system

Abstract

The system according to this embodiment aims to support visually impaired and physically disabled individuals in moving safely and comfortably. [Solution] The system according to the embodiment comprises a sensor unit, an operating unit, a monitoring unit, and an adjustment unit. The sensor unit detects the surrounding environment. The operating unit operates appropriately based on the information detected by the sensor unit. The monitoring unit monitors the user's movements and health condition. The adjustment unit adjusts to a good or comfortable posture based on the information obtained by the monitoring unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , ,

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is not enough support for visually impaired people or people with physical disabilities to move safely and comfortably, and there is room for improvement.

[0005] The system according to the embodiment aims to support visually impaired people or people with physical disabilities to move safely and comfortably.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a sensor unit, an operating unit, a monitoring unit, and an adjustment unit. The sensor unit detects the surrounding environment. The operating unit operates appropriately based on the information detected by the sensor unit. The monitoring unit monitors the user's movements and health condition. The adjustment unit adjusts to a good or comfortable posture based on the information obtained by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can assist visually impaired or physically disabled individuals in moving safely and comfortably. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The mobility support system according to an embodiment of the present invention is a system that helps visually impaired or physically disabled individuals move more freely and safely by wearing a robot. In this mobility support system, the robot detects the surrounding environment (people flow, cars, obstacles, etc.) with sensors and operates appropriately. Next, sensors mounted on the robot constantly monitor the user's movements and health status (blood pressure, heart rate, etc.), and adjust to a good or comfortable posture through learning. This system enables visually impaired or physically disabled individuals to move safely and comfortably. For example, sensors mounted on the robot detect the surrounding environment. For example, it can grasp the movement of people and cars, the location of obstacles, etc. in real time. As a result, the robot operates appropriately according to the surrounding situation, enabling the user to move safely. Next, sensors mounted on the robot monitor the user's movements and health status. For example, it can constantly measure the user's blood pressure and heart rate and issue an alert if an abnormality is detected. It can also learn the user's movements and automatically adjust to a good or comfortable posture. As a result, the user can move for long periods of time without getting tired and can stay comfortable. Furthermore, the robot learns the user's movements and health condition, providing optimal support for each individual user. For example, it can learn the user's walking patterns and changes in physical condition, and provide support accordingly. This allows users to move more safely and comfortably. This system enables visually impaired and physically disabled individuals to move safely and comfortably. For example, when a visually impaired person wears the robot and moves, the robot detects the surrounding environment and operates appropriately, allowing them to move safely without bumping into obstacles. Similarly, when a physically disabled person wears the robot and moves, the robot monitors the user's movements and health condition, and adjusts to a good or comfortable posture, making them less tired and more comfortable even during long journeys. In this way, the mobility assistance system can help visually impaired and physically disabled individuals move more freely and safely.

[0029] The mobility support system according to this embodiment comprises a sensor unit, an operating unit, a monitoring unit, and an adjustment unit. The sensor unit detects the surrounding environment. The sensor unit can detect, for example, pedestrian flow, vehicle movement, and the location of obstacles in real time. The sensor unit detects the surrounding environment using, for example, an infrared sensor or an ultrasonic sensor. The sensor unit can also visually detect the surrounding environment using a camera. For example, the sensor unit can detect the surrounding environment even at night using an infrared sensor. An ultrasonic sensor can detect the location of obstacles using sound waves. A camera can detect the surrounding environment in detail using image processing technology. The operating unit operates appropriately based on the information detected by the sensor unit. The operating unit can control the robot's movement using, for example, a motor or actuator. The operating unit can change the robot's direction of travel to avoid obstacles detected by the sensor unit. The operating unit can also adjust the robot's speed according to the pedestrian flow detected by the sensor unit. For example, the operating unit can change the robot's direction of travel using a motor. Actuators can move the robot's joints. The operating unit can control the robot's movements in real time based on information detected by the sensor unit. The monitoring unit monitors the user's movements and health status. The monitoring unit can, for example, continuously measure the user's blood pressure and heart rate. The monitoring unit can, for example, detect the user's movements with sensors and monitor them in real time. The monitoring unit can, for example, use a blood pressure monitor to measure the user's blood pressure. It can use a heart rate sensor to measure the heart rate. It can use an accelerometer or gyroscope to detect the user's movements. The adjustment unit adjusts to a good or comfortable posture based on information obtained by the monitoring unit. The adjustment unit can, for example, adjust the user's posture in real time. The adjustment unit can, for example, use motors or actuators to adjust the user's posture. The adjustment unit can, for example, use a motor to change the angle of the chair to adjust the user's posture.An actuator can be used to adjust the angle of the user's backrest. The adjustment unit can adjust the user's posture in real time based on information obtained from the monitoring unit. As a result, the mobility support system according to this embodiment can help visually impaired or physically disabled individuals move more freely and safely.

[0030] The sensor unit detects the surrounding environment. For example, it can detect pedestrian flow, vehicle movement, and the location of obstacles in real time. The sensor unit uses, for example, infrared sensors and ultrasonic sensors to detect the surrounding environment. It can also visually detect the surrounding environment using a camera. For example, the sensor unit can detect the surrounding environment even at night using an infrared sensor. Ultrasonic sensors can detect the location of obstacles using sound waves. Cameras can detect the surrounding environment in detail using image processing technology. By using these sensors in combination, the sensor unit achieves more accurate environmental recognition. For example, combining an infrared sensor and a camera allows for accurate understanding of the surrounding situation day and night. Combining an ultrasonic sensor and a camera allows for detailed detection of the location and shape of obstacles, enabling smoother avoidance maneuvers. Furthermore, the sensor unit processes the collected data in real time and transmits it to the central control unit. This enables the entire system to operate quickly and accurately. The sensor unit can maintain high accuracy at all times through regular calibration and maintenance. For example, by regularly adjusting the sensitivity of the infrared sensor and cleaning the camera lens, stable performance can be maintained over a long period of time. This allows the sensor unit to accurately detect the surrounding environment, enabling visually impaired and physically disabled individuals to move safely, and improving the reliability of the entire system.

[0031] The operating unit operates appropriately based on information detected by the sensor unit. The operating unit can control the robot's movements using, for example, motors and actuators. For example, the operating unit can change the robot's direction of travel to avoid obstacles detected by the sensor unit. The operating unit can also adjust the robot's speed according to the flow of people detected by the sensor unit. For example, the operating unit can change the robot's direction of travel using motors. Actuators can move the robot's joints. The operating unit can control the robot's movements in real time based on information detected by the sensor unit. The operating unit receives data from the sensor unit and quickly executes the actions instructed by the central control unit. For example, if an obstacle is detected, the operating unit immediately changes direction of travel to avoid a collision. Also, in areas with heavy pedestrian traffic, the operating unit reduces the robot's speed to allow for safe movement. The operating unit can achieve complex movements by coordinating multiple motors and actuators. For example, even in situations that are difficult with normal movement, such as climbing stairs or overcoming steps, the operating unit can perform appropriate actions and move smoothly. Furthermore, the movement unit can perform customized actions in response to user instructions. For example, if the user wants to move to a specific location, the movement unit will select the optimal route according to the instructions and safely move to the destination. In this way, the movement unit can assist visually impaired and physically disabled individuals in moving more freely, improving their quality of life.

