system

Abstract

The system according to this embodiment aims to support the independent mobility of the elderly and people with disabilities. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a control unit, and a monitoring unit. The reception unit receives destination information from the user. The generation unit calculates the optimal route based on the destination information received by the reception unit. The control unit controls the autonomous wheelchair or mobile robot based on the route calculated by the generation unit. The monitoring unit monitors the surrounding environment while the autonomous wheelchair or mobile robot controlled by the control unit is moving and avoids obstacles.

Description

Technical Field

[0006] , , , ,

[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 in 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

[0007] The system according to this embodiment can support the independent mobility of elderly people and people with disabilities. [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, etc. The communication I / F manages communication between multiple 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 receiving 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 receiving 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 AI ​​mobility support agent system according to an embodiment of the present invention is a next-generation agent designed to solve the problem of labor shortages in the nursing care industry. This AI mobility support agent system controls autonomous wheelchairs and mobile robots to support the independent mobility of the elderly and people with disabilities, enabling safe and comfortable movement for users. It provides multifunctional support, including navigation inside and outside facilities, obstacle avoidance, and guidance to destinations. First, the user inputs the destination they wish to go to. Next, the AI ​​analyzes the input destination information and calculates the optimal route. Based on the calculated route, the autonomous wheelchair or mobile robot begins to move. During movement, the AI ​​monitors the surrounding environment with sensors and guides the user safely to the destination while avoiding obstacles. For example, when moving within a facility, if the user wants to move from their room to the dining room, the AI ​​calculates the optimal route, and the autonomous wheelchair moves according to that route. If an obstacle appears during movement, the AI ​​immediately takes evasive action. Also, when moving outside a facility, if the user wants to take a walk to the park, the AI ​​calculates the route to the park, and the mobile robot moves according to that route. This mechanism reduces the burden of mobility assistance on nursing staff and enables users to move independently. Furthermore, AI-assisted mobility enhances user safety and enables more comfortable travel. This allows AI mobility support agent systems to address the labor shortage in the caregiving industry and support independent mobility for the elderly and people with disabilities.

[0029] The AI ​​mobility support agent system according to this embodiment comprises a reception unit, a generation unit, a control unit, and a monitoring unit. The reception unit receives destination information from the user. The reception unit provides, for example, an interface for the user to input the destination they wish to travel to. The generation unit calculates the optimal route based on the destination information received by the reception unit. The generation unit calculates the optimal route based on criteria such as distance, time, and the presence or absence of obstacles, for example, using AI. The control unit controls the autonomous wheelchair or mobile robot based on the route calculated by the generation unit. The control unit controls the movement of the autonomous wheelchair or mobile robot, for example, using AI. The monitoring unit monitors the surrounding environment while the autonomous wheelchair or mobile robot controlled by the control unit is moving and avoids obstacles. The monitoring unit monitors the surrounding environment using sensors and detects obstacles, for example, using AI. Thus, the AI ​​mobility support agent system according to this embodiment receives destination information from the user, calculates the optimal route, controls the autonomous wheelchair or mobile robot, and monitors the surrounding environment to avoid obstacles, thereby achieving safe and comfortable travel.

[0030] The reception desk receives the user's destination information. For example, the reception desk provides an interface for the user to input the destination they wish to travel to. Specifically, the reception desk is equipped with a touchscreen and a voice recognition system, designed to allow users to intuitively input their destination. On the touchscreen, users can select a destination on a map or input addresses and facility names using a keyboard. The voice recognition system allows users to specify destinations by speaking, which is particularly convenient for visually impaired or hand-impaired users. Furthermore, the reception desk learns the user's past travel history and preferences, and has a function to suggest frequently visited places and preferred routes. For example, it can automatically list places the user frequently visits, such as hospitals and supermarkets, and allow them to be selected with a single touch. The reception desk can also automatically acquire the user's current location using GPS or Wi-Fi location information and set it as the starting point. This allows users to easily start traveling simply by entering their destination. The reception desk also has a function to analyze the user's input in real time and prompt appropriate corrections or confirmations if there are errors or unclear information. For example, if the destination address is incomplete, the system will list possible addresses and prompt the user to select one. Furthermore, the voice recognition system can learn the characteristics of the user's voice and use an individually optimized voice recognition model to improve recognition accuracy. This allows the reception desk to enable users to easily and accurately input destination information, resulting in smoother travel assistance.

[0031] The generation unit calculates the optimal route based on destination information received by the reception unit. For example, the generation unit uses AI to calculate the optimal route based on criteria such as distance, time, and the presence of obstacles. Specifically, the generation unit implements a route search algorithm using deep learning, analyzing vast amounts of map data and traffic information to derive the optimal route. The generation unit incorporates real-time updated traffic and weather information, calculating routes while considering the effects of congestion, road construction, and bad weather. For example, it may suggest routes that avoid slippery roads during rainy weather, or calculate detour routes when traffic congestion occurs. Furthermore, the generation unit can optimize routes considering the user's individual needs and constraints. For example, for wheelchair users, it prioritizes barrier-free routes and avoids stairs and steep slopes. In addition, the generation unit can learn the user's past travel history and preferences to suggest the optimal route for that user. For example, it customizes the optimal route considering routes the user has used in the past and their preferred modes of transportation. The generation unit also has a function to present multiple route options, allowing the user to choose. This allows users to select the optimal route according to their preferences and circumstances. The generation unit visually displays the calculated route, allowing users to review route details. For example, it displays the route on a map and provides information such as major landmarks, intersections, and travel time. This enables the generation unit to quickly and accurately calculate the optimal route for the user, supporting safe and comfortable travel.

[0032] The control unit controls the autonomous wheelchair or mobile robot based on the route calculated by the generation unit. For example, the control unit uses AI to control the movement of the autonomous wheelchair or mobile robot. Specifically, based on the route information provided by the generation unit, the control unit controls the motors and steering of the wheelchair or robot to achieve movement to the destination. The control unit has the ability to monitor the surrounding environment in real time and detect and avoid obstacles and pedestrians. For example, it uses cameras and LiDAR sensors mounted on the wheelchair or robot to constantly understand the surrounding situation and automatically stops or selects an alternative route if an obstacle appears. Furthermore, to ensure user safety, the control unit implements control algorithms to avoid sudden acceleration and deceleration, enabling smooth movement. For example, even if the user suddenly changes direction, the control unit controls the wheelchair or robot to follow a smooth curve, allowing the user to move comfortably. In addition, the control unit has the ability to start and stop movement in response to user instructions. For example, the user can instruct movement using a remote control or smartphone app, and the control unit controls the wheelchair or robot according to those instructions. The control unit can also adjust the movement speed and route according to the user's physical condition and circumstances. For example, if the user is tired, the control unit will take measures such as slowing down the movement speed or providing rest points. This allows the control unit to implement movement control that prioritizes the user's safety and comfort, providing a safe and secure environment for travel.

