Abstract: A terminal apparatus includes a camera, a display that displays a display screen including a mobile robot that autonomously travels, and a control circuit. The control circuit acquires a first planned route of the mobile robot, displays, on the display, a screen having the first planned route superimposed on a camera image taken by the camera, detects a contact point on the display on which the screen is displayed, generates a second planned route of the mobile robot that travels through the contact point, and transmits the second planned route to the mobile robot.

Abstract: A terminal apparatus includes a camera, a display that displays a display screen including a mobile robot that autonomously travels, and a control circuit. The control circuit acquires a first planned route of the mobile robot, displays, on the display, a screen having the first planned route superimposed on a camera image taken by the camera, detects a contact point on the display on which the screen is displayed, generates a second planned route of the mobile robot that travels through the contact point, and transmits the second planned route to the mobile robot.

Abstract: An electrocardiogram analysis apparatus includes a machine learning part that has a machine learning model realized by machine learning that uses training electrocardiogram data of a patient with paroxysmal arrhythmia during a non-paroxysmal period during which no episode of paroxysmal arrhythmia occurs; an input processing part that inputs electrocardiogram data of a person to be analyzed, which is a subject of analysis, into the machine learning model; and an output control part that outputs, to an information terminal, abnormality information which is to be output from the machine learning model and is about whether the person to be analyzed has paroxysmal arrhythmia.

Abstract: An electrocardiogram display apparatus includes an input processing part that inputs divided electrocardiogram data, which is obtained by dividing whole electrocardiogram data of a patient that has been measured over a predetermined time period into electrocardiogram data having a predetermined time length shorter than the predetermined time period, into a machine learning model realized by machine learning that uses a plurality of pieces of training electrocardiogram data each having the predetermined time length; a result acquisition part that acquires, from the machine learning model, a determination result indicating whether or not the divided electrocardiogram data includes a waveform portion suspected of indicating heart disease; and a display controlling part that causes a display apparatus to display information based on the determination result.

Abstract: In a driving data analyzer, a data collector collects, from at least one vehicle, driving data sequences while each of the driving data sequences is correlated with identification data. Each driving data sequence includes sequential driving data items, and each driving data item represents at least one of a driver's operation of at least one vehicle and a behavior of the at least one vehicle based on the at least one of a driver's operation. The identification data represents a type of at least one external factor that contributes to variations in the driving data items. A feature extractor applies a data compression network model to the driving data sequences to thereby extract, from the driving data sequences, at least one latent feature independently from the type of the at least one external factor.

Abstract: A terminal apparatus includes a camera, a display that displays a display screen including a mobile robot that autonomously travels, and a control circuit. The control circuit acquires a first planned route of the mobile robot, displays, on the display, a screen having the first planned route superimposed on a camera image taken by the camera, detects a contact point on the display on which the screen is displayed, generates a second planned route of the mobile robot that travels through the contact point, and transmits the second planned route to the mobile robot.

Abstract: In a driving data analyzer, a data collector collects, from at least one vehicle, driving data sequences while each of the driving data sequences is correlated with identification data. Each driving data sequence includes sequential driving data items, and each driving data item represents at least one of a driver's operation of at least one vehicle and a behavior of the at least one vehicle based on the at least one of a driver's operation. The identification data represents a type of at least one external factor that contributes to variations in the driving data items. A feature extractor applies a data compression network model to the driving data sequences to thereby extract, from the driving data sequences, at least one latent feature independently from the type of the at least one external factor.

Abstract: In a system, a behavior description generator extracts a feature portion from at least one of segmented behavior-data sequences corresponding to at least one target driving situation included in sequential driving situations, and generates behavior-description information indicative of a description of a driver's behavior associated with the at least one target driving situation. An event description generator sequentially detects traffic events in at least one environmental data sequence indicative of time-series driving environments of a vehicle. The event description generator generates event-description information including a description indicative of each of the detected traffic events. A context description generator extracts, from the sequentially detected traffic events, one target traffic event that is detected immediately adjacent to the feature portion.

Abstract: In a system, a behavior description generator extracts a feature portion from at least one of segmented behavior-data sequences corresponding to at least one target driving situation included in sequential driving situations, and generates behavior-description information indicative of a description of a driver's behavior associated with the at least one target driving situation. An event description generator sequentially detects traffic events in at least one environmental data sequence indicative of time-series driving environments of a vehicle. The event description generator generates event-description information including a description indicative of each of the detected traffic events. A context description generator extracts, from the sequentially detected traffic events, one target traffic event that is detected immediately adjacent to the feature portion.