[0032] The monitoring unit monitors the user's movements and health status. For example, the monitoring unit can continuously measure the user's blood pressure and heart rate. For example, the monitoring unit can detect the user's movements using sensors and monitor them in real time. For example, the monitoring unit can use a blood pressure monitor to measure the user's blood pressure. For example, it can use a heart rate sensor to measure the heart rate. For example, it can use an accelerometer or gyroscope to detect the user's movements. By using these sensors in combination, the monitoring unit can comprehensively understand the user's health status. For example, by combining a blood pressure monitor and a heart rate sensor, the user's cardiovascular system can be monitored in detail. In addition, by using an accelerometer or gyroscope, changes in the user's movements and posture can be detected in real time, allowing for early detection of falls or abnormal movements. The monitoring unit transmits the collected data to a central database, making it accessible to medical professionals and caregivers. This allows for remote monitoring of the user's health status and rapid response when necessary. Furthermore, the monitoring unit can analyze the collected data to detect trends and abnormalities in the user's health status. For example, by analyzing fluctuations in blood pressure and heart rate, the system can issue a warning if an abnormal pattern is detected. This allows the monitoring unit to constantly monitor the user's health status, detect abnormalities early, and take appropriate action.

[0033] The adjustment unit adjusts to a good or comfortable posture based on information obtained from the monitoring unit. The adjustment unit can, for example, adjust the user's posture in real time. The adjustment unit can, for example, use motors or actuators to adjust the user's posture. The adjustment unit can, for example, change the angle of the chair using a motor to adjust the user's posture. It can also adjust the angle of the user's backrest using an actuator. The adjustment unit can adjust the user's posture in real time based on information obtained from the monitoring unit. The adjustment unit makes adjustments to maintain the optimal posture according to the user's health condition and movement. For example, since blood circulation worsens if the user stays in the same position for a long time, it adjusts to change the posture periodically. Also, if the user feels fatigued, it can adjust to a relaxing posture. The adjustment unit can also receive user feedback and customize the adjustments. For example, if the user prefers a particular posture, it can remember that posture and automatically adjust it as needed. Furthermore, the adjustment unit can also suggest the optimal posture considering the user's health condition. For example, for a user with lower back pain, it can suggest a posture that does not strain the lower back and actually adjust to that posture. This allows the adjustment unit to maintain the user's health and support a comfortable life. The adjustment unit works in conjunction with the monitoring unit to constantly monitor the user's health and make adjustments to maintain the optimal posture. This enables the adjustment unit to support visually impaired and physically disabled individuals in living more comfortably and improve their quality of life.

[0034] The sensor unit can detect pedestrian flow, vehicle movement, and the location of obstacles in real time. For example, the sensor unit can use an infrared sensor to detect pedestrian flow. The infrared sensor detects infrared radiation emitted from the human body and can determine pedestrian flow. The sensor unit can also use an ultrasonic sensor to detect vehicle movement. The ultrasonic sensor uses sound waves to detect vehicle movement and can determine its direction of travel and speed. Furthermore, the sensor unit can use a camera to detect the location of obstacles. The camera can use image processing technology to determine the location of obstacles in detail. As a result, the sensor unit can detect the surrounding environment in real time and operate appropriately. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input data obtained from the infrared sensor, ultrasonic sensor, and camera into a generating AI, which can analyze the data to detect pedestrian flow, vehicle movement, and the location of obstacles.

[0035] The monitoring unit can continuously measure the user's blood pressure and heart rate and issue an alert if an abnormality is detected. For example, the monitoring unit can continuously measure the user's blood pressure using a blood pressure monitor. The blood pressure monitor can measure the user's blood pressure in real time and issue an alert if an abnormality is detected. The monitoring unit can also continuously measure the user's heart rate using a heart rate sensor. The heart rate sensor can measure the user's heart rate in real time and issue an alert if an abnormality is detected. Furthermore, the monitoring unit can detect and monitor the user's movements in real time using an accelerometer and a gyroscope. This allows the monitoring unit to constantly monitor the user's health status and respond quickly if an abnormality is detected. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data obtained from the blood pressure monitor, heart rate sensor, accelerometer, and gyroscope into a generating AI, which can analyze the data to detect abnormalities and issue an alert.

[0036] The adjustment unit can learn the user's movements and automatically adjust to a good or comfortable posture. The adjustment unit can, for example, detect the user's movements with sensors and learn them in real time. The adjustment unit can, for example, use motors or actuators to adjust the user's posture. The adjustment unit can, for example, change the angle of the chair using a motor to adjust the user's posture. It can adjust the angle of the user's backrest using an actuator. The adjustment unit can learn the user's movements and automatically adjust to a good or comfortable posture. As a result, the adjustment unit can learn the user's movements and provide the optimal posture. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user movement data obtained from sensors into a generating AI, which can analyze the data and adjust to a good or comfortable posture.

[0037] The adjustment unit can learn the user's walking patterns and changes in their physical condition, and provide support accordingly. For example, the adjustment unit can detect the user's walking patterns with sensors and learn them in real time. For example, the adjustment unit can detect changes in the user's physical condition with sensors and learn them in real time. The adjustment unit can learn the user's walking patterns and changes in their physical condition, and provide support accordingly. As a result, the adjustment unit can provide support that is tailored to the user's walking patterns and changes in their physical condition. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input data on the user's walking patterns and changes in their physical condition obtained from sensors into a generating AI, which can then analyze the data to provide optimal support.

[0038] The sensor unit can switch its operating mode according to the weather and time of day when detecting the surrounding environment. For example, the sensor unit can switch to night vision mode at night to detect the surrounding environment. For example, the sensor unit can switch to waterproof mode in rainy weather to maintain sensor operation. For example, the sensor unit can switch to high-sensitivity mode during crowded daytime hours to detect pedestrian flow in detail. In this way, the sensor unit can provide more appropriate information by switching the sensor's operating mode according to the weather and time of day. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input weather and time-of-day data into a generating AI, which can analyze the data and switch the sensor's operating mode.

[0039] The sensor unit can transmit detected information to the cloud in real time and share information with other robots. For example, the sensor unit can transmit obstacle information detected by the sensor to the cloud, allowing other robots to share avoidance routes. For example, the sensor unit can transmit pedestrian flow information detected by the sensor to the cloud, allowing other robots to share routes to avoid congestion. For example, the sensor unit can transmit vehicle movement detected by the sensor to the cloud, allowing other robots to share safe travel routes. In this way, the sensor unit can provide safer movement by sharing information with other robots. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input detected information into a generating AI, which will analyze the data and transmit it to the cloud, allowing information to be shared with other robots.

[0040] The sensor unit can notify the user's smartphone of the detected information, enabling the user to understand the situation. For example, the sensor unit can notify the smartphone of obstacle information detected by the sensor, enabling the user to take evasive action. For example, the sensor unit can notify the smartphone of pedestrian flow information detected by the sensor, enabling the user to choose a route that avoids congestion. For example, the sensor unit can notify the smartphone of vehicle movement detected by the sensor, enabling the user to choose a safe route. In this way, the sensor unit can provide safer travel by enabling the user to understand the situation. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input detected information into a generating AI, the generating AI can analyze the data and notify the smartphone, enabling the user to understand the situation.

[0041] The sensor unit can analyze ambient sound information based on the detected information and issue voice warnings to the user. For example, the sensor unit can issue voice warnings to the user based on obstacle information detected by the sensor. For example, the sensor unit can issue voice warnings to the user based on pedestrian flow information detected by the sensor. For example, the sensor unit can issue voice warnings to the user based on vehicle movement detected by the sensor. In this way, the sensor unit can provide safer travel by analyzing ambient sound information and issuing voice warnings to the user. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input detected information into a generating AI, which can analyze the data to generate ambient sound information and issue voice warnings to the user.

[0042] The operating unit can automatically switch between walking mode and running mode depending on the surrounding environment. For example, in crowded places, the operating unit can switch to walking mode for safe movement. For example, in open spaces, the operating unit can switch to running mode for rapid movement. For example, in places with many obstacles, the operating unit can switch to walking mode for careful movement. In this way, the operating unit can provide safer and faster movement by switching operating modes according to the surrounding environment. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input surrounding environment data into a generating AI, and the generating AI can analyze the data and automatically switch between walking mode and running mode.