[0033] The monitoring unit monitors the surrounding environment and avoids obstacles while autonomous wheelchairs and mobile robots controlled by the control unit are moving. For example, the monitoring unit uses AI and sensors to monitor the surrounding environment and detect obstacles. Specifically, the monitoring unit uses a combination of multiple sensors, such as cameras, LiDAR, and ultrasonic sensors, to accurately understand the surrounding situation. Data obtained from these sensors is analyzed in real time by AI to accurately identify the location and movement of obstacles. For example, it detects pedestrians and other vehicles from camera footage and obtains distance information from LiDAR sensors to measure the distance to obstacles. Ultrasonic sensors are used to detect obstacles at close range with high accuracy. The monitoring unit integrates the information obtained from these sensors to create a three-dimensional model of the surrounding environment. This allows the monitoring unit to understand the location and movement of obstacles in real time and take appropriate avoidance actions. For example, if an obstacle appears ahead, the monitoring unit instructs the control unit to stop or detour, safely avoiding the obstacle. The monitoring unit can also respond quickly to changes in the surrounding environment. For example, the monitoring unit can grasp the situation in real time and take appropriate action in the event of sudden obstacles or changes in weather. Furthermore, the monitoring unit is equipped with an anomaly detection function to ensure user safety. For instance, if a wheelchair or robot behaves abnormally, or if an anomaly is detected by a sensor, the monitoring unit will immediately issue a warning and stop movement if necessary. In this way, the monitoring unit provides monitoring functions that prioritize user safety, creating an environment where users can move with peace of mind.

[0034] The monitoring unit can monitor the surrounding environment using sensors and detect obstacles. The monitoring unit monitors the surrounding environment and detects obstacles using sensors such as cameras, LiDAR, and ultrasonic sensors. This enables safe movement by monitoring the surrounding environment using sensors and detecting obstacles. 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 acquired by sensors into a generating AI and have the generating AI perform obstacle detection.

[0035] The monitoring unit can take immediate evasive action when it detects an obstacle. For example, when the monitoring unit detects an obstacle, it takes immediate evasive action based on the timing and method of evasion. This ensures safe movement by taking immediate evasive action when an obstacle 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 obstacle detection data into a generating AI and cause the generating AI to execute the evasive action.

[0036] The control unit can control movement inside and outside the facility. For example, to control movement within the facility, the control unit uses map data of the building and facility layout information. The control unit can also use GPS data and map information to control movement outside the facility. This allows the control unit to meet the diverse movement needs of users by controlling movement inside and outside the facility. Some or all of the above-described processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input movement data inside and outside the facility into a generating AI and have the generating AI perform movement control.

[0037] The generation unit can calculate the optimal route within the facility. For example, the generation unit uses AI to calculate the optimal route within the facility based on criteria such as distance, time, and the presence or absence of obstacles. This enables efficient movement by calculating the optimal route within the facility. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input map data of the facility into a generation AI and have the generation AI perform the calculation of the optimal route.

[0038] The generation unit can calculate the optimal route outside the facility. For example, the generation unit uses AI to calculate the optimal route outside the facility based on criteria such as distance, time, and the presence or absence of obstacles. This enables efficient travel by calculating the optimal route outside the facility. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input map data from outside the facility into the generation AI and have the generation AI perform the calculation of the optimal route.

[0039] The reception unit allows the user to input the destination they wish to travel to. The reception unit provides, for example, an interface for the user to input the destination they wish to travel to. By doing so, the system calculates the optimal route and supports the travel based on the user's input of the destination. Some or all of the above-described processes in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's destination information into a generating AI and have the generating AI perform the destination input.

[0040] The control unit may include a provision unit that provides the user with information on the progress of the movement. For example, the control unit displays the current location, estimated time of arrival, route information, etc., to provide the user with information on the progress of the movement. This allows the user to understand the current status of their movement. Some or all of the above-described processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input movement progress data into a generating AI and have the generating AI perform the provision of progress information.

[0041] The reception desk can analyze the user's past travel history and suggest destinations when the user enters a destination. For example, the reception desk can automatically display places the user has frequently visited in the past as suggestions. The reception desk can also predict places the user will visit on specific days of the week or times of day and suggest them as suggestions. Furthermore, the reception desk can analyze the user's past travel patterns and suggest the most suitable destinations. This makes it easier for the user to select a destination by analyzing the user's past travel history and suggesting destinations when the user enters a destination. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past travel data into a generating AI and have the generating AI perform the task of suggesting destinations.

[0042] The reception desk can suggest the most suitable destination based on the user's current health and physical condition when the user enters their destination. For example, if the user is tired, the reception desk can suggest a nearby rest area or cafe. It can also suggest a park or route suitable for a walk if the user is seeking healthy exercise. Furthermore, if the user is feeling unwell, the reception desk can suggest the nearest medical facility or pharmacy. This allows the user to select an appropriate destination by suggesting the most suitable destination based on their health and physical condition. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's health data into a generating AI and have the generating AI suggest the most suitable destination.

[0043] The reception desk can prioritize suggesting highly relevant destinations when a user enters a destination, taking into account the user's geographical location. For example, the reception desk can prioritize suggesting locations close to the user's current location. Furthermore, if the user is in a specific area, the reception desk can also suggest popular spots within that area. Additionally, if the user is traveling a specific route, the reception desk can prioritize suggesting destinations along that route. This makes it easier for the user to select a destination by prioritizing highly relevant destinations based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant destinations.

[0044] The reception desk can analyze the user's social media activity when a destination is entered and suggest relevant destinations. For example, the reception desk can suggest relevant destinations based on places the user has checked into on social media. It can also suggest relevant destinations based on places and events the user follows on social media. Furthermore, it can suggest relevant destinations based on photos and posts the user has shared on social media. This allows the user to select destinations that interest them by analyzing their social media activity and suggesting relevant destinations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant destinations.

[0045] The generation unit can predict the optimal route by referring to past travel data during route calculation. For example, the generation unit can propose the optimal route based on routes previously used by the user. It can also propose routes that avoid congestion based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and propose the most efficient route. This enables efficient travel by predicting the optimal route by referring to past travel data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past travel data into a generation AI and have the generation AI perform the prediction of the optimal route.

[0046] The generation unit can adjust the difficulty of the route based on the user's health and physical condition when calculating the route. For example, if the user is tired, the generation unit may suggest a flat and short route. It may also suggest a slightly longer route if the user is seeking healthy exercise. Furthermore, if the user is unwell, the generation unit may suggest a route that includes rest stops. This allows the user to select an appropriate route by adjusting the difficulty based on their health and physical condition. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health data into a generation AI and have the generation AI adjust the route difficulty.

[0047] The generation unit can calculate the optimal route by considering the user's geographical location information during route calculation. For example, the generation unit can prioritize suggesting routes that are close to the user's current location. Furthermore, if the user is in a specific area, the generation unit can suggest the optimal route within that area. Additionally, if the user is traveling along a specific route, the generation unit can suggest the optimal route along that route. This enables efficient travel by calculating the optimal route while considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI perform the calculation of the optimal route.

[0048] The generation unit can analyze the user's social media activity and suggest relevant routes when calculating routes. For example, the generation unit can suggest relevant routes based on places the user has checked in to on social media. It can also suggest relevant routes based on places and events the user follows on social media. Furthermore, it can suggest relevant routes based on photos and posts the user has shared on social media. This allows the user to select routes that interest them by analyzing their social media activity and suggesting relevant routes. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI perform the task of suggesting relevant routes.

