System

Abstract

An object of the system according to the embodiment is to support effective recovery by clearly setting a goal and monitoring the progress of a patient who is hospitalized for a long time or undergoing rehabilitation.SOLUTION: A system includes a target setting unit, a standing position setting unit, and a monitoring unit. The target setting unit sets a target task based on a current state of a patient or a treatment plan. The standing-position setting unit determines a standing position after recovery on the basis of the target task set by the target setting unit and sets a target. The monitoring unit monitors the progress of the patient in real time based on the target set by the standing-position setting unit, and corrects the target task as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques lack clear goal setting and progress monitoring for patients undergoing long-term hospitalization or rehabilitation, leaving room for improvement in maximizing the benefits of recovery.

[0005] The system according to the embodiment aims to set clear goals and monitor progress for patients undergoing long-term hospitalization or rehabilitation, thereby supporting effective recovery. [Means for solving the problem]

[0006] The system according to the embodiment includes a goal setting unit, a standing position setting unit, and a monitoring unit. The goal setting unit sets a target task based on the patient's current condition or treatment plan. The standing position setting unit determines a standing position after recovery based on the target task set by the goal setting unit and sets a goal. The monitoring unit monitors the patient's progress in real time based on the goal set by the standing position setting unit and modifies the target task as necessary. [Effects of the Invention]

[0007] The system according to the embodiment can set clear goals and monitor progress for patients undergoing long-term hospitalization or rehabilitation, thereby supporting effective recovery. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An assistance system according to an embodiment of the present invention supports people who have taken time off work or whose social life has been suspended due to a fracture or injury. This assistance system uses generative AI to propose goal tasks for those undergoing long-term hospitalization or rehabilitation who are uncertain about their goals, consider their post-recovery goals, and set specific goals. For example, the assistance system uses generative AI to set appropriate goal tasks based on the patient's current condition and treatment plan. Next, the generative AI considers their post-recovery goals and sets specific goals. Furthermore, the assistance system monitors the patient's progress in real time and modifies the goal tasks as needed. This enables patients to recover and restart more effectively while maintaining hope and direction. The assistance system thus supports effective recovery and restart by setting goal tasks based on the patient's current condition and treatment plan, considering their post-recovery goals, and monitoring their progress. For example, progressing rehabilitation toward the goals set by the generative AI can speed up recovery and enable earlier return to work. Furthermore, having specific post-recovery goals reduces anxiety about restarting work and supports work style.

[0029] The support system according to the embodiment includes a goal setting unit, a standing position setting unit, and a monitoring unit. The goal setting unit sets a goal task based on the patient's current condition or treatment plan. For example, the goal setting unit sets rehabilitation goals based on the patient's medical records and diagnosis results. The goal setting unit can also use a generation AI to set gradual goals according to the patient's degree of physical recovery. The standing position setting unit determines a standing position after recovery and sets a goal based on the target task set by the goal setting unit. For example, the standing position setting unit sets a goal according to the patient's work content and working style after returning to work. The standing position setting unit can also use a generation AI to set a goal that takes into account the patient's living environment and support system. The monitoring unit monitors the patient's progress in real time based on the goals set by the standing position setting unit and modifies the goal task as necessary. For example, the monitoring unit analyzes the patient's rehabilitation progress in real time, and the generation AI modifies the goal as appropriate. The monitoring unit can also estimate the patient's emotions and adjust the monitoring frequency based on the estimated emotions. As a result, the support system according to the embodiment can set target tasks based on the patient's current condition and treatment plan, consider where they will stand after recovery, and monitor their progress, thereby supporting effective recovery and restart.

[0030] The assistance system includes a data collection unit that collects the patient's rehabilitation progress and the degree of physical recovery. The data collection unit collects the patient's rehabilitation progress and the degree of physical recovery. For example, the data collection unit collects an evaluation of athletic ability and the degree of rehabilitation achievement. The data collection unit can also collect the results of physical fitness tests and evaluations of exercise tolerance. By collecting the rehabilitation progress and the degree of physical recovery, more appropriate target tasks can be set. Some or all of the above-described processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can input evaluation data of the patient's athletic ability into a generation AI, which can analyze the data and evaluate the rehabilitation progress.

[0031] The assistance system includes a data analysis unit that analyzes data collected by the data collection unit. The data analysis unit analyzes the data collected by the data collection unit. For example, the data analysis unit analyzes collected motor skill evaluation data to evaluate the progress of rehabilitation. The data analysis unit can also analyze the results of a physical fitness test to evaluate the degree of physical fitness recovery. Furthermore, the data analysis unit can use a generation AI to set or modify target tasks based on the collected data. This allows for more accurate setting and modification of target tasks by analyzing the collected data. Some or all of the above-described processing in the data analysis unit may be performed using AI, or may be performed without using AI. For example, the data analysis unit can input collected data to a generation AI, which can analyze the data and set target tasks.