[0043] The operating unit can calculate the optimal avoidance route when avoiding obstacles and provide the user with safe movement. For example, when the operating unit detects an obstacle, it can calculate the optimal avoidance route and provide the user with safe movement. For example, in areas with many obstacles, the operating unit can calculate multiple avoidance routes and select the safest route. For example, if an obstacle is moving, the operating unit can update the avoidance route in real time and provide the user with safe movement. In this way, the operating unit can calculate the optimal route when avoiding obstacles and provide safe movement. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input obstacle data into a generating AI, which can analyze the data to calculate the optimal avoidance route and provide the user with safe movement.

[0044] The operating unit can issue warnings to the user via sound or vibration depending on the surrounding environment. For example, the operating unit can issue a voice warning to the user when it detects an obstacle. For example, the operating unit can issue a vibration warning to the user in a place with heavy pedestrian traffic. For example, the operating unit can issue a voice and vibration warning to the user when it detects the movement of a vehicle. In this way, the operating unit can provide safer travel by issuing warnings via sound and vibration depending on the surrounding environment. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input surrounding environment data into a generating AI, and the generating AI can analyze the data and issue a warning to the user via sound or vibration.

[0045] The operating unit can work in cooperation with other robots to enable multiple users to move safely. For example, the operating unit can work in cooperation with other robots to enable multiple users to move safely. For example, the operating unit can work in cooperation with other robots to select a route that avoids congestion. For example, the operating unit can work in cooperation with other robots to select a route that avoids obstacles. In this way, the operating unit can work in cooperation with other robots to enable multiple users to move safely. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input cooperation data with other robots into a generating AI, and the generating AI can analyze the data to realize cooperative operation.

[0046] The monitoring unit can transmit the user's health status to a medical institution in real time, enabling a rapid response in emergencies. For example, the monitoring unit can transmit the user's blood pressure and heart rate to a medical institution in real time, enabling a rapid response in emergencies. For example, the monitoring unit can transmit the user's health status to a medical institution in real time, enabling a rapid response if an abnormality is detected. For example, the monitoring unit can transmit the user's health data to a medical institution in real time, enabling regular health checks. As a result, the monitoring unit can transmit the user's health status to a medical institution in real time, enabling a rapid response in emergencies. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's health data into a generating AI, which can analyze the data and transmit it to a medical institution in real time, enabling a rapid response in emergencies.

[0047] The monitoring unit can also measure the user's body temperature and respiratory rate, enabling it to grasp their overall health status. For example, the monitoring unit can continuously measure the user's body temperature and issue an alert if an abnormality is detected. For example, the monitoring unit can continuously measure the user's respiratory rate and issue an alert if an abnormality is detected. For example, the monitoring unit can comprehensively measure the user's body temperature and respiratory rate to grasp their health status. In this way, the monitoring unit can grasp the user's overall health status by measuring their body temperature and respiratory rate. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's body temperature and respiratory rate data into a generating AI, which can analyze the data to grasp their overall health status.

[0048] The monitoring unit can also collect data on the user's diet and exercise to support health management. For example, the monitoring unit can collect the user's dietary data to support health management. For example, the monitoring unit can collect the user's exercise data to support health management. For example, the monitoring unit can comprehensively collect the user's dietary and exercise data to support health management. As a result, the monitoring unit can provide comprehensive health management by collecting data on the user's diet and exercise. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's dietary and exercise data into a generating AI, which can then analyze the data to support health management.

[0049] The monitoring unit can analyze the user's sleep state and suggest the optimal rest timing. For example, the monitoring unit can analyze the user's sleep data and suggest the optimal rest timing. For example, the monitoring unit can analyze the user's sleep state in real time and suggest the optimal rest timing. For example, the monitoring unit can comprehensively analyze the user's sleep data and suggest the optimal rest timing. In this way, the monitoring unit can suggest the optimal rest timing by analyzing the user's sleep state. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's sleep data into a generating AI, which can analyze the data and suggest the optimal rest timing.

[0050] The adjustment unit can measure the user's muscle tension and provide the optimal posture. For example, the adjustment unit can measure the user's muscle tension and provide the optimal posture. For example, if the user's muscle tension is high, the adjustment unit can provide a relaxed posture. For example, if the user's muscle tension is low, the adjustment unit can provide a posture that maintains moderate tension. Thus, the adjustment unit can provide the optimal posture by measuring the user's muscle tension. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input data on the user's muscle tension into a generating AI, and the generating AI can analyze the data to provide the optimal posture.

[0051] The adjustment unit can learn the user's skeletal and muscular condition and provide an individually optimized posture. For example, the adjustment unit can learn the user's skeletal and muscular condition and provide an individually optimized posture. For example, the adjustment unit can learn the user's skeletal distortion and provide a posture that corrects it. For example, the adjustment unit can learn the user's muscle balance and provide a posture that balances it. In this way, the adjustment unit can provide an individually optimized posture by learning the user's skeletal and muscular condition. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input data on the user's skeletal and muscular condition into a generating AI, and the generating AI can analyze the data and provide an individually optimized posture.

[0052] The adjustment unit can also adjust the user's sitting and standing postures, enabling comprehensive posture management. For example, the adjustment unit can adjust the user's sitting posture to provide a comfortable posture. For example, the adjustment unit can adjust the user's standing posture to provide a posture that reduces fatigue. For example, the adjustment unit can comprehensively adjust the user's sitting and standing postures to provide an optimal posture. In this way, the adjustment unit enables comprehensive posture management by adjusting the user's sitting and standing postures. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input data on the user's sitting and standing postures into a generating AI, which can then analyze the data to perform comprehensive posture management.

[0053] The adjustment unit can analyze the user's exercise data and propose an optimal rehabilitation plan. For example, the adjustment unit can analyze the user's exercise data and propose an optimal rehabilitation plan. For example, the adjustment unit can propose an individually optimized rehabilitation plan based on the user's exercise data. For example, the adjustment unit can comprehensively analyze the user's exercise data and propose an effective rehabilitation plan. In this way, the adjustment unit can propose an optimal rehabilitation plan by analyzing the user's exercise data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's exercise data into a generating AI, and the generating AI can analyze the data and propose an optimal rehabilitation plan.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The mobility assistance system can also be equipped with a voice recognition unit. The voice recognition unit can recognize the user's voice commands and send instructions to various parts of the system. For example, if the user commands "move forward," the operation unit can move the robot forward. If the user commands "stop," the operation unit can stop the robot. Furthermore, if the user commands "turn right," the operation unit can turn the robot to the right. This allows the user to operate the robot using voice commands, enabling more freedom and safer movement.

[0056] The mobility assistance system can also be equipped with a lighting unit. The lighting unit can automatically turn on and off depending on the ambient light. For example, at night or in dark places, the lighting unit will automatically turn on to ensure the user's visibility. Conversely, during the day or in bright places, the lighting unit will automatically turn off to conserve energy. Furthermore, the lighting unit can also be manually turned on and off by the user. This allows the user to adjust the lighting according to the ambient light, enabling safer movement.

[0057] The mobility assistance system can also be equipped with a navigation unit. The navigation unit can use GPS to determine the user's current location and provide the optimal route to their destination. For example, once the user sets a destination, the navigation unit can calculate the best route and provide voice and visual instructions. Furthermore, the navigation unit can acquire real-time traffic information and suggest routes that avoid congestion. This allows the user to travel to their destination more smoothly.

[0058] The mobility assistance system can also be equipped with an emergency call unit. This unit allows the user to press a button in an emergency, automatically notifying pre-registered contacts. For example, if the user falls or experiences a sudden change in their health, the unit can send an emergency call along with the user's location information. The unit can also monitor the user's health status and automatically send a notification if an abnormality is detected. This ensures that the user receives a prompt response in an emergency.