[0049] The control unit can monitor the user's health and physical condition during travel and adjust the movement as needed. For example, if the user is tired, the control unit can slow down the travel speed. Conversely, if the user is seeking healthy exercise, the control unit can also increase the travel speed. Furthermore, if the user is unwell, the control unit can temporarily suspend travel and prompt them to rest. This allows the user to travel safely by adjusting the movement according to their health and physical condition. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's health data into a generating AI and have the generating AI perform the adjustments to the movement.

[0050] The control unit can select the optimal mode of travel by referring to the user's past travel history while the user is traveling. For example, the control unit can suggest the optimal mode of travel based on the user's past travel history. The control unit can also suggest a mode of travel that avoids congestion based on the user's past travel history. Furthermore, the control unit can analyze the user's past travel history and suggest the most efficient mode of travel. This enables efficient travel by selecting the optimal mode of travel by referring to the user's past travel history. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input past travel data into a generating AI and have the generating AI select the optimal mode of travel.

[0051] The control unit can select the optimal mode of transport while the user is in transit, taking into account the user's geographical location. For example, the control unit may prioritize suggesting modes of transport that are closest to the user's current location. Furthermore, if the user is in a specific area, the control unit can suggest the optimal mode of transport within that area. Additionally, if the user is traveling along a specific route, the control unit can suggest the optimal mode of transport along that route. This enables efficient travel by selecting the optimal mode of transport while considering the user's geographical location. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For instance, the control unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal mode of transport.

[0052] The control unit can analyze the user's social media activity while they are traveling and suggest relevant travel methods. For example, the control unit can suggest relevant travel methods based on places the user has checked into on social media. It can also suggest relevant travel methods based on places and events the user follows on social media. Furthermore, it can suggest relevant travel methods based on photos and posts the user has shared on social media. This allows the user to select a travel method that interests them by analyzing their social media activity and suggesting relevant travel methods. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's social media data into a generating AI and have the generating AI suggest relevant travel methods.

[0053] The monitoring unit can improve the accuracy of obstacle avoidance by referring to past obstacle data during monitoring. For example, the monitoring unit optimizes the current obstacle avoidance route based on the location information of previously detected obstacles. The monitoring unit can also analyze past obstacle data and predict and avoid frequently occurring obstacle patterns. Furthermore, the monitoring unit can refer to past obstacle data and calculate avoidance routes considering the obstacle occurrence rate at specific times and locations. This improves the accuracy of obstacle avoidance by referring to past obstacle data, thereby ensuring safe movement. 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 past obstacle data into a generating AI and have the generating AI perform the task of improving the accuracy of obstacle avoidance.

[0054] The monitoring unit can monitor the user's health and physical condition during monitoring and adjust the monitoring method as needed. For example, if the user is tired, the monitoring unit can increase the frequency of monitoring and provide more detailed information. If the user is healthy, the monitoring unit can also broaden the scope of monitoring and provide more comprehensive information. Furthermore, if the user is unwell, the monitoring unit can improve the accuracy of monitoring and provide information more quickly. This allows the user to travel safely by adjusting the monitoring method according to their health and physical condition. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the user's health data into a generating AI and have the generating AI adjust the monitoring method.

[0055] The monitoring unit can select the optimal monitoring method while monitoring, taking into account the user's geographical location information. For example, the monitoring unit may prioritize monitoring locations close to the user's current location. Furthermore, if the user is in a specific area, the monitoring unit can prioritize monitoring important information within that area. Additionally, if the user is traveling a specific route, the monitoring unit can prioritize monitoring important information along that route. This ensures safe travel by selecting the optimal monitoring method based on the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For instance, the monitoring unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal monitoring method.

[0056] The monitoring unit can analyze the user's social media activity during monitoring and propose relevant monitoring methods. For example, the monitoring unit can propose relevant monitoring methods based on the locations the user has checked into on social media. It can also propose relevant monitoring methods based on the locations and events the user follows on social media. Furthermore, the monitoring unit can propose relevant monitoring methods based on the photos and posts the user has shared on social media. In this way, by analyzing the user's social media activity and proposing relevant monitoring methods, it can provide information that the user is interested in. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the user's social media data into a generating AI and have the generating AI execute suggestions for relevant monitoring methods.

[0057] The service provider can select the optimal display method when providing information on the progress of travel by referring to the user's past travel history. For example, the service provider can suggest the optimal display method for the progress based on the display method the user has preferred in the past. The service provider can also suggest a display method that avoids congestion based on the user's past travel history. Furthermore, the service provider can analyze the user's past travel history and suggest the most efficient display method. This allows the user to travel efficiently by selecting the optimal display method by referring to the user's past travel history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past travel data into a generating AI and have the generating AI select the optimal display method.

[0058] The service provider can update and display the user's current location information in real time when providing information on the progress of movement. For example, the service provider can update the user's current location in real time and display the progress while the user is moving. The service provider can also update the current location in real time and display the optimal progress as the user approaches their destination. Furthermore, if the user gets lost, the service provider can update the current location in real time and display the progress again. This allows the user to understand their current movement status by updating and displaying their current location information in real time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input current location data into a generating AI and have the generating AI perform real-time updates and displays.

[0059] The service provider can select the optimal display method when providing information on the progress of movement, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the user to move efficiently by selecting the optimal display method considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0060] The service provider can provide multilingual displays according to the user's language settings when providing movement progress. For example, the service provider can automatically set the language of the progress based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, if the service provider selects a specific language, the service provider can provide the progress in that language. This allows the user to move efficiently by providing multilingual displays according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language setting data into a generating AI and have the generating AI execute the multilingual display.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The monitoring unit can monitor the user's health status and issue alerts if abnormalities are detected. For example, it can measure the user's heart rate and blood pressure with sensors and display a warning if abnormal values ​​are detected. The monitoring unit can also analyze the user's walking pattern and issue a warning if there is a high risk of falling. Furthermore, the monitoring unit can measure the user's body temperature and issue an alert if a fever is detected. This allows for real-time monitoring of the user's health status and a rapid response when abnormalities occur.

[0063] The generation unit can analyze the user's past travel history and propose the optimal travel route. For example, it can prioritize suggesting routes that the user has frequently used in the past. Furthermore, the generation unit can suggest routes that avoid congestion based on the user's past travel history. In addition, the generation unit can analyze the user's past travel history and propose the most efficient route. This allows for efficient travel by suggesting the optimal route based on the user's past travel history.

[0064] The monitoring unit can monitor the user's health status and issue alerts if abnormalities are detected. For example, it can measure the user's heart rate and blood pressure with sensors and display a warning if abnormal values ​​are detected. The monitoring unit can also analyze the user's walking pattern and issue a warning if there is a high risk of falling. Furthermore, the monitoring unit can measure the user's body temperature and issue an alert if a fever is detected. This allows for real-time monitoring of the user's health status and a rapid response when abnormalities occur.

[0065] The generation unit can analyze the user's past travel history and propose the optimal travel route. For example, it can prioritize suggesting routes that the user has frequently used in the past. Furthermore, the generation unit can suggest routes that avoid congestion based on the user's past travel history. In addition, the generation unit can analyze the user's past travel history and propose the most efficient route. This allows for efficient travel by suggesting the optimal route based on the user's past travel history.

