Provided is an information processing apparatus including: a metadata generation unit (210) that generates metadata from information indicating a state of an occupant riding in a mobile body, the information being obtained from a sensor provided in the mobile body; a first encryption unit (214) that encrypts the generated metadata; and a first recording unit (218) that stores the encrypted metadata.

A smart mirror can show live or recorded streaming video of an instructor performing a workout in a package that is attractive and unobtrusive enough to hang in a living room. The smart mirror includes a mirror surface with a fully reflecting section and a partially reflecting section. A display behind the partially reflecting section shows the video when the smart mirror is on and is almost invisible when the smart mirror is off. The smart mirror also has a speaker, a microphone, and a camera to enable a user to view the video content and interact with the instructor. The smart mirror may connect to the user's smart phone, a peripheral device (e.g., a Bluetooth speaker) to augment user experience, a biometric sensor to provide biometric data to assess user performance, and/or a network router to connect the smart mirror to a content provider, an instructor, and/or other users.

The present disclosure relates to an intelligent internet of things (IoT) monitoring system, and in particular to techniques (e.g., systems, methods, computer program products storing code or instructions executable by one or more processors) for the implementation of an IoT solution to manage urinary incontinence. Some aspects are directed to the concept of a management platform that allows for end users such as health care providers, caretakers, or medical personnel to manage and monitor one or more subjects through one or more client devices using a network of sensors and IoT devices. Other aspects are directed the concept of a data analysis system configured to train and deploy one or more prediction models for analysis and tracking metrics of health or wellbeing for the one or more subjects.

The present disclosure relates to hiding data trends in a machine-to-machine (M2M) system. A method for operating a first device in a machine-to-machine (M2M) system may include: performing a registration procedure for a second device; receiving real data generated by the second device; obtaining fake data of an identical type as the real data; and storing the real data and the fake data in a same resource.

The present disclosure relates to hiding data trends in a machine-to-machine (M2M) system. A method for operating a first device in a machine-to-machine (M2M) system may include: performing a registration procedure for a second device; receiving real data generated by the second device; obtaining fake data of an identical type as the real data; and storing the real data and the fake data in a same resource.

Provided is a system for providing a service to a user using an IoT device. A system for providing a service to a user using an IoT device executes an application or software for executing a function of an IoT device on a cloud server, and the cloud server controls individual IoT devices. A system for providing a service to a user using an IoT device may be related to an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, and devices related to a 5G service.

Provided is a system for providing a service to a user using an IoT device. A system for providing a service to a user using an IoT device executes an application or software for executing a function of an IoT device on a cloud server, and the cloud server controls individual IoT devices. A system for providing a service to a user using an IoT device may be related to an artificial intelligence module, a robot, an augmented reality (AR) device, a virtual reality (VR) device, and devices related to a 5G service.

A data processing method, a detection device administration server, a user terminal, a network server, and a data processing system are provided. The data processing method is applicable to a detection device administration server and includes: communicating with a first detection device; sending, in response to a user terminal of a user binding the first detection device through first identification information of the first detection device, a first detection request to the first detection device; and receiving, after the user completes the first detection, first detection data sent by the first detection device.

A smart mirror can show live or recorded streaming video of an instructor performing a workout in a package that is attractive and unobtrusive enough to hang in a living room. The smart mirror includes a mirror surface with a fully reflecting section and a partially reflecting section. A display behind the partially reflecting section shows the video when the smart mirror is on and is almost invisible when the smart mirror is off. The smart mirror also has a speaker, a microphone, and a camera to enable a user to view the video content and interact with the instructor. The smart mirror may connect to the user's smart phone, a peripheral device (e.g., a Bluetooth speaker) to augment user experience, a biometric sensor to provide biometric data to assess user performance, and/or a network router to connect the smart mirror to a content provider, an instructor, and/or other users.

Disclosed are a combined-learning-based Internet of Things data service method and apparatus, a device and a medium. The method includes: acquiring a data processing result of an edge side for target user data; performing combined learning training based on a combined learning engine, the data processing result and the target user data, to obtain a combined learning training model; storing the combined learning training model in a target model base; and calling a service-side requirement by using the target model base. According to the present disclosure, target user data is processed, and then combined learning training is performed by using obtained data processing results, so that a combined learning training model meeting a user management and calling requirement can be obtained. Users' requirements for model training and calling are met based on a service-side requirement calling model, which facilitates the users' subsequent use of data.