[0032] The assistance system includes a providing unit that provides the patient with a target task or post-recovery position proposed by the generation AI. The providing unit provides the patient with the target task or post-recovery position proposed by the generation AI. For example, the providing unit provides the patient with a rehabilitation goal set by the generation AI. The providing unit can also provide the patient with a goal of returning to work after recovery proposed by the generation AI. Furthermore, when providing the patient with the target task or post-recovery position proposed by the generation AI, the providing unit can estimate the patient's emotions and adjust the method of providing the information based on the estimated emotions. In this way, by providing the patient with the target task or post-recovery position proposed by the generation AI, the patient can engage in rehabilitation with a specific goal in mind. Some or all of the above-described processing in the providing unit may be performed using AI or without AI. For example, the providing unit can provide the patient with the target task set by the generation AI and modify the goal according to the patient's progress.

[0033] The assistance system includes a goal correction unit including a specific process by which the generation AI corrects the target task. The goal correction unit includes a specific process by which the generation AI corrects the target task. For example, the goal correction unit includes a process by which the generation AI analyzes the patient's rehabilitation progress and resets the goal. The goal correction unit may also include a process by which the generation AI estimates the patient's emotions and corrects the goal based on the estimated emotions. Furthermore, the goal correction unit may also include a process by which the generation AI corrects the goal taking into account the patient's living environment and support system. In this way, the inclusion of a specific process by which the generation AI corrects the target task enables appropriate goal correction according to the patient's progress. Some or all of the above-mentioned processing in the goal correction unit may be performed using AI, or may be performed without using AI. For example, the goal correction unit may execute a process by which the generation AI analyzes the patient's rehabilitation progress and resets the goal.

[0034] The goal setting unit can analyze the patient's past treatment history and select the optimal target task. For example, the goal setting unit allows the generation AI to set target tasks based on similar cases based on the patient's past rehabilitation history. The goal setting unit can also allow the generation AI to analyze the patient's response to a specific treatment from the patient's treatment history and select the optimal target task. The goal setting unit can also allow the generation AI to set step-by-step target tasks according to the progress of rehabilitation based on the patient's past treatment history. This makes it possible to set more appropriate target tasks by analyzing the patient's past treatment history. Some or all of the above-mentioned processing in the goal setting unit may be performed using AI, or may be performed without using AI. For example, the goal setting unit can execute a process in which the generation AI analyzes the patient's past treatment history and selects the optimal target task.

[0035] When setting goals, the goal setting unit can customize the goals by taking into account the patient's living environment and support system. For example, the goal setting unit can take into account the patient's home environment and set rehabilitation tasks that the generation AI can perform at home. The goal setting unit can also take into account the patient's support system and set goal tasks that the generation AI can complete with the cooperation of family and friends. The goal setting unit can also take into account the patient's living environment and set goal tasks that require hospital visits or outings. This makes it possible to set more realistic and achievable goals by taking into account the patient's living environment and support system. Some or all of the above-mentioned processing in the goal setting unit may be performed using AI, or may be performed without using AI. For example, the goal setting unit can execute a process in which the generation AI customizes goals by taking into account the patient's living environment and support system.

[0036] When setting goals, the goal setting unit can set gradual goals according to the patient's rehabilitation progress. For example, the goal setting unit allows the generation AI to set short-term and long-term goals based on the patient's rehabilitation progress. The goal setting unit can also allow the generation AI to set goal tasks of gradually increasing difficulty according to the patient's rehabilitation progress. The goal setting unit can also monitor the patient's rehabilitation progress in real time, and the generation AI can modify the goals as appropriate. This allows for setting gradual goals according to the patient's rehabilitation progress, thereby promoting a reasonable rehabilitation. Some or all of the above-mentioned processing in the goal setting unit may be performed using AI, or may be performed without using AI. For example, the goal setting unit can execute a process in which the generation AI sets gradual goals based on the patient's rehabilitation progress.

[0037] When setting goals, the goal setting unit can set goals based on the patient's occupation or hobbies. For example, the goal setting unit allows the generation AI to set goal tasks to improve skills related to the patient's occupation. The goal setting unit can also allow the generation AI to set goal tasks that incorporate activities related to the patient's hobbies. The goal setting unit can also allow the generation AI to set goal tasks that increase motivation based on the patient's occupation or hobbies. In this way, setting goals based on the patient's occupation or hobbies can increase motivation. Some or all of the above-mentioned processing in the goal setting unit may be performed using AI, or may be performed without using AI. For example, the goal setting unit can execute a process in which the generation AI sets goals based on the patient's occupation or hobbies.