[0066] The monitoring unit can monitor the user's health status and issue alerts if abnormalities are detected. For example, it can measure the user's heart rate and blood pressure with sensors and display a warning if abnormal values ​​are detected. The monitoring unit can also analyze the user's walking pattern and issue a warning if there is a high risk of falling. Furthermore, the monitoring unit can measure the user's body temperature and issue an alert if a fever is detected. This allows for real-time monitoring of the user's health status and a rapid response when abnormalities occur.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception unit receives the user's destination information. The reception unit provides an interface for the user to input the destination they wish to travel to, for example. Step 2: The generation unit calculates the optimal route based on the destination information received by the reception unit. The generation unit calculates the optimal route based on criteria such as distance, time, and the presence or absence of obstacles, for example, using AI. Step 3: The control unit controls the autonomous wheelchair or mobile robot based on the route calculated by the generation unit. The control unit controls the movement of the autonomous wheelchair or mobile robot, for example, using AI. Step 4: The monitoring unit monitors the surrounding environment while the autonomous wheelchair or mobile robot controlled by the control unit is moving, and avoids obstacles. The monitoring unit, for example, uses AI and sensors to monitor the surrounding environment and detect obstacles.

[0069] (Example of form 2) The AI ​​mobility support agent system according to an embodiment of the present invention is a next-generation agent designed to solve the problem of labor shortages in the nursing care industry. This AI mobility support agent system controls autonomous wheelchairs and mobile robots to support the independent mobility of the elderly and people with disabilities, enabling safe and comfortable movement for users. It provides multifunctional support, including navigation inside and outside facilities, obstacle avoidance, and guidance to destinations. First, the user inputs the destination they wish to go to. Next, the AI ​​analyzes the input destination information and calculates the optimal route. Based on the calculated route, the autonomous wheelchair or mobile robot begins to move. During movement, the AI ​​monitors the surrounding environment with sensors and guides the user safely to the destination while avoiding obstacles. For example, when moving within a facility, if the user wants to move from their room to the dining room, the AI ​​calculates the optimal route, and the autonomous wheelchair moves according to that route. If an obstacle appears during movement, the AI ​​immediately takes evasive action. Also, when moving outside a facility, if the user wants to take a walk to the park, the AI ​​calculates the route to the park, and the mobile robot moves according to that route. This mechanism reduces the burden of mobility assistance on nursing staff and enables users to move independently. Furthermore, AI-assisted mobility enhances user safety and enables more comfortable travel. This allows AI mobility support agent systems to address the labor shortage in the caregiving industry and support independent mobility for the elderly and people with disabilities.

[0070] The AI ​​mobility support agent system according to this embodiment comprises a reception unit, a generation unit, a control unit, and a monitoring unit. The reception unit receives destination information from the user. The reception unit provides, for example, an interface for the user to input the destination they wish to travel to. The generation unit calculates the optimal route based on the destination information received by the reception unit. The generation unit calculates the optimal route based on criteria such as distance, time, and the presence or absence of obstacles, for example, using AI. The control unit controls the autonomous wheelchair or mobile robot based on the route calculated by the generation unit. The control unit controls the movement of the autonomous wheelchair or mobile robot, for example, using AI. The monitoring unit monitors the surrounding environment while the autonomous wheelchair or mobile robot controlled by the control unit is moving and avoids obstacles. The monitoring unit monitors the surrounding environment using sensors and detects obstacles, for example, using AI. Thus, the AI ​​mobility support agent system according to this embodiment receives destination information from the user, calculates the optimal route, controls the autonomous wheelchair or mobile robot, and monitors the surrounding environment to avoid obstacles, thereby achieving safe and comfortable travel.

[0071] The reception desk receives the user's destination information. For example, the reception desk provides an interface for the user to input the destination they wish to travel to. Specifically, the reception desk is equipped with a touchscreen and a voice recognition system, designed to allow users to intuitively input their destination. On the touchscreen, users can select a destination on a map or input addresses and facility names using a keyboard. The voice recognition system allows users to specify destinations by speaking, which is particularly convenient for visually impaired or hand-impaired users. Furthermore, the reception desk learns the user's past travel history and preferences, and has a function to suggest frequently visited places and preferred routes. For example, it can automatically list places the user frequently visits, such as hospitals and supermarkets, and allow them to be selected with a single touch. The reception desk can also automatically acquire the user's current location using GPS or Wi-Fi location information and set it as the starting point. This allows users to easily start traveling simply by entering their destination. The reception desk also has a function to analyze the user's input in real time and prompt appropriate corrections or confirmations if there are errors or unclear information. For example, if the destination address is incomplete, the system will list possible addresses and prompt the user to select one. Furthermore, the voice recognition system can learn the characteristics of the user's voice and use an individually optimized voice recognition model to improve recognition accuracy. This allows the reception desk to enable users to easily and accurately input destination information, resulting in smoother travel assistance.

[0072] The generation unit calculates the optimal route based on destination information received by the reception unit. For example, the generation unit uses AI to calculate the optimal route based on criteria such as distance, time, and the presence of obstacles. Specifically, the generation unit implements a route search algorithm using deep learning, analyzing vast amounts of map data and traffic information to derive the optimal route. The generation unit incorporates real-time updated traffic and weather information, calculating routes while considering the effects of congestion, road construction, and bad weather. For example, it may suggest routes that avoid slippery roads during rainy weather, or calculate detour routes when traffic congestion occurs. Furthermore, the generation unit can optimize routes considering the user's individual needs and constraints. For example, for wheelchair users, it prioritizes barrier-free routes and avoids stairs and steep slopes. In addition, the generation unit can learn the user's past travel history and preferences to suggest the optimal route for that user. For example, it customizes the optimal route considering routes the user has used in the past and their preferred modes of transportation. The generation unit also has a function to present multiple route options, allowing the user to choose. This allows users to select the optimal route according to their preferences and circumstances. The generation unit visually displays the calculated route, allowing users to review route details. For example, it displays the route on a map and provides information such as major landmarks, intersections, and travel time. This enables the generation unit to quickly and accurately calculate the optimal route for the user, supporting safe and comfortable travel.

[0073] The control unit controls the autonomous wheelchair or mobile robot based on the route calculated by the generation unit. For example, the control unit uses AI to control the movement of the autonomous wheelchair or mobile robot. Specifically, based on the route information provided by the generation unit, the control unit controls the motors and steering of the wheelchair or robot to achieve movement to the destination. The control unit has the ability to monitor the surrounding environment in real time and detect and avoid obstacles and pedestrians. For example, it uses cameras and LiDAR sensors mounted on the wheelchair or robot to constantly understand the surrounding situation and automatically stops or selects an alternative route if an obstacle appears. Furthermore, to ensure user safety, the control unit implements control algorithms to avoid sudden acceleration and deceleration, enabling smooth movement. For example, even if the user suddenly changes direction, the control unit controls the wheelchair or robot to follow a smooth curve, allowing the user to move comfortably. In addition, the control unit has the ability to start and stop movement in response to user instructions. For example, the user can instruct movement using a remote control or smartphone app, and the control unit controls the wheelchair or robot according to those instructions. The control unit can also adjust the movement speed and route according to the user's physical condition and circumstances. For example, if the user is tired, the control unit will take measures such as slowing down the movement speed or providing rest points. This allows the control unit to implement movement control that prioritizes the user's safety and comfort, providing a safe and secure environment for travel.