[0059] The mobility assistance system can also be equipped with an entertainment section. This section provides functions that allow users to enjoy music and audiobooks while traveling. For example, if a user wants to listen to music, the entertainment section can play a playlist tailored to the user's preferences. If a user wants to listen to an audiobook, the entertainment section can play an audiobook of their choice. Furthermore, the entertainment section can also provide users with news and weather forecasts. This allows users to enjoy themselves while traveling.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The sensor unit detects the surrounding environment. The sensor unit can detect pedestrian flow, vehicle movement, and the location of obstacles in real time. Specifically, it uses infrared sensors, ultrasonic sensors, cameras, etc., to detect the surrounding environment. For example, infrared sensors can detect the surrounding environment even at night, ultrasonic sensors can detect the location of obstacles using sound waves, and cameras can detect the surrounding environment in detail using image processing technology. Step 2: The operating unit operates appropriately based on the information detected by the sensor unit. The operating unit controls the robot's movements using motors and actuators. For example, it may change the robot's direction of travel to avoid obstacles detected by the sensor unit, or adjust the robot's speed according to the flow of people. The operating unit can control the robot's movements in real time based on the information detected by the sensor unit. Step 3: The monitoring unit monitors the user's movements and health status. The monitoring unit constantly measures the user's blood pressure and heart rate, and detects the user's movements with sensors for real-time monitoring. Specifically, it can use a blood pressure monitor, heart rate sensor, accelerometer, gyroscope, etc. Step 4: The adjustment unit adjusts to a good or comfortable posture based on the information obtained by the monitoring unit. The adjustment unit uses motors and actuators to adjust the user's posture in real time. For example, it may use a motor to change the angle of the chair or an actuator to adjust the angle of the backrest. The adjustment unit can adjust the user's posture in real time based on the information obtained by the monitoring unit.

[0062] (Example of form 2) The mobility support system according to an embodiment of the present invention is a system that helps visually impaired or physically disabled individuals move more freely and safely by wearing a robot. In this mobility support system, the robot detects the surrounding environment (people flow, cars, obstacles, etc.) with sensors and operates appropriately. Next, sensors mounted on the robot constantly monitor the user's movements and health status (blood pressure, heart rate, etc.), and adjust to a good or comfortable posture through learning. This system enables visually impaired or physically disabled individuals to move safely and comfortably. For example, sensors mounted on the robot detect the surrounding environment. For example, it can grasp the movement of people and cars, the location of obstacles, etc. in real time. As a result, the robot operates appropriately according to the surrounding situation, enabling the user to move safely. Next, sensors mounted on the robot monitor the user's movements and health status. For example, it can constantly measure the user's blood pressure and heart rate and issue an alert if an abnormality is detected. It can also learn the user's movements and automatically adjust to a good or comfortable posture. As a result, the user can move for long periods of time without getting tired and can stay comfortable. Furthermore, the robot learns the user's movements and health condition, providing optimal support for each individual user. For example, it can learn the user's walking patterns and changes in physical condition, and provide support accordingly. This allows users to move more safely and comfortably. This system enables visually impaired and physically disabled individuals to move safely and comfortably. For example, when a visually impaired person wears the robot and moves, the robot detects the surrounding environment and operates appropriately, allowing them to move safely without bumping into obstacles. Similarly, when a physically disabled person wears the robot and moves, the robot monitors the user's movements and health condition, and adjusts to a good or comfortable posture, making them less tired and more comfortable even during long journeys. In this way, the mobility assistance system can help visually impaired and physically disabled individuals move more freely and safely.

[0063] The mobility support system according to this embodiment comprises a sensor unit, an operating unit, a monitoring unit, and an adjustment unit. The sensor unit detects the surrounding environment. The sensor unit can detect, for example, pedestrian flow, vehicle movement, and the location of obstacles in real time. The sensor unit detects the surrounding environment using, for example, an infrared sensor or an ultrasonic sensor. The sensor unit can also visually detect the surrounding environment using a camera. For example, the sensor unit can detect the surrounding environment even at night using an infrared sensor. An ultrasonic sensor can detect the location of obstacles using sound waves. A camera can detect the surrounding environment in detail using image processing technology. The operating unit operates appropriately based on the information detected by the sensor unit. The operating unit can control the robot's movement using, for example, a motor or actuator. The operating unit can change the robot's direction of travel to avoid obstacles detected by the sensor unit. The operating unit can also adjust the robot's speed according to the pedestrian flow detected by the sensor unit. For example, the operating unit can change the robot's direction of travel using a motor. Actuators can move the robot's joints. The operating unit can control the robot's movements in real time based on information detected by the sensor unit. The monitoring unit monitors the user's movements and health status. The monitoring unit can, for example, continuously measure the user's blood pressure and heart rate. The monitoring unit can, for example, detect the user's movements with sensors and monitor them in real time. The monitoring unit can, for example, use a blood pressure monitor to measure the user's blood pressure. It can use a heart rate sensor to measure the heart rate. It can use an accelerometer or gyroscope to detect the user's movements. The adjustment unit adjusts to a good or comfortable posture based on information obtained by the monitoring unit. The adjustment unit can, for example, adjust the user's posture in real time. The adjustment unit can, for example, use motors or actuators to adjust the user's posture. The adjustment unit can, for example, use a motor to change the angle of the chair to adjust the user's posture.An actuator can be used to adjust the angle of the user's backrest. The adjustment unit can adjust the user's posture in real time based on information obtained from the monitoring unit. As a result, the mobility support system according to this embodiment can help visually impaired or physically disabled individuals move more freely and safely.

[0064] The sensor unit detects the surrounding environment. For example, it can detect pedestrian flow, vehicle movement, and the location of obstacles in real time. The sensor unit uses, for example, infrared sensors and ultrasonic sensors to detect the surrounding environment. It can also visually detect the surrounding environment using a camera. For example, the sensor unit can detect the surrounding environment even at night using an infrared sensor. Ultrasonic sensors can detect the location of obstacles using sound waves. Cameras can detect the surrounding environment in detail using image processing technology. By using these sensors in combination, the sensor unit achieves more accurate environmental recognition. For example, combining an infrared sensor and a camera allows for accurate understanding of the surrounding situation day and night. Combining an ultrasonic sensor and a camera allows for detailed detection of the location and shape of obstacles, enabling smoother avoidance maneuvers. Furthermore, the sensor unit processes the collected data in real time and transmits it to the central control unit. This enables the entire system to operate quickly and accurately. The sensor unit can maintain high accuracy at all times through regular calibration and maintenance. For example, by regularly adjusting the sensitivity of the infrared sensor and cleaning the camera lens, stable performance can be maintained over a long period of time. This allows the sensor unit to accurately detect the surrounding environment, enabling visually impaired and physically disabled individuals to move safely, and improving the reliability of the entire system.

[0065] The operating unit operates appropriately based on information detected by the sensor unit. The operating unit can control the robot's movements using, for example, motors and actuators. For example, the operating unit can change the robot's direction of travel to avoid obstacles detected by the sensor unit. The operating unit can also adjust the robot's speed according to the flow of people detected by the sensor unit. For example, the operating unit can change the robot's direction of travel using motors. Actuators can move the robot's joints. The operating unit can control the robot's movements in real time based on information detected by the sensor unit. The operating unit receives data from the sensor unit and quickly executes the actions instructed by the central control unit. For example, if an obstacle is detected, the operating unit immediately changes direction of travel to avoid a collision. Also, in areas with heavy pedestrian traffic, the operating unit reduces the robot's speed to allow for safe movement. The operating unit can achieve complex movements by coordinating multiple motors and actuators. For example, even in situations that are difficult with normal movement, such as climbing stairs or overcoming steps, the operating unit can perform appropriate actions and move smoothly. Furthermore, the movement unit can perform customized actions in response to user instructions. For example, if the user wants to move to a specific location, the movement unit will select the optimal route according to the instructions and safely move to the destination. In this way, the movement unit can assist visually impaired and physically disabled individuals in moving more freely, improving their quality of life.