[0038] The goal setting unit can set goals taking into consideration the support of the patient's family and friends when setting goals. For example, the goal setting unit causes the generation AI to set rehabilitation tasks that the patient's family can cooperate with. The goal setting unit can also cause the generation AI to set goal tasks that incorporate activities that the patient's friends can support. The goal setting unit can also set goal tasks that the generation AI can achieve together, taking into consideration the support of the patient's family and friends. This makes it possible to set more realistic and achievable goals by taking into consideration the support of the patient's family and friends. Some or all of the above-mentioned processing in the goal setting unit may be performed using AI, or may be performed without using AI. For example, the goal setting unit can cause the generation AI to execute a process of setting goals taking into consideration the support of the patient's family and friends.

[0039] When setting goals, the goal setting unit can set goals taking into account the patient's geographical conditions. For example, the goal setting unit can cause the generation AI to set appropriate rehabilitation tasks taking into account the climate of the area where the patient lives. The goal setting unit can also cause the generation AI to set goal tasks that require medical visits taking into account access to medical facilities in the area where the patient lives. The goal setting unit can also cause the generation AI to set rehabilitation tasks that can be done outdoors taking into account the geographical conditions of the area where the patient lives. In this way, more realistic and achievable goals can be set by taking into account the patient's geographical conditions. Some or all of the above-mentioned processing in the goal setting unit may be performed using AI, or may be performed without using AI. For example, the goal setting unit can execute a process in which the generation AI sets goals taking into account the patient's geographical conditions.

[0040] When setting a standing position, the standing position setting unit can set specific goals by taking into account the patient's work environment and work content. For example, the standing position setting unit allows the generation AI to propose appropriate work content by taking into account the patient's work environment. The standing position setting unit can also allow the generation AI to set goals aimed at improving skills by taking into account the patient's work content. The standing position setting unit can also allow the generation AI to set gradual goals based on the patient's work environment and work content. This makes it possible to set more realistic and achievable goals by taking into account the patient's work environment and work content. Some or all of the above-mentioned processing in the standing position setting unit may be performed using AI, or may be performed without using AI. For example, the standing position setting unit can execute a process in which the generation AI sets specific goals by taking into account the patient's work environment and work content.

[0041] The position setting unit can set goals taking into account the patient's skills and experience when setting the position. For example, the position setting unit can take into account the patient's skills and set goals that the generation AI aims to improve. The position setting unit can also take into account the patient's experience and suggest work content that allows the generation AI to utilize that experience. The position setting unit can also allow the generation AI to set gradual goals based on the patient's skills and experience. This makes it possible to set more realistic and achievable goals by taking into account the patient's skills and experience. Some or all of the above-mentioned processing in the position setting unit may be performed using AI, or may be performed without using AI. For example, the position setting unit can execute a process in which the generation AI sets goals taking into account the patient's skills and experience.

[0042] When setting the standing position, the standing position setting unit can set gradual goals according to the patient's rehabilitation progress. For example, the standing position setting unit allows the generation AI to set short-term and long-term goals based on the patient's rehabilitation progress. The standing position setting unit can also allow the generation AI to set goal tasks of gradually increasing difficulty according to the patient's rehabilitation progress. The standing position setting unit can also monitor the patient's rehabilitation progress in real time, and the generation AI can modify the goals as appropriate. This makes it possible to set gradual goals according to the patient's rehabilitation progress, thereby providing a reasonable return plan. Some or all of the above-mentioned processing in the standing position setting unit may be performed using AI, or may be performed without using AI. For example, the standing position setting unit can execute a process in which the generation AI sets gradual goals based on the patient's rehabilitation progress.

[0043] The standing position setting unit can set goals based on the patient's occupation and hobbies when setting the standing position. For example, the generation AI in the standing position setting unit sets goal tasks to improve skills related to the patient's occupation. The standing position setting unit can also set goal tasks that incorporate activities related to the patient's hobbies. The standing position setting unit can also set goal tasks that increase motivation based on the patient's occupation and hobbies. In this way, motivation can be increased by setting goals based on the patient's occupation and hobbies. Some or all of the above-mentioned processing in the standing position setting unit may be performed using AI, or may be performed without using AI. For example, the standing position setting unit can execute a process in which the generation AI sets goals based on the patient's occupation and hobbies.

[0044] When setting the standing position, the standing position setting unit can set goals taking into consideration the support of the patient's family and friends. For example, the standing position setting unit causes the generation AI to set rehabilitation tasks that the patient's family can cooperate with. The standing position setting unit can also cause the generation AI to set goal tasks that incorporate activities that the patient's friends can support. The standing position setting unit can also set goal tasks that the generation AI can achieve together, taking into consideration the support of the patient's family and friends. This makes it possible to set more realistic and achievable goals by taking into consideration the support of the patient's family and friends. Some or all of the above-mentioned processing in the standing position setting unit may be performed using AI, or may be performed without using AI. For example, the standing position setting unit can cause the generation AI to execute a process of setting goals taking into consideration the support of the patient's family and friends.