[0074] The monitoring unit monitors the surrounding environment and avoids obstacles while autonomous wheelchairs and mobile robots controlled by the control unit are moving. For example, the monitoring unit uses AI and sensors to monitor the surrounding environment and detect obstacles. Specifically, the monitoring unit uses a combination of multiple sensors, such as cameras, LiDAR, and ultrasonic sensors, to accurately understand the surrounding situation. Data obtained from these sensors is analyzed in real time by AI to accurately identify the location and movement of obstacles. For example, it detects pedestrians and other vehicles from camera footage and obtains distance information from LiDAR sensors to measure the distance to obstacles. Ultrasonic sensors are used to detect obstacles at close range with high accuracy. The monitoring unit integrates the information obtained from these sensors to create a three-dimensional model of the surrounding environment. This allows the monitoring unit to understand the location and movement of obstacles in real time and take appropriate avoidance actions. For example, if an obstacle appears ahead, the monitoring unit instructs the control unit to stop or detour, safely avoiding the obstacle. The monitoring unit can also respond quickly to changes in the surrounding environment. For example, the monitoring unit can grasp the situation in real time and take appropriate action in the event of sudden obstacles or changes in weather. Furthermore, the monitoring unit is equipped with an anomaly detection function to ensure user safety. For instance, if a wheelchair or robot behaves abnormally, or if an anomaly is detected by a sensor, the monitoring unit will immediately issue a warning and stop movement if necessary. In this way, the monitoring unit provides monitoring functions that prioritize user safety, creating an environment where users can move with peace of mind.

[0075] The monitoring unit can monitor the surrounding environment using sensors and detect obstacles. The monitoring unit monitors the surrounding environment and detects obstacles using sensors such as cameras, LiDAR, and ultrasonic sensors. This enables safe movement by monitoring the surrounding environment using sensors and detecting obstacles. 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 acquired by sensors into a generating AI and have the generating AI perform obstacle detection.

[0076] The monitoring unit can take immediate evasive action when it detects an obstacle. For example, when the monitoring unit detects an obstacle, it takes immediate evasive action based on the timing and method of evasion. This ensures safe movement by taking immediate evasive action when an obstacle 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 obstacle detection data into a generating AI and cause the generating AI to execute the evasive action.

[0077] The control unit can control movement inside and outside the facility. For example, to control movement within the facility, the control unit uses map data of the building and facility layout information. The control unit can also use GPS data and map information to control movement outside the facility. This allows the control unit to meet the diverse movement needs of users by controlling movement inside and outside the facility. Some or all of the above-described processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input movement data inside and outside the facility into a generating AI and have the generating AI perform movement control.

[0078] The generation unit can calculate the optimal route within the facility. For example, the generation unit uses AI to calculate the optimal route within the facility based on criteria such as distance, time, and the presence or absence of obstacles. This enables efficient movement by calculating the optimal route within the facility. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input map data of the facility into a generation AI and have the generation AI perform the calculation of the optimal route.

[0079] The generation unit can calculate the optimal route outside the facility. For example, the generation unit uses AI to calculate the optimal route outside the facility based on criteria such as distance, time, and the presence or absence of obstacles. This enables efficient travel by calculating the optimal route outside the facility. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input map data from outside the facility into the generation AI and have the generation AI perform the calculation of the optimal route.

[0080] The reception unit allows the user to input the destination they wish to travel to. The reception unit provides, for example, an interface for the user to input the destination they wish to travel to. By doing so, the system calculates the optimal route and supports the travel based on the user's input of the destination. Some or all of the above-described processes in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's destination information into a generating AI and have the generating AI perform the destination input.

[0081] The control unit may include a provision unit that provides the user with information on the progress of the movement. For example, the control unit displays the current location, estimated time of arrival, route information, etc., to provide the user with information on the progress of the movement. This allows the user to understand the current status of their movement. Some or all of the above-described processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input movement progress data into a generating AI and have the generating AI perform the provision of progress information.

[0082] The reception desk can estimate the user's emotions and adjust the destination input interface based on the estimated emotions. For example, if the user is feeling anxious, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick destination input. This allows the user to comfortably input their destination by adjusting the destination input interface according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The reception desk can analyze the user's past travel history and suggest destinations when the user enters a destination. For example, the reception desk can automatically display places the user has frequently visited in the past as suggestions. The reception desk can also predict places the user will visit on specific days of the week or times of day and suggest them as suggestions. Furthermore, the reception desk can analyze the user's past travel patterns and suggest the most suitable destinations. This makes it easier for the user to select a destination by analyzing the user's past travel history and suggesting destinations when the user enters a destination. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past travel data into a generating AI and have the generating AI perform the task of suggesting destinations.

[0084] The reception desk can suggest the most suitable destination based on the user's current health and physical condition when the user enters their destination. For example, if the user is tired, the reception desk can suggest a nearby rest area or cafe. It can also suggest a park or route suitable for a walk if the user is seeking healthy exercise. Furthermore, if the user is feeling unwell, the reception desk can suggest the nearest medical facility or pharmacy. This allows the user to select an appropriate destination by suggesting the most suitable destination based on their health and physical condition. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's health data into a generating AI and have the generating AI suggest the most suitable destination.

[0085] The reception desk can estimate the user's emotions and determine the priority of destination input based on the estimated emotions. For example, if the user is feeling stressed, the reception desk will prioritize suggesting places where the user can relax. It can also prioritize suggesting the nearest destination if the user is in a hurry. Furthermore, if the user is enjoying themselves, it can prioritize suggesting tourist attractions and entertainment facilities. This allows the user to comfortably select a destination by prioritizing destination input according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The reception desk can prioritize suggesting highly relevant destinations when a user enters a destination, taking into account the user's geographical location. For example, the reception desk can prioritize suggesting locations close to the user's current location. Furthermore, if the user is in a specific area, the reception desk can also suggest popular spots within that area. Additionally, if the user is traveling a specific route, the reception desk can prioritize suggesting destinations along that route. This makes it easier for the user to select a destination by prioritizing highly relevant destinations based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant destinations.

[0087] The reception desk can analyze the user's social media activity when a destination is entered and suggest relevant destinations. For example, the reception desk can suggest relevant destinations based on places the user has checked into on social media. It can also suggest relevant destinations based on places and events the user follows on social media. Furthermore, it can suggest relevant destinations based on photos and posts the user has shared on social media. This allows the user to select destinations that interest them by analyzing their social media activity and suggesting relevant destinations. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant destinations.

[0088] The generation unit can estimate the user's emotions and adjust the route calculation algorithm based on the estimated emotions. For example, if the user is relaxed, the generation unit may use an algorithm that prioritizes scenic routes. If the user is in a hurry, the generation unit may also use an algorithm that prioritizes the shortest route. Furthermore, if the user is feeling anxious, the generation unit may use an algorithm that prioritizes safety routes. By adjusting the route calculation algorithm according to the user's emotions, the user can travel comfortably. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the route calculation algorithm.

