattern generator, a pattern storage, a pattern selector, a pattern deleter. The pattern generator generates a group of patterns from data including a plurality of items. The pattern storage stores the group of patterns generated by the pattern generator. The pattern selector selects at least one pattern to be deleted from the group of patterns with reference to an evaluated value indicating a change of an item distribution condition in the group of patterns when at least one pattern which is selected as a candidate is excluded from the group of patterns stored in the pattern storage. The pattern deleter deletes the at least one pattern that is selected by the pattern selector from the pattern storage.

Abstract: An embodiment of a data pattern analysis optimizer includes a time sequence data memory, an estimator, a grouping unit, and a time sequence pattern extractor. The time sequence data memory stores a plurality of time sequence data made from items in time order. The estimator estimates the upper limit of the total number of types of time sequence patterns present in the time sequence data at a rate higher than a minimum support level, based on a respective rate of presence of each item, wherein each of the time sequence patterns present in the time sequence data is a predefined number of items. In case that the estimated upper limit exceeds an upper limit of the number of types of time sequence patterns as a maximum processing load to a computer, the grouping unit groups a plurality of time sequence data into sub-groups, based on a group of items having the increased number of items and gives the estimator instructions to perform estimation.

Abstract: A data analyzing apparatus of an embodiment generates a format variation, analytical algorithm name, and analytical parameter not stored in a first storage device, and executes analysis. The data analyzing apparatus determines whether the application accuracy is lower than the knowledge model accuracy. If the determination result is “not lower”, the data analyzing apparatus reactivates a format variation generating device and an analytical parameter generating device. If the determination result is “lower”, the data analyzing apparatus reads out a format variation and knowledge model name associated with the highest priority order in the first storage device, and executes analysis.

Abstract: A data analysis supporting apparatus according to an embodiment comprises a variable-type determining device, a variable-variation generating device, and a variable-contribution rate determining device. The variable-type determining device determines the type of each variable. The variable-variation generating device generates variations of each variable in accordance with the variable type determined and with a variable variation rule that defines a method of generating the variations of each variable of any type. The variable-contribution rate determining device calculates the rate at which the variations and analysis data contribute to the objective variable, and determines, from the rate, whether the variations should be used or deleted.