[0066] The monitoring unit monitors the user's movements and health status. For example, the monitoring unit can continuously measure the user's blood pressure and heart rate. For example, the monitoring unit can detect the user's movements using sensors and monitor them in real time. For example, the monitoring unit can use a blood pressure monitor to measure the user's blood pressure. For example, it can use a heart rate sensor to measure the heart rate. For example, it can use an accelerometer or gyroscope to detect the user's movements. By using these sensors in combination, the monitoring unit can comprehensively understand the user's health status. For example, by combining a blood pressure monitor and a heart rate sensor, the user's cardiovascular system can be monitored in detail. In addition, by using an accelerometer or gyroscope, changes in the user's movements and posture can be detected in real time, allowing for early detection of falls or abnormal movements. The monitoring unit transmits the collected data to a central database, making it accessible to medical professionals and caregivers. This allows for remote monitoring of the user's health status and rapid response when necessary. Furthermore, the monitoring unit can analyze the collected data to detect trends and abnormalities in the user's health status. For example, by analyzing fluctuations in blood pressure and heart rate, the system can issue a warning if an abnormal pattern is detected. This allows the monitoring unit to constantly monitor the user's health status, detect abnormalities early, and take appropriate action.

[0067] The adjustment unit adjusts to a good or comfortable posture based on information obtained from the monitoring unit. The adjustment unit can, for example, adjust the user's posture in real time. The adjustment unit can, for example, use motors or actuators to adjust the user's posture. The adjustment unit can, for example, change the angle of the chair using a motor to adjust the user's posture. It can also adjust the angle of the user's backrest using an actuator. The adjustment unit can adjust the user's posture in real time based on information obtained from the monitoring unit. The adjustment unit makes adjustments to maintain the optimal posture according to the user's health condition and movement. For example, since blood circulation worsens if the user stays in the same position for a long time, it adjusts to change the posture periodically. Also, if the user feels fatigued, it can adjust to a relaxing posture. The adjustment unit can also receive user feedback and customize the adjustments. For example, if the user prefers a particular posture, it can remember that posture and automatically adjust it as needed. Furthermore, the adjustment unit can also suggest the optimal posture considering the user's health condition. For example, for a user with lower back pain, it can suggest a posture that does not strain the lower back and actually adjust to that posture. This allows the adjustment unit to maintain the user's health and support a comfortable life. The adjustment unit works in conjunction with the monitoring unit to constantly monitor the user's health and make adjustments to maintain the optimal posture. This enables the adjustment unit to support visually impaired and physically disabled individuals in living more comfortably and improve their quality of life.

[0068] The sensor unit can detect pedestrian flow, vehicle movement, and the location of obstacles in real time. For example, the sensor unit can use an infrared sensor to detect pedestrian flow. The infrared sensor detects infrared radiation emitted from the human body and can determine pedestrian flow. The sensor unit can also use an ultrasonic sensor to detect vehicle movement. The ultrasonic sensor uses sound waves to detect vehicle movement and can determine its direction of travel and speed. Furthermore, the sensor unit can use a camera to detect the location of obstacles. The camera can use image processing technology to determine the location of obstacles in detail. As a result, the sensor unit can detect the surrounding environment in real time and operate appropriately. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input data obtained from the infrared sensor, ultrasonic sensor, and camera into a generating AI, which can analyze the data to detect pedestrian flow, vehicle movement, and the location of obstacles.

[0069] The monitoring unit can continuously measure the user's blood pressure and heart rate and issue an alert if an abnormality is detected. For example, the monitoring unit can continuously measure the user's blood pressure using a blood pressure monitor. The blood pressure monitor can measure the user's blood pressure in real time and issue an alert if an abnormality is detected. The monitoring unit can also continuously measure the user's heart rate using a heart rate sensor. The heart rate sensor can measure the user's heart rate in real time and issue an alert if an abnormality is detected. Furthermore, the monitoring unit can detect and monitor the user's movements in real time using an accelerometer and a gyroscope. This allows the monitoring unit to constantly monitor the user's health status and respond quickly if an abnormality is detected. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data obtained from the blood pressure monitor, heart rate sensor, accelerometer, and gyroscope into a generating AI, which can analyze the data to detect abnormalities and issue an alert.

[0070] The adjustment unit can learn the user's movements and automatically adjust to a good or comfortable posture. The adjustment unit can, for example, detect the user's movements with sensors and learn them in real time. The adjustment unit can, for example, use motors or actuators to adjust the user's posture. The adjustment unit can, for example, change the angle of the chair using a motor to adjust the user's posture. It can adjust the angle of the user's backrest using an actuator. The adjustment unit can learn the user's movements and automatically adjust to a good or comfortable posture. As a result, the adjustment unit can learn the user's movements and provide the optimal posture. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input user movement data obtained from sensors into a generating AI, which can analyze the data and adjust to a good or comfortable posture.

[0071] The adjustment unit can learn the user's walking patterns and changes in their physical condition, and provide support accordingly. For example, the adjustment unit can detect the user's walking patterns with sensors and learn them in real time. For example, the adjustment unit can detect changes in the user's physical condition with sensors and learn them in real time. The adjustment unit can learn the user's walking patterns and changes in their physical condition, and provide support accordingly. As a result, the adjustment unit can provide support that is tailored to the user's walking patterns and changes in their physical condition. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input data on the user's walking patterns and changes in their physical condition obtained from sensors into a generating AI, which can then analyze the data to provide optimal support.

[0072] The sensor unit can estimate the user's emotions and adjust the sensor's sensitivity based on the estimated emotions. For example, if the user is tense, the sensor unit can increase the sensor's sensitivity to detect the surrounding environment in more detail. For example, if the user is relaxed, the sensor unit can return the sensor's sensitivity to normal mode to reduce energy consumption. For example, if the user is tired, the sensor unit can adjust the sensor's sensitivity appropriately to detect only the necessary information. In this way, the sensor unit can adjust the sensor's sensitivity according to the user's emotions and provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input the user's facial expression data and voice data into the generative AI, which can analyze the data to estimate the user's emotions and adjust the sensor's sensitivity.

[0073] The sensor unit can switch its operating mode according to the weather and time of day when detecting the surrounding environment. For example, the sensor unit can switch to night vision mode at night to detect the surrounding environment. For example, the sensor unit can switch to waterproof mode in rainy weather to maintain sensor operation. For example, the sensor unit can switch to high-sensitivity mode during crowded daytime hours to detect pedestrian flow in detail. In this way, the sensor unit can provide more appropriate information by switching the sensor's operating mode according to the weather and time of day. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input weather and time-of-day data into a generating AI, which can analyze the data and switch the sensor's operating mode.

[0074] The sensor unit can transmit detected information to the cloud in real time and share information with other robots. For example, the sensor unit can transmit obstacle information detected by the sensor to the cloud, allowing other robots to share avoidance routes. For example, the sensor unit can transmit pedestrian flow information detected by the sensor to the cloud, allowing other robots to share routes to avoid congestion. For example, the sensor unit can transmit vehicle movement detected by the sensor to the cloud, allowing other robots to share safe travel routes. In this way, the sensor unit can provide safer movement by sharing information with other robots. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input detected information into a generating AI, which will analyze the data and transmit it to the cloud, allowing information to be shared with other robots.