[0089] The generation unit can predict the optimal route by referring to past travel data during route calculation. For example, the generation unit can propose the optimal route based on routes previously used by the user. It can also propose routes that avoid congestion based on the user's past travel history. Furthermore, the generation unit can analyze the user's past travel history and propose the most efficient route. This enables efficient travel by predicting the optimal route by referring to past travel data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past travel data into a generation AI and have the generation AI perform the prediction of the optimal route.

[0090] The generation unit can adjust the difficulty of the route based on the user's health and physical condition when calculating the route. For example, if the user is tired, the generation unit may suggest a flat and short route. It may also suggest a slightly longer route if the user is seeking healthy exercise. Furthermore, if the user is unwell, the generation unit may suggest a route that includes rest stops. This allows the user to select an appropriate route by adjusting the difficulty based on their health and physical condition. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health data into a generation AI and have the generation AI adjust the route difficulty.

[0091] The generation unit can estimate the user's emotions and determine the priority of route calculations based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize suggesting relaxing routes. It can also prioritize suggesting the shortest route if the user is in a hurry. Furthermore, if the user is enjoying themselves, the generation unit can prioritize suggesting routes that pass through tourist attractions and entertainment facilities. This allows the user to travel comfortably by prioritizing route calculations according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a 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 generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI determine the priority of route calculations.

[0092] The generation unit can calculate the optimal route by considering the user's geographical location information during route calculation. For example, the generation unit can prioritize suggesting routes that are close to the user's current location. Furthermore, if the user is in a specific area, the generation unit can suggest the optimal route within that area. Additionally, if the user is traveling along a specific route, the generation unit can suggest the optimal route along that route. This enables efficient travel by calculating the optimal route while considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI perform the calculation of the optimal route.

[0093] The generation unit can analyze the user's social media activity and suggest relevant routes when calculating routes. For example, the generation unit can suggest relevant routes based on places the user has checked in to on social media. It can also suggest relevant routes based on places and events the user follows on social media. Furthermore, it can suggest relevant routes based on photos and posts the user has shared on social media. This allows the user to select routes that interest them by analyzing their social media activity and suggesting relevant routes. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media data into a generation AI and have the generation AI perform the task of suggesting relevant routes.

[0094] The control unit can estimate the user's emotions and adjust the speed and behavior of movement based on the estimated emotions. For example, if the user is relaxed, the control unit will move at a slow speed. If the user is in a hurry, the control unit can also move at a faster speed. Furthermore, if the user is feeling anxious, the control unit can move with safety-conscious behavior. In this way, by adjusting the speed and behavior of movement according to the user's emotions, the user can move comfortably. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 control unit may be performed using AI, or not using AI. For example, the control unit can input user emotion data into the generative AI and have the generative AI adjust the speed and behavior of movement.

[0095] The control unit can monitor the user's health and physical condition during travel and adjust the movement as needed. For example, if the user is tired, the control unit can slow down the travel speed. Conversely, if the user is seeking healthy exercise, the control unit can also increase the travel speed. Furthermore, if the user is unwell, the control unit can temporarily suspend travel and prompt them to rest. This allows the user to travel safely by adjusting the movement according to their health and physical condition. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's health data into a generating AI and have the generating AI perform the adjustments to the movement.

[0096] The control unit can select the optimal mode of travel by referring to the user's past travel history while the user is traveling. For example, the control unit can suggest the optimal mode of travel based on the user's past travel history. The control unit can also suggest a mode of travel that avoids congestion based on the user's past travel history. Furthermore, the control unit can analyze the user's past travel history and suggest the most efficient mode of travel. This enables efficient travel by selecting the optimal mode of travel by referring to the user's past travel history. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input past travel data into a generating AI and have the generating AI select the optimal mode of travel.

[0097] The control unit can estimate the user's emotions and determine travel priorities based on the estimated emotions. For example, if the user is feeling stressed, the control unit will prioritize suggesting relaxing travel methods. It can also prioritize suggesting the fastest travel methods if the user is in a hurry. Furthermore, if the user is enjoying themselves, the control unit can prioritize suggesting travel methods that pass through tourist attractions and entertainment facilities. This allows the user to travel comfortably by prioritizing travel according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 control unit may be performed using AI or not. For example, the control unit can input user emotion data into a generative AI and have the generative AI determine travel priorities.

[0098] The control unit can select the optimal mode of transport while the user is in transit, taking into account the user's geographical location. For example, the control unit may prioritize suggesting modes of transport that are closest to the user's current location. Furthermore, if the user is in a specific area, the control unit can suggest the optimal mode of transport within that area. Additionally, if the user is traveling along a specific route, the control unit can suggest the optimal mode of transport along that route. This enables efficient travel by selecting the optimal mode of transport while considering the user's geographical location. Some or all of the above-described processes in the control unit may be performed using AI, for example, or without AI. For instance, the control unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal mode of transport.

[0099] The control unit can analyze the user's social media activity while they are traveling and suggest relevant travel methods. For example, the control unit can suggest relevant travel methods based on places the user has checked into on social media. It can also suggest relevant travel methods based on places and events the user follows on social media. Furthermore, it can suggest relevant travel methods based on photos and posts the user has shared on social media. This allows the user to select a travel method that interests them by analyzing their social media activity and suggesting relevant travel methods. Some or all of the above processing in the control unit may be performed using AI, for example, or without AI. For example, the control unit can input the user's social media data into a generating AI and have the generating AI suggest relevant travel methods.

[0100] The monitoring unit can estimate the user's emotions and adjust the accuracy and scope of monitoring based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can increase the accuracy of monitoring and provide more detailed information. If the user is relaxed, the monitoring unit can also broaden the scope of monitoring and provide more comprehensive information. Furthermore, if the user is in a hurry, the monitoring unit can increase the accuracy of monitoring and provide information more quickly. This allows users to move with peace of mind by adjusting the accuracy and scope of monitoring according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, 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 or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the accuracy and scope of monitoring.

[0101] The monitoring unit can improve the accuracy of obstacle avoidance by referring to past obstacle data during monitoring. For example, the monitoring unit optimizes the current obstacle avoidance route based on the location information of previously detected obstacles. The monitoring unit can also analyze past obstacle data and predict and avoid frequently occurring obstacle patterns. Furthermore, the monitoring unit can refer to past obstacle data and calculate avoidance routes considering the obstacle occurrence rate at specific times and locations. This improves the accuracy of obstacle avoidance by referring to past obstacle data, thereby ensuring safe movement. 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 past obstacle data into a generating AI and have the generating AI perform the task of improving the accuracy of obstacle avoidance.

[0102] The monitoring unit can monitor the user's health and physical condition during monitoring and adjust the monitoring method as needed. For example, if the user is tired, the monitoring unit can increase the frequency of monitoring and provide more detailed information. If the user is healthy, the monitoring unit can also broaden the scope of monitoring and provide more comprehensive information. Furthermore, if the user is unwell, the monitoring unit can improve the accuracy of monitoring and provide information more quickly. This allows the user to travel safely by adjusting the monitoring method according to their health and physical condition. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the user's health data into a generating AI and have the generating AI adjust the monitoring method.

