Abstract: A time-series data processing apparatus of the present invention includes: an extracting unit that extracts a feature value of each of a plurality of image regions within an image at each time of day when a target is captured; a generating unit that generates time-series data in which the feature value of each of the plurality of image regions and a measured value measured from the target using a measurement device are parameters; and a detecting unit that detects a state of the target based on the time-series data.

Abstract: A generation unit that generates a new abstraction log based on the distribution of log values corresponding to a variable in an abstraction log is included. The abstraction log includes at least one or more variables. The variable includes at least one or more log values. The new abstraction log is obtained by replacing the variable in the abstraction log with the log value.

Abstract: A prediction device f includes an acquisition unit and a prediction unit. The acquisition unit is configured to acquire attribute data pertaining to the plurality of exhibits and the number of visitors to each of the plurality of exhibits during a first period in the display area in which the plurality of exhibition articles are exhibited. The prediction unit is configured to predict a future flow of persons to the plurality of exhibits by a prediction model. The prediction model is generated using attribute data for a second period that is a period previous to the first period, graph time-series data pertaining to a change over time in a movement pattern for each of the plurality of visitors to the plurality of exhibits during the second period, the number of visitors to the exhibits, and the number of visitors to each of the plurality of exhibits.

Abstract: A time-series data processing apparatus according to the present invention includes: a generating unit configured to generate a generator having learned so as to generate state information representing a state of time-series data having a predetermined time width in accordance with a label given to the time-series data; a state information generating unit configured to generate, by using the generator, state information representing a state of division time-series data obtained by dividing the time-series data by a shorter time width than the predetermined time width; and a classifying unit configured to classify a plurality of division time-series data based on state information of the plurality of division time-series data.

Abstract: An analysis apparatus includes: an anomaly classifying unit that classifies the type of an anomaly having occurred based on time-series sensing data in occurrence of anomaly received from a plurality of sensors and a learned model learned in advance; and an identifying unit that identifies a sensor having detected information corresponding to the cause of the anomaly classified by the anomaly classifying unit based on information corresponding to the sensing data and information corresponding to past data that is past sensing data stored in advance.

Abstract: The present invention provides an anomaly detection method, an anomaly detection system, and an anomaly detection program that can detect an anomaly at high accuracy by using log output quantity distributions generated for to different aggregate units and different devices. An anomaly detection system according to one example embodiment of the present invention has: a reference distribution, which is a time-series distribution of a log output quantity acquisition unit that acquires a plurality of distributions generated for each device that outputs logs and for each unit of a time range in which logs are aggregated; and an anomaly detection unit that detects an anomaly by using the plurality of distributions.

Abstract: A sales assistance system comprises an acquisition unit and a prediction unit. The acquisition unit is configured to acquire attribute data on each of target customers as candidates for sales targets, and time-series data on actions included in sales activities that have been directed to a plurality of target customers until a predetermined time point. The prediction unit is configured to use a prediction model and the attribute data on the plurality of target customers and the time-series data acquired from the acquisition unit, and predicts recommended products for the target customers, and customers who are likely to purchase the recommended products among the target customers. The prediction model is generated based on the attribute data on each of a plurality of existing customers, time-series data on a plurality of actions included in sales activities, and product data about purchased products through the sales activities.

Abstract: A medical planning assistance system provides assistance to a medical worker in performing an appropriate medical practice according to the condition of a patient by being provided with: an acquisition unit that acquires patient condition information about the condition of a first patient; an inference model that is a model obtained by learning of a relation among a condition of a second patient, a medical practice performed on the second patient, and a condition change of the second patient due to the medical practice performed on the second patient; an inference unit that infers recommended medical practice to be recommended for the first patient, on the basis of the inference model and the patient condition information acquired by the acquisition unit; and an output unit that outputs the recommended medical practice inferred by the inference unit.

Abstract: An article recommendation system 30 is provided with: an estimation model 31, which expresses a relationship between purchase behavior information 310 pertaining to a subject customer during a first period (expressing a time-series change in article purchase activity on the part of the subject customer) and subject customer attribute information 314 (expressing a time-series change in an attribute of the subject customer), and an article purchase record 315 for the subject customer after the first period; and an estimation unit 32 which, on the basis of purchase behavior information 300 and subject customer attribute information 304 during a second period after the first period, estimates a recommended article for the subject customer after the second period. Thus, it is possible to improve the precision with which an article to be recommended to a subject customer is estimated.

Abstract: This plant control support system is provided with an acquisition unit, a generation unit, and an output unit, in order to provide a technique for appropriately supporting plant control without relying on the know-how and knowledge of experts and manuals. The acquisition unit acquires state information regarding the state of a plant. The generation unit uses a control model, and on the basis of the state information, generates control contents according to the state of the plant as proposed control contents. The control model is a model obtained by training the relationship between the control contents for controlling the plant and the control result obtained by executing the control of the control contents on the plant. The output unit outputs the proposed control contents.