[0075] The sensor unit can estimate the user's emotions and adjust its detection range based on the estimated emotions. For example, if the user is feeling anxious, the sensor unit can widen its detection range to gain a more detailed understanding of the surrounding environment. For example, if the user is relaxed, the sensor unit can return the detection range to normal mode. For example, if the user is in a hurry, the sensor unit can narrow its detection range to prioritize the detection of only important information. In this way, the sensor unit can adjust its detection range according to the user's emotions and provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sensor unit may be performed using AI, or not using AI. For example, the sensor unit can input user facial expression data and voice data into the generative AI, which can analyze the data to estimate the user's emotions and adjust the sensor's detection range.

[0076] The sensor unit can notify the user's smartphone of the detected information, enabling the user to understand the situation. For example, the sensor unit can notify the smartphone of obstacle information detected by the sensor, enabling the user to take evasive action. For example, the sensor unit can notify the smartphone of pedestrian flow information detected by the sensor, enabling the user to choose a route that avoids congestion. For example, the sensor unit can notify the smartphone of vehicle movement detected by the sensor, enabling the user to choose a safe route. In this way, the sensor unit can provide safer travel by enabling the user to understand the situation. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input detected information into a generating AI, the generating AI can analyze the data and notify the smartphone, enabling the user to understand the situation.

[0077] The sensor unit can analyze ambient sound information based on the detected information and issue voice warnings to the user. For example, the sensor unit can issue voice warnings to the user based on obstacle information detected by the sensor. For example, the sensor unit can issue voice warnings to the user based on pedestrian flow information detected by the sensor. For example, the sensor unit can issue voice warnings to the user based on vehicle movement detected by the sensor. In this way, the sensor unit can provide safer travel by analyzing ambient sound information and issuing voice warnings to the user. Some or all of the above processing in the sensor unit may be performed using AI, for example, or without AI. For example, the sensor unit can input detected information into a generating AI, which can analyze the data to generate ambient sound information and issue voice warnings to the user.

[0078] The operating unit can estimate the user's emotions and adjust the robot's operating speed based on the estimated emotions. For example, if the user is tense, the operating unit can slow down the robot's operating speed to move safely. For example, if the user is relaxed, the operating unit can return the robot's operating speed to normal mode. For example, if the user is in a hurry, the operating unit can speed up the robot's operating speed to move quickly. In this way, the operating unit can adjust the robot's operating speed according to the user's emotions and provide safer movement. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input user facial expression data or voice data into a generative AI, which can analyze the data to estimate the user's emotions and adjust the robot's operating speed.

[0079] The operating unit can automatically switch between walking mode and running mode depending on the surrounding environment. For example, in crowded places, the operating unit can switch to walking mode for safe movement. For example, in open spaces, the operating unit can switch to running mode for rapid movement. For example, in places with many obstacles, the operating unit can switch to walking mode for careful movement. In this way, the operating unit can provide safer and faster movement by switching operating modes according to the surrounding environment. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input surrounding environment data into a generating AI, and the generating AI can analyze the data and automatically switch between walking mode and running mode.

[0080] The operating unit can calculate the optimal avoidance route when avoiding obstacles and provide the user with safe movement. For example, when the operating unit detects an obstacle, it can calculate the optimal avoidance route and provide the user with safe movement. For example, in areas with many obstacles, the operating unit can calculate multiple avoidance routes and select the safest route. For example, if an obstacle is moving, the operating unit can update the avoidance route in real time and provide the user with safe movement. In this way, the operating unit can calculate the optimal route when avoiding obstacles and provide safe movement. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input obstacle data into a generating AI, which can analyze the data to calculate the optimal avoidance route and provide the user with safe movement.

[0081] The operating unit can estimate the user's emotions and change the robot's movement pattern based on the estimated emotions. For example, if the user is feeling anxious, the operating unit can carefully change the robot's movement pattern. For example, if the user is relaxed, the operating unit can return the robot's movement pattern to normal mode. For example, if the user is in a hurry, the operating unit can quickly change the robot's movement pattern. In this way, the operating unit can change the robot's movement pattern according to the user's emotions and provide safer movement. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input user facial expression data or voice data into the generative AI, which can analyze the data to estimate the user's emotions and change the robot's movement pattern.

[0082] The operating unit can issue warnings to the user via sound or vibration depending on the surrounding environment. For example, the operating unit can issue a voice warning to the user when it detects an obstacle. For example, the operating unit can issue a vibration warning to the user in a place with heavy pedestrian traffic. For example, the operating unit can issue a voice and vibration warning to the user when it detects the movement of a vehicle. In this way, the operating unit can provide safer travel by issuing warnings via sound and vibration depending on the surrounding environment. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input surrounding environment data into a generating AI, and the generating AI can analyze the data and issue a warning to the user via sound or vibration.

[0083] The operating unit can work in cooperation with other robots to enable multiple users to move safely. For example, the operating unit can work in cooperation with other robots to enable multiple users to move safely. For example, the operating unit can work in cooperation with other robots to select a route that avoids congestion. For example, the operating unit can work in cooperation with other robots to select a route that avoids obstacles. In this way, the operating unit can work in cooperation with other robots to enable multiple users to move safely. Some or all of the above processing in the operating unit may be performed using AI, for example, or without AI. For example, the operating unit can input cooperation data with other robots into a generating AI, and the generating AI can analyze the data to realize cooperative operation.

[0084] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit can increase the monitoring frequency to gain a more detailed understanding of their health. For example, if the user is relaxed, the monitoring unit can return the monitoring frequency to normal. For example, if the user is tired, the monitoring unit can adjust the monitoring frequency appropriately to acquire only the necessary information. This allows the monitoring unit to adjust the monitoring frequency according to the user's emotions and provide more appropriate health management. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's facial expression data and voice data into the generative AI, which can analyze the data to estimate the user's emotions and adjust the monitoring frequency.

[0085] The monitoring unit can transmit the user's health status to a medical institution in real time, enabling a rapid response in emergencies. For example, the monitoring unit can transmit the user's blood pressure and heart rate to a medical institution in real time, enabling a rapid response in emergencies. For example, the monitoring unit can transmit the user's health status to a medical institution in real time, enabling a rapid response if an abnormality is detected. For example, the monitoring unit can transmit the user's health data to a medical institution in real time, enabling regular health checks. As a result, the monitoring unit can transmit the user's health status to a medical institution in real time, enabling a rapid response in emergencies. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's health data into a generating AI, which can analyze the data and transmit it to a medical institution in real time, enabling a rapid response in emergencies.

[0086] The monitoring unit can also measure the user's body temperature and respiratory rate, enabling it to grasp their overall health status. For example, the monitoring unit can continuously measure the user's body temperature and issue an alert if an abnormality is detected. For example, the monitoring unit can continuously measure the user's respiratory rate and issue an alert if an abnormality is detected. For example, the monitoring unit can comprehensively measure the user's body temperature and respiratory rate to grasp their health status. In this way, the monitoring unit can grasp the user's overall health status by measuring their body temperature and respiratory rate. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's body temperature and respiratory rate data into a generating AI, which can analyze the data to grasp their overall health status.

[0087] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring data based on the estimated emotions. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. For example, if the user is relaxed, the monitoring unit can provide a display method that includes detailed information. For example, if the user is tired, the monitoring unit can provide a method that displays only the necessary information. This allows the monitoring unit to adjust the display method of the monitoring data according to the user's emotions, enabling the provision of more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's facial expression data and voice data into the generative AI, which can analyze the data to estimate the user's emotions and adjust the display method of the monitoring data.

[0088] The monitoring unit can also collect data on the user's diet and exercise to support health management. For example, the monitoring unit can collect the user's dietary data to support health management. For example, the monitoring unit can collect the user's exercise data to support health management. For example, the monitoring unit can comprehensively collect the user's dietary and exercise data to support health management. As a result, the monitoring unit can provide comprehensive health management by collecting data on the user's diet and exercise. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's dietary and exercise data into a generating AI, which can then analyze the data to support health management.