[0103] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit can prioritize monitoring and providing important information. It can also prioritize monitoring and providing general information if the user is relaxed. Furthermore, if the user is in a hurry, the monitoring unit can prioritize monitoring and providing information quickly. This allows users to move with peace of mind by prioritizing monitoring according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI determine monitoring priorities.

[0104] The monitoring unit can select the optimal monitoring method while monitoring, taking into account the user's geographical location information. For example, the monitoring unit may prioritize monitoring locations close to the user's current location. Furthermore, if the user is in a specific area, the monitoring unit can prioritize monitoring important information within that area. Additionally, if the user is traveling a specific route, the monitoring unit can prioritize monitoring important information along that route. This ensures safe travel by selecting the optimal monitoring method based on the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For instance, the monitoring unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal monitoring method.

[0105] The monitoring unit can analyze the user's social media activity during monitoring and propose relevant monitoring methods. For example, the monitoring unit can propose relevant monitoring methods based on the locations the user has checked into on social media. It can also propose relevant monitoring methods based on the locations and events the user follows on social media. Furthermore, the monitoring unit can propose relevant monitoring methods based on the photos and posts the user has shared on social media. In this way, by analyzing the user's social media activity and proposing relevant monitoring methods, it can provide information that the user is interested in. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the user's social media data into a generating AI and have the generating AI execute suggestions for relevant monitoring methods.

[0106] The service provider can estimate the user's emotions and adjust the display method of the travel progress based on the estimated user emotions. For example, if the user is feeling anxious, the service provider can provide detailed progress updates to reassure them. If the user is relaxed, the service provider can also provide concise progress updates to reduce stress. Furthermore, if the user is in a hurry, the service provider can provide rapid progress updates to support efficient travel. This allows users to travel with peace of mind by adjusting the display method of travel progress according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the display method of the progress.

[0107] The service provider can select the optimal display method when providing information on the progress of travel by referring to the user's past travel history. For example, the service provider can suggest the optimal display method for the progress based on the display method the user has preferred in the past. The service provider can also suggest a display method that avoids congestion based on the user's past travel history. Furthermore, the service provider can analyze the user's past travel history and suggest the most efficient display method. This allows the user to travel efficiently by selecting the optimal display method by referring to the user's past travel history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past travel data into a generating AI and have the generating AI select the optimal display method.

[0108] The service provider can update and display the user's current location information in real time when providing information on the progress of movement. For example, the service provider can update the user's current location in real time and display the progress while the user is moving. The service provider can also update the current location in real time and display the optimal progress as the user approaches their destination. Furthermore, if the user gets lost, the service provider can update the current location in real time and display the progress again. This allows the user to understand their current movement status by updating and displaying their current location information in real time. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input current location data into a generating AI and have the generating AI perform real-time updates and displays.

[0109] The service provider can estimate the user's emotions and prioritize the progress of the journey based on the estimated emotions. For example, if the user is feeling stressed, the service provider can prioritize displaying progress that promotes relaxation. It can also prioritize displaying the most important progress if the user is in a hurry. Furthermore, if the user is enjoying themselves, the service provider can prioritize displaying progress related to tourist attractions and entertainment facilities. This allows the user to travel with peace of mind by prioritizing the progress of the journey according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of the progress.

[0110] The service provider can select the optimal display method when providing information on the progress of movement, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. In addition, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the user to move efficiently by selecting the optimal display method considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0111] The service provider can provide multilingual displays according to the user's language settings when providing movement progress. For example, the service provider can automatically set the language of the progress based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. Furthermore, if the service provider selects a specific language, the service provider can provide the progress in that language. This allows the user to move efficiently by providing multilingual displays according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language setting data into a generating AI and have the generating AI execute the multilingual display.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The reception unit can recognize the user's voice and accept voice input. For example, users can easily input their destination without using their hands by specifying it by voice. The reception unit can also use voice recognition technology to convert the user's speech into text and send it to the generation unit. Furthermore, the reception unit can analyze the tone and speed of the user's voice during voice input and estimate the user's emotions. As a result, using voice input allows users to input their destination more intuitively and enables responses that are tailored to their emotions.

[0114] The monitoring unit can monitor the user's health status and issue alerts if abnormalities are detected. For example, it can measure the user's heart rate and blood pressure with sensors and display a warning if abnormal values ​​are detected. The monitoring unit can also analyze the user's walking pattern and issue a warning if there is a high risk of falling. Furthermore, the monitoring unit can measure the user's body temperature and issue an alert if a fever is detected. This allows for real-time monitoring of the user's health status and a rapid response when abnormalities occur.

[0115] The control unit can provide music and audio guides while the user is traveling. For example, it can play relaxing music if the user wants to relax. It can also provide audio guidance about the history and highlights of tourist attractions when the user is visiting them. Furthermore, it can play music suitable for exercise if the user is exercising. By providing music and audio guides during travel, the control unit can make the journey more enjoyable and comfortable.

[0116] The generation unit can analyze the user's past travel history and propose the optimal travel route. For example, it can prioritize suggesting routes that the user has frequently used in the past. Furthermore, the generation unit can suggest routes that avoid congestion based on the user's past travel history. In addition, the generation unit can analyze the user's past travel history and propose the most efficient route. This allows for efficient travel by suggesting the optimal route based on the user's past travel history.

[0117] The service provider can estimate the user's emotions and adjust how the travel progress is displayed based on those emotions. For example, if the user is feeling anxious, detailed progress updates can be provided to reassure them. If the user is relaxed, concise progress updates can be provided to reduce stress. Furthermore, if the user is in a hurry, rapid progress updates can be provided to support efficient travel. In this way, by adjusting how travel progress is displayed according to the user's emotions, the user can travel with peace of mind.

[0118] The monitoring unit can monitor the user's health status and issue alerts if abnormalities are detected. For example, it can measure the user's heart rate and blood pressure with sensors and display a warning if abnormal values ​​are detected. The monitoring unit can also analyze the user's walking pattern and issue a warning if there is a high risk of falling. Furthermore, the monitoring unit can measure the user's body temperature and issue an alert if a fever is detected. This allows for real-time monitoring of the user's health status and a rapid response when abnormalities occur.

[0119] The control unit can provide music and audio guides while the user is traveling. For example, it can play relaxing music if the user wants to relax. It can also provide audio guidance about the history and highlights of tourist attractions when the user is visiting them. Furthermore, it can play music suitable for exercise if the user is exercising. By providing music and audio guides during travel, the control unit can make the journey more enjoyable and comfortable.

[0120] The generation unit can analyze the user's past travel history and propose the optimal travel route. For example, it can prioritize suggesting routes that the user has frequently used in the past. Furthermore, the generation unit can suggest routes that avoid congestion based on the user's past travel history. In addition, the generation unit can analyze the user's past travel history and propose the most efficient route. This allows for efficient travel by suggesting the optimal route based on the user's past travel history.

[0121] The service provider can estimate the user's emotions and adjust how the travel progress is displayed based on those emotions. For example, if the user is feeling anxious, detailed progress updates can be provided to reassure them. If the user is relaxed, concise progress updates can be provided to reduce stress. Furthermore, if the user is in a hurry, rapid progress updates can be provided to support efficient travel. In this way, by adjusting how travel progress is displayed according to the user's emotions, the user can travel with peace of mind.

