Abstract: A system for generating guidance information for a driver includes a processor and a storage device. The storage device stores guidance history of the driver. The processor generates a feature value based on match/mismatch of guidance types abstracted from the guidance history. The processor determines a guidance type for the driver on the basis of the feature value. The processor generates information for issuing guidance of the determined guidance type to the driver according to driver monitoring information.

Abstract: A computer generates an accident risk definition model to estimate a probability of hazard occurrence as an accident risk by inputting first in-vehicle sensor data collected in the past and hazard occurrence data having information on hazard occurrence from the first in-vehicle sensor data preset therein, generates accident risk estimation data by inputting second in-vehicle sensor data collected in the past to the accident risk definition model and estimating the probability of the hazard occurrence, generates an accident risk prediction model to predict the accident risk after a predetermined time by inputting first biological index data corresponding to the second in-vehicle sensor data and the accident risk estimation data, calculates second biological index data from second biological sensor data by acquiring the second biological sensor data of a driver, and predicts the accident risk after the predetermined time by inputting second biological index data to the accident risk prediction model.

Abstract: A system for generating guidance information for a driver includes a processor and a storage device. The storage device stores guidance history of the driver. The processor generates a feature value based on match/mismatch of guidance types abstracted from the guidance history. The processor determines a guidance type for the driver on the basis of the feature value. The processor generates information for issuing guidance of the determined guidance type to the driver according to driver monitoring information.

Abstract: A driving assistance system includes a processor, a storage device, and an output device. The storage device retains traveling environment information indicating a traveling environment of a vehicle and driving characteristic information indicating driving characteristics of a driver of the vehicle. The processor generates a message corresponding to the traveling environment of the vehicle based on the traveling environment information, and generates a message corresponding to the driving characteristics of the driver of the vehicle based on the driving characteristic information. The output device outputs the message corresponding to the traveling environment and the message corresponding to the driving characteristics in different aspects.

Abstract: A logistics prediction system includes an arithmetic device that executes a predetermined process, a storage device connected to the arithmetic device, a shipment information input section to which past shipment information is input, a constraint input section to which a calculation resource and a calculation time period are input as constraints for prediction, an integration prediction database holding shipment trends of products, a prediction method selector that selects a prediction method to be used to predict a shipment volume for each of the products from among multiple prediction methods so that the constraints input to the constraint input section are satisfied, multiple prediction method sections that predict shipment volumes using the prediction methods, an integrator that integrates the results of the prediction by the prediction method sections for each of the products, and a shipment prediction output section that outputs the result of the integration by the integrator.

Abstract: A logistics prediction system includes an arithmetic device that executes a predetermined process, a storage device connected to the arithmetic device, a shipment information input section to which past shipment information is input, a constraint input section to which a calculation resource and a calculation time period are input as constraints for prediction, an integration prediction database holding shipment trends of products, a prediction method selector that selects a prediction method to be used to predict a shipment volume for each of the products from among multiple prediction methods so that the constraints input to the constraint input section are satisfied, multiple prediction method sections that predict shipment volumes using the prediction methods, an integrator that integrates the results of the prediction by the prediction method sections for each of the products, and a shipment prediction output section that outputs the result of the integration by the integrator.

Abstract: An inventory management apparatus including a processor and a memory. The processor stores shipping record data that stores the actual results of the shipment amount of the article, possible variation factor data in which variation factors affecting the shipment amount are set in advance, and a user forecasting model from the selected variation factor and shipment record data. The processor selects a variation factor from the possible variation factor data and generates a recommended forecasting model from the user forecasting model and the shipment result data. The processor calculates the forecasted value of the shipping amount of the articles from the user forecasting model as the first forecasted value and calculates the forecasted value of the shipping amount of the articles from the recommended forecasting model as the second forecasted value, and calculates the degree of effect on the first and second forecasted shipping amount for each variation factors.