[0089] The monitoring unit can analyze the user's sleep state and suggest the optimal rest timing. For example, the monitoring unit can analyze the user's sleep data and suggest the optimal rest timing. For example, the monitoring unit can analyze the user's sleep state in real time and suggest the optimal rest timing. For example, the monitoring unit can comprehensively analyze the user's sleep data and suggest the optimal rest timing. In this way, the monitoring unit can suggest the optimal rest timing by analyzing the user's sleep state. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's sleep data into a generating AI, which can analyze the data and suggest the optimal rest timing.

[0090] The adjustment unit can estimate the user's emotions and adjust the frequency of posture adjustments based on the estimated emotions. For example, if the user is tense, the adjustment unit can increase the frequency of posture adjustments to maintain a comfortable posture. For example, if the user is relaxed, the adjustment unit can return the frequency of posture adjustments to the normal mode. For example, if the user is tired, the adjustment unit can moderately adjust the frequency of posture adjustments, adjusting the posture only when necessary. In this way, the adjustment unit can adjust the frequency of posture adjustments according to the user's emotions and provide a more comfortable posture. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's facial expression data or voice data into the generative AI, which can analyze the data to estimate the user's emotions and adjust the frequency of posture adjustments.

[0091] The adjustment unit can measure the user's muscle tension and provide the optimal posture. For example, the adjustment unit can measure the user's muscle tension and provide the optimal posture. For example, if the user's muscle tension is high, the adjustment unit can provide a relaxed posture. For example, if the user's muscle tension is low, the adjustment unit can provide a posture that maintains moderate tension. Thus, the adjustment unit can provide the optimal posture by measuring the user's muscle tension. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input data on the user's muscle tension into a generating AI, and the generating AI can analyze the data to provide the optimal posture.

[0092] The adjustment unit can learn the user's skeletal and muscular condition and provide an individually optimized posture. For example, the adjustment unit can learn the user's skeletal and muscular condition and provide an individually optimized posture. For example, the adjustment unit can learn the user's skeletal distortion and provide a posture that corrects it. For example, the adjustment unit can learn the user's muscle balance and provide a posture that balances it. In this way, the adjustment unit can provide an individually optimized posture by learning the user's skeletal and muscular condition. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input data on the user's skeletal and muscular condition into a generating AI, and the generating AI can analyze the data and provide an individually optimized posture.

[0093] The adjustment unit can estimate the user's emotions and change the posture adjustment method based on the estimated emotions. For example, if the user is tense, the adjustment unit can provide a relaxing posture adjustment method. For example, if the user is relaxed, the adjustment unit can provide a normal posture adjustment method. For example, if the user is tired, the adjustment unit can provide a posture adjustment method that reduces fatigue. In this way, the adjustment unit can change the posture adjustment method according to the user's emotions and provide a more comfortable posture. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's facial expression data or voice data into the generative AI, which can analyze the data to estimate the user's emotions and change the posture adjustment method.

[0094] The adjustment unit can also adjust the user's sitting and standing postures, enabling comprehensive posture management. For example, the adjustment unit can adjust the user's sitting posture to provide a comfortable posture. For example, the adjustment unit can adjust the user's standing posture to provide a posture that reduces fatigue. For example, the adjustment unit can comprehensively adjust the user's sitting and standing postures to provide an optimal posture. In this way, the adjustment unit enables comprehensive posture management by adjusting the user's sitting and standing postures. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input data on the user's sitting and standing postures into a generating AI, which can then analyze the data to perform comprehensive posture management.

[0095] The adjustment unit can analyze the user's exercise data and propose an optimal rehabilitation plan. For example, the adjustment unit can analyze the user's exercise data and propose an optimal rehabilitation plan. For example, the adjustment unit can propose an individually optimized rehabilitation plan based on the user's exercise data. For example, the adjustment unit can comprehensively analyze the user's exercise data and propose an effective rehabilitation plan. In this way, the adjustment unit can propose an optimal rehabilitation plan by analyzing the user's exercise data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's exercise data into a generating AI, and the generating AI can analyze the data and propose an optimal rehabilitation plan.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The mobility assistance system can also be equipped with a voice recognition unit. The voice recognition unit can recognize the user's voice commands and send instructions to various parts of the system. For example, if the user commands "move forward," the operation unit can move the robot forward. If the user commands "stop," the operation unit can stop the robot. Furthermore, if the user commands "turn right," the operation unit can turn the robot to the right. This allows the user to operate the robot using voice commands, enabling more freedom and safer movement.

[0098] The mobility assistance system can also be equipped with a lighting unit. The lighting unit can automatically turn on and off depending on the ambient light. For example, at night or in dark places, the lighting unit will automatically turn on to ensure the user's visibility. Conversely, during the day or in bright places, the lighting unit will automatically turn off to conserve energy. Furthermore, the lighting unit can also be manually turned on and off by the user. This allows the user to adjust the lighting according to the ambient light, enabling safer movement.

[0099] The mobility assistance system can also be equipped with a navigation unit. The navigation unit can use GPS to determine the user's current location and provide the optimal route to their destination. For example, once the user sets a destination, the navigation unit can calculate the best route and provide voice and visual instructions. Furthermore, the navigation unit can acquire real-time traffic information and suggest routes that avoid congestion. This allows the user to travel to their destination more smoothly.

[0100] The mobility assistance system can also be equipped with an emergency call unit. This unit allows the user to press a button in an emergency, automatically notifying pre-registered contacts. For example, if the user falls or experiences a sudden change in their health, the unit can send an emergency call along with the user's location information. The unit can also monitor the user's health status and automatically send a notification if an abnormality is detected. This ensures that the user receives a prompt response in an emergency.

[0101] The mobility assistance system can also be equipped with an entertainment section. This section provides functions that allow users to enjoy music and audiobooks while traveling. For example, if a user wants to listen to music, the entertainment section can play a playlist tailored to the user's preferences. If a user wants to listen to an audiobook, the entertainment section can play an audiobook of their choice. Furthermore, the entertainment section can also provide users with news and weather forecasts. This allows users to enjoy themselves while traveling.

[0102] The mobility assistance system can further estimate the user's stress level using emotion estimation capabilities and provide a relaxing environment. For example, if the user is experiencing high stress, the system can play relaxing music. If the user is feeling tense, the system can provide guidance to encourage deep breathing. Furthermore, if the user is tired, the system can suggest a break and provide a relaxing posture. This allows the user to reduce stress and travel more comfortably.

[0103] The mobility support system can further utilize emotion estimation capabilities to provide communication tailored to the user's emotions. For example, if the user is feeling lonely, the system can offer an encouraging message. If the user is happy, the system can offer an empathetic message. Furthermore, if the user is sad, the system can offer a comforting message. This allows the user to receive appropriate communication based on their emotions and gain psychological support.

[0104] The mobility assistance system can further adjust the travel route based on the user's emotions using emotion estimation capabilities. For example, if the user is feeling anxious, the system can suggest a safe and well-trafficked route. If the user is relaxed, the system can suggest a scenic route. Furthermore, if the user is in a hurry, the system can suggest the shortest route. This allows the user to choose the optimal travel route according to their emotions.

[0105] The mobility support system can further utilize emotion estimation capabilities to provide health management advice based on the user's emotions. For example, if the user is stressed, the system can offer advice on exercise and diet to help them relax. If the user is tired, the system can offer advice on resting. Furthermore, if the user is feeling energetic, the system can offer advice to encourage positive activity. This allows users to receive health management advice tailored to their emotions.