[0122] The monitoring unit can monitor the user's health status and issue alerts if abnormalities are detected. For example, it can measure the user's heart rate and blood pressure with sensors and display a warning if abnormal values ​​are detected. The monitoring unit can also analyze the user's walking pattern and issue a warning if there is a high risk of falling. Furthermore, the monitoring unit can measure the user's body temperature and issue an alert if a fever is detected. This allows for real-time monitoring of the user's health status and a rapid response when abnormalities occur.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The reception unit receives the user's destination information. The reception unit provides an interface for the user to input the destination they wish to travel to, for example. Step 2: The generation unit calculates the optimal route based on the destination information received by the reception unit. The generation unit calculates the optimal route based on criteria such as distance, time, and the presence or absence of obstacles, for example, using AI. Step 3: The control unit controls the autonomous wheelchair or mobile robot based on the route calculated by the generation unit. The control unit controls the movement of the autonomous wheelchair or mobile robot, for example, using AI. Step 4: The monitoring unit monitors the surrounding environment while the autonomous wheelchair or mobile robot controlled by the control unit is moving, and avoids obstacles. The monitoring unit, for example, uses AI and sensors to monitor the surrounding environment and detect obstacles.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the reception unit, generation unit, control unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for the user to input the destination they wish to travel to. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and calculates the optimal route using AI. The control unit is implemented by the control unit 46A of the smart device 14 and controls the movement of the autonomous wheelchair or mobile robot. The monitoring unit monitors the surrounding environment using the camera 42 and sensors of the smart device 14 and detects obstacles. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[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 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.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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 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.

[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 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.

[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 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.

[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 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.

[0144] Each of the multiple elements described above, including the reception unit, generation unit, control unit, and monitoring unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for the user to voice input the destination they wish to go to. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and calculates the optimal route using AI. The control unit is implemented, for example, by the control unit 46A of the smart glasses 214 and controls the movement of the autonomous wheelchair or mobile robot. The monitoring unit monitors the surrounding environment using the camera 42 and sensors of the smart glasses 214 and detects obstacles. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[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 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.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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 (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).

[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] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the reception unit, generation unit, control unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for the user to voice input the destination they wish to go to. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and calculates the optimal route using AI. The control unit is implemented by the control unit 46A of the headset terminal 314 and controls the movement of the autonomous wheelchair or mobile robot. The monitoring unit monitors the surrounding environment and detects obstacles using the camera 42 and sensors of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the reception unit, generation unit, control unit, and monitoring unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for the user to voice input the destination they wish to go to. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and calculates the optimal route using AI. The control unit is implemented by, for example, the control unit 46A of the robot 414 and controls the movement of the autonomous wheelchair or mobile robot. The monitoring unit monitors the surrounding environment using, for example, the camera 42 and sensors of the robot 414 and detects obstacles. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A reception area that receives user destination information, A generation unit that calculates the optimal route based on destination information received by the reception unit, A control unit that controls an autonomous wheelchair or mobile robot based on the route calculated by the generation unit, The system includes a monitoring unit that monitors the surrounding environment and avoids obstacles while an autonomous wheelchair or mobile robot controlled by the control unit is in motion. A system characterized by the following features. (Note 2) The aforementioned monitoring unit, Sensors are used to monitor the surrounding environment and detect obstacles. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned monitoring unit, If an obstacle is detected, take immediate evasive action. The system described in Appendix 1, characterized by the features described herein. (Note 4) The control unit, Controlling movement inside and outside the facility The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Calculate the optimal route within the facility. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Calculate the optimal route outside the facility. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The user enters the destination they want to go to. The system described in Appendix 1, characterized by the features described herein. (Note 8) The control unit, It includes a service that provides users with information on the progress of their movements. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and adjusts the destination input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system analyzes the user's past travel history and suggests destinations when the user enters a destination. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When you enter a destination, the system will suggest the most suitable destination based on your current health and physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is It estimates the user's emotions and determines the priority of destination inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When a destination is entered, the system prioritizes and displays highly relevant destinations, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When a destination is entered, the system analyzes the user's social media activity and suggests relevant destinations. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system estimates the user's emotions and adjusts the root calculation algorithm based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When calculating a route, the system predicts the optimal route by referring to past travel data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When calculating a route, the difficulty of the route is adjusted based on the user's health and physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The system estimates the user's emotions and determines the priority of route calculations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is When calculating a route, the system takes the user's geographical location into consideration to calculate the optimal route. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During route calculation, the system analyzes the user's social media activity and suggests relevant routes. The system described in Appendix 1, characterized by the features described herein. (Note 21) The control unit, It estimates the user's emotions and adjusts the movement speed and behavior based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The control unit, During travel, the system monitors the user's health and physical condition and adjusts the travel plan as needed. The system described in Appendix 1, characterized by the features described herein. (Note 23) The control unit, During travel, the system selects the optimal mode of transport by referring to the user's past travel history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The control unit, It estimates the user's emotions and determines the priority of movement based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The control unit, During travel, the system selects the optimal mode of transport, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The control unit, While the user is on the move, the system analyzes their social media activity and suggests relevant travel methods. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the accuracy and scope of monitoring based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned monitoring unit, During monitoring, historical obstacle data is referenced to improve the accuracy of obstacle avoidance. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned monitoring unit, During monitoring, the system monitors the user's health and physical condition and adjusts the monitoring method as needed. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned monitoring unit, During monitoring, the optimal monitoring method is selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned monitoring unit, During monitoring, we analyze users' social media activity and suggest relevant monitoring methods. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the movement progress is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing information on the progress of a user's movement, the system selects the optimal display method by referring to the user's past movement history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing movement progress, the system updates and displays the user's current location information in real time. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the progress of the movement based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned supply unit is, When providing progress updates for movement, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned supply unit is, When providing progress updates for travel, the system provides multilingual support based on the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 reception area that receives user destination information, A generation unit that calculates the optimal route based on destination information received by the reception unit, A control unit that controls an autonomous wheelchair or mobile robot based on the route calculated by the generation unit, The system includes a monitoring unit that monitors the surrounding environment and avoids obstacles while an autonomous wheelchair or mobile robot controlled by the control unit is in motion. A system characterized by the following features.

2. The aforementioned monitoring unit, Sensors are used to monitor the surrounding environment and detect obstacles. The system according to feature 1.

3. The aforementioned monitoring unit, If an obstacle is detected, take immediate evasive action. The system according to feature 1.

4. The control unit, Controlling movement inside and outside the facility The system according to feature 1.

5. The generating unit is Calculate the optimal route within the facility. The system according to feature 1.

6. The generating unit is Calculate the optimal route outside the facility. The system according to feature 1.

7. The aforementioned reception unit is The user enters the destination they want to go to. The system according to feature 1.

8. The control unit, It includes a service that provides users with information on the progress of their movements. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and adjusts the destination input interface based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is The system analyzes the user's past travel history and suggests destinations when the user enters a destination. The system according to feature 1.

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