Abstract: A prediction device f includes an acquisition unit and a prediction unit. The acquisition unit is configured to acquire attribute data pertaining to the plurality of exhibits and the number of visitors to each of the plurality of exhibits during a first period in the display area in which the plurality of exhibition articles are exhibited. The prediction unit is configured to predict a future flow of persons to the plurality of exhibits by a prediction model. The prediction model is generated using attribute data for a second period that is a period previous to the first period, graph time-series data pertaining to a change over time in a movement pattern for each of the plurality of visitors to the plurality of exhibits during the second period, the number of visitors to the exhibits, and the number of visitors to each of the plurality of exhibits.

Abstract: A growth potential estimation system 30 is provided with: an estimation model 31 that represents a relationship between transaction information 310 (representing a time-series change of company-to-company transaction relations of an intended company), account time-series information 313 (representing a time-series change of deposits and withdrawals of accounts of the intended company), and intended company attribute information 314 (representing a time-series change of the attribute of the intended company) of the intended company for a first period and the growth potential 315 of the intended company after the first period; and an estimation unit 32 for estimating the growth potential of the intended company after a second period on the basis of transaction information 300, account time-series information 303, and company attribute information 304 for a second period that is later than the first period.

Abstract: An abnormal access prediction system is configured to comprise an acquisition unit and a prediction unit. The acquisition unit acquires time-series access data and time-series resource usage data in a first period. The time-series access data is data relating to access to a server on a network from a first plurality of terminal devices individually operated by a first plurality of users. The time-series resource usage data is data relating to a time-series change in resource usage of each of the first plurality of terminal devices. The prediction unit predicts a terminal device that performs abnormal access by using: a prediction model generated on the basis of time-series access data and time-series resource usage data in a second period earlier than the first period; time-series access data in the first period; and time-series resource usage data.

Abstract: An SNS analysis system 30 improves the precision of estimating the existence of an unknown relationship among multiple persons in an SNS, wherein the improvement being achieved by comprising an estimation unit 32 that estimates the existence of an unknown relationship among a second plurality of persons on the basis of: an estimation model 31 that represents a relationship between communication history information 310 related to a first plurality of persons (which represents time-series variations in at least either giving and receiving of information via the SNS among persons or persons' transmitting of information related to each other via the SNS) as well as attribute information 313 (which represents time-series variations in attributes of persons) and presence-or-absence 314 of a relationship existing among the first plurality of persons; and communication history information 300 as well as attribute information 303 both related to the second plurality of persons.

Abstract: A sales assistance system is configured to comprise an acquisition unit and a prediction unit. The acquisition unit acquires: sales process time series data indicating the time series sequence of a plurality of actions included in sales activity for a target customer at a first time point; and customer attribute data of the target customer. The prediction unit predicts a sales activity action for the target customer subsequent to the first time point, and a probability of success of the sales activity for the target customer if an action is taken based on the sales process time-series data and the customer attribute data by using a prediction model.

Abstract: A crime investigation assisting system 30 is provided with an estimation unit 32 for estimating the type of a second case on the basis of: an estimation model 31 representing a relationship between action history information 300 (representing a time-series change of action of related persons) and human relation information 303 (representing a time-series change of human relation of related persons) pertaining to a first case and a type 314 of the first case; and action history information 300 and human relation information 303 pertaining to the second case.

Abstract: A time-series data processing apparatus according to the present invention includes: a generating unit configured to generate a generator having learned so as to generate state information representing a state of time-series data having a predetermined time width in accordance with a label given to the time-series data; a state information generating unit configured to generate, by using the generator, state information representing a state of division time-series data obtained by dividing the time-series data by a shorter time width than the predetermined time width; and a classifying unit configured to classify a plurality of division time-series data based on state information of the plurality of division time-series data.

Abstract: The present invention provides an anomaly detection method, an anomaly detection system, and an anomaly detection program that can detect an anomaly at high accuracy by using log output quantity distributions generated for to different aggregate units and different devices. An anomaly detection system according to one example embodiment of the present invention has: a reference distribution, which is a time-series distribution of a log output quantity acquisition unit that acquires a plurality of distributions generated for each device that outputs logs and for each unit of a time range in which logs are aggregated; and an anomaly detection unit that detects an anomaly by using the plurality of distributions.

Abstract: A time-series data processing device 100 includes an analysis unit 121 that sets, on the basis of an analysis result with respect to first time-series data, a given section of the first time-series data, and an output unit 122 that, on the basis of the first time-series data included in the set section, controls output of information based on an analysis result with respect to second time-series data.

Abstract: The present invention provides an anomaly detection method, an anomaly detection system, and an anomaly detection program that can detect an anomaly at high accuracy by using log output quantity distributions generated for to different aggregate units and different devices. An anomaly detection system according to one example embodiment of the present invention has: a reference distribution, which is a time-series distribution of a log output quantity acquisition unit that acquires a plurality of distributions generated for each device that outputs logs and for each unit of a time range in which logs are aggregated; and an anomaly detection unit that detects an anomaly by using the plurality of distributions.