[0106] The mobility support system can further adjust the entertainment content based on the user's emotions using emotion estimation capabilities. For example, if the user is sad, the system can suggest uplifting music or movies. If the user is happy, the system can provide entertainment that further enhances that emotion. Furthermore, if the user is relaxed, the system can provide relaxing music or videos. This allows the user to enjoy entertainment that is tailored to their emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The sensor unit detects the surrounding environment. The sensor unit can detect pedestrian flow, vehicle movement, and the location of obstacles in real time. Specifically, it uses infrared sensors, ultrasonic sensors, cameras, etc., to detect the surrounding environment. For example, infrared sensors can detect the surrounding environment even at night, ultrasonic sensors can detect the location of obstacles using sound waves, and cameras can detect the surrounding environment in detail using image processing technology. Step 2: The operating unit operates appropriately based on the information detected by the sensor unit. The operating unit controls the robot's movements using motors and actuators. For example, it may change the robot's direction of travel to avoid obstacles detected by the sensor unit, or adjust the robot's speed according to the flow of people. The operating unit can control the robot's movements in real time based on the information detected by the sensor unit. Step 3: The monitoring unit monitors the user's movements and health status. The monitoring unit constantly measures the user's blood pressure and heart rate, and detects the user's movements with sensors for real-time monitoring. Specifically, it can use a blood pressure monitor, heart rate sensor, accelerometer, gyroscope, etc. Step 4: The adjustment unit adjusts to a good or comfortable posture based on the information obtained by the monitoring unit. The adjustment unit uses motors and actuators to adjust the user's posture in real time. For example, it may use a motor to change the angle of the chair or an actuator to adjust the angle of the backrest. The adjustment unit can adjust the user's posture in real time based on the information obtained by the monitoring unit.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the sensor unit, operation unit, monitoring unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the sensor unit detects the surrounding environment using the camera 42 and infrared sensor of the smart device 14, and the control unit 46A monitors the user's movements and health status. The operation unit is implemented in the specific processing unit 290 of the data processing unit 12 and controls the robot's movements based on information from the sensor unit. The monitoring unit is implemented in the specific processing unit 46A of the smart device 14 and measures the user's blood pressure and heart rate. The adjustment unit is implemented in the specific processing unit 290 of the data processing unit 12 and adjusts the user's posture in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the sensor unit, operation unit, monitoring unit, and adjustment unit, is implemented in, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the sensor unit detects the surrounding environment using the camera 42 and infrared sensor of the smart glasses 214, and the control unit 46A monitors the user's movements and health status. The operation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and controls the robot's movements based on information from the sensor unit. The monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214, and measures the user's blood pressure and heart rate. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and adjusts the user's posture in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the sensor unit, operation unit, monitoring unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the sensor unit detects the surrounding environment using the camera 42 and infrared sensor of the headset terminal 314, and the control unit 46A monitors the user's movements and health status. The operation unit is implemented in the specific processing unit 290 of the data processing unit 12 and controls the robot's movements based on information from the sensor unit. The monitoring unit is implemented in the specific processing unit 46A of the headset terminal 314 and measures the user's blood pressure and heart rate. The adjustment unit is implemented in the specific processing unit 290 of the data processing unit 12 and adjusts the user's posture in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the sensor unit, operation unit, monitoring unit, and adjustment unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the sensor unit detects the surrounding environment using the camera 42 and infrared sensor of the robot 414, and the control unit 46A monitors the user's movements and health status. The operation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and controls the robot's movements based on information from the sensor unit. The monitoring unit is implemented, for example, by the control unit 46A of the robot 414, and measures the user's blood pressure and heart rate. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and adjusts the user's posture in real time. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A sensor unit that detects the surrounding environment, An operating unit that operates appropriately based on the information detected by the sensor unit, The monitoring unit monitors the user's movements and health status, The system includes an adjustment unit that adjusts for good posture or a comfortable posture based on information obtained by the monitoring unit. A system characterized by the following features. (Note 2) The aforementioned sensor unit is It detects pedestrian and vehicle movements and the location of obstacles in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The monitoring unit, It continuously measures the user's blood pressure and heart rate, and issues an alert if an abnormality is detected. The system described in Appendix 1, characterized by the features described herein. (Note 4) The adjustment unit is, It learns the user's movements and automatically adjusts to a good or comfortable posture. The system described in Appendix 1, characterized by the features described herein. (Note 5) The adjustment unit is, It learns the user's walking patterns and changes in their physical condition, and provides support accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned sensor unit is The system estimates the user's emotions and adjusts the sensor sensitivity based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned sensor unit is When detecting the surrounding environment, the sensor switches its operating mode according to the weather and time of day. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned sensor unit is The detected information is sent to the cloud in real time and shared with other robots. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned sensor unit is The system estimates the user's emotions and adjusts the sensor's detection range based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned sensor unit is The detected information is sent to the user's smartphone, allowing the user to understand the situation. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned sensor unit is Based on the detected information, the system analyzes surrounding audio information and issues a voice warning to the user. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned operating unit is The robot estimates the user's emotions and adjusts the robot's operating speed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned operating unit is It automatically switches between walking and running modes depending on the surrounding environment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned operating unit is When avoiding obstacles, it calculates the optimal avoidance route and provides the user with safe movement. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned operating unit is The system estimates the user's emotions and modifies the robot's behavior patterns based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned operating unit is Depending on the surrounding environment, it will warn the user with sound or vibration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned operating unit is By coordinating and working together with other robots, it enables multiple users to move safely. The system described in Appendix 1, characterized by the features described herein. (Note 18) The monitoring unit, The system estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The monitoring unit, The system transmits the user's health status to medical institutions in real time, enabling a rapid response in emergencies. The system described in Appendix 1, characterized by the features described herein. (Note 20) The monitoring unit, The system also measures the user's body temperature and respiratory rate to assess their overall health. The system described in Appendix 1, characterized by the features described herein. (Note 21) The monitoring unit, The system estimates the user's emotions and adjusts how monitoring data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The monitoring unit, It also collects data on users' diet and exercise to support their health management. The system described in Appendix 1, characterized by the features described herein. (Note 23) The monitoring unit, It analyzes the user's sleep patterns and suggests the optimal timing for rest. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, The system estimates the user's emotions and adjusts the frequency of posture adjustments based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, It measures the user's muscle tension and provides the optimal posture. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, It learns the user's skeletal and muscular condition and provides a individually optimized posture. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, The system estimates the user's emotions and modifies the posture adjustment method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The adjustment unit is, It also adjusts the user's sitting and standing posture to provide comprehensive posture management. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, We analyze the user's exercise data and propose the optimal rehabilitation plan. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A sensor unit that detects the surrounding environment, An operating unit that operates appropriately based on the information detected by the sensor unit, The monitoring unit monitors the user's movements and health status, The system includes an adjustment unit that adjusts for good posture or a comfortable posture based on information obtained by the monitoring unit. A system characterized by the following features.

2. The aforementioned sensor unit is It detects pedestrian and vehicle movements and the location of obstacles in real time. The system according to feature 1.

3. The monitoring unit, It continuously measures the user's blood pressure and heart rate, and issues an alert if an abnormality is detected. The system according to feature 1.

4. The adjustment unit is, It learns the user's movements and automatically adjusts to a good or comfortable posture. The system according to feature 1.

5. The adjustment unit is, It learns the user's walking patterns and changes in their physical condition, and provides support accordingly. The system according to feature 1.

6. The aforementioned sensor unit is The system estimates the user's emotions and adjusts the sensor sensitivity based on those emotions. The system according to feature 1.

7. The aforementioned sensor unit is When detecting the surrounding environment, the sensor switches its operating mode according to the weather and time of day. The system according to feature 1.

8. The aforementioned sensor unit is The detected information is sent to the cloud in real time and shared with other robots. The system according to feature 1.

9. The aforementioned sensor unit is The system estimates the user's emotions and adjusts the sensor's detection range based on those emotions. The system according to feature 1.

10. The aforementioned sensor unit is The detected information is sent to the user's smartphone, allowing the user to understand the situation. The system according to feature 1.

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