[0045] When setting the standing position, the standing position setting unit can set a goal taking into account the geographical conditions of the patient. For example, the standing position setting unit can cause the generation AI to set an appropriate rehabilitation task taking into account the climate of the area where the patient lives. The standing position setting unit can also cause the generation AI to set a goal task that requires medical visits taking into account access to medical facilities in the area where the patient lives. The standing position setting unit can also cause the generation AI to set a rehabilitation task that can be performed outdoors taking into account the geographical conditions of the area where the patient lives. In this way, by taking the patient's geographical conditions into account, more realistic and achievable goals can be set. Some or all of the above-mentioned processing in the standing position setting unit may be performed using AI, or may be performed without using AI. For example, the standing position setting unit can execute a process in which the generation AI sets a goal taking into account the patient's geographical conditions.

[0046] During monitoring, the monitoring unit can analyze the patient's rehabilitation progress in real time and revise goals as necessary. For example, the monitoring unit can analyze the patient's rehabilitation progress in real time, and the generating AI can revise goals as appropriate. Furthermore, if the patient's rehabilitation progress is slow, the monitoring unit can have the generating AI reset goals and provide appropriate support. Furthermore, if the patient's rehabilitation progress is going well, the monitoring unit can have the generating AI raise goals and encourage further challenges. This enables appropriate goal revision by analyzing the patient's rehabilitation progress in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can execute a process in which the generating AI analyzes the patient's rehabilitation progress in real time and revise goals.

[0047] The monitoring unit can customize the monitoring method during monitoring, taking into account the patient's living environment and support system. For example, the monitoring unit can set a monitoring method that the generation AI can perform at home, taking into account the patient's home environment. The monitoring unit can also set a monitoring method that allows the generation AI to obtain cooperation from family and friends, taking into account the patient's support system. The monitoring unit can also set a monitoring method that requires the generation AI to visit a hospital or go outside, taking into account the patient's living environment. This makes it possible to provide a more realistic and appropriate monitoring method by taking into account the patient's living environment and support system. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can execute a process in which the generation AI customizes the monitoring method, taking into account the patient's living environment and support system.

[0048] During monitoring, the monitoring unit can set step-by-step goals according to the patient's rehabilitation progress. For example, the monitoring unit allows the generation AI to set short-term and long-term goals based on the patient's rehabilitation progress. The monitoring unit can also allow the generation AI to set goal tasks of gradually increasing difficulty according to the patient's rehabilitation progress. The monitoring unit can also monitor the patient's rehabilitation progress in real time, and the generation AI can modify the goals as appropriate. This allows for step-by-step goals to be set according to the patient's rehabilitation progress, thereby promoting a reasonable rehabilitation. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can execute a process in which the generation AI sets step-by-step goals based on the patient's rehabilitation progress.

[0049] During monitoring, the monitoring unit can set a monitoring method based on the patient's occupation or hobbies. For example, the monitoring unit allows the generation AI to set a method for monitoring activities related to the patient's occupation. The monitoring unit can also allow the generation AI to set a method for monitoring activities related to the patient's hobbies. The monitoring unit can also allow the generation AI to set a monitoring method that increases motivation based on the patient's occupation or hobbies. In this way, motivation can be increased by setting a monitoring method based on the patient's occupation or hobbies. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI or without AI. For example, the monitoring unit can execute a process in which the generation AI sets a monitoring method based on the patient's occupation or hobbies.

[0050] The monitoring unit can set a monitoring method during monitoring, taking into consideration the support of the patient's family and friends. For example, the monitoring unit allows the generation AI to set a monitoring method that allows the patient's family to cooperate. The monitoring unit can also allow the generation AI to set a monitoring method that allows the patient's friends to provide support. The monitoring unit can also set a monitoring method that the generation AI can use together, taking into consideration the support of the patient's family and friends. This makes it possible to provide a more realistic and appropriate monitoring method by taking into consideration the support of the patient's family and friends. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can execute a process in which the generation AI sets a monitoring method, taking into consideration the support of the patient's family and friends.

[0051] During monitoring, the monitoring unit can set a monitoring method taking into account the geographical conditions of the patient. For example, the monitoring unit can cause the generation AI to set an appropriate monitoring method taking into account the climate of the area where the patient lives. The monitoring unit can also set a monitoring method that requires medical visits to the hospital taking into account access to medical facilities in the area where the patient lives. The monitoring unit can also set a monitoring method that can be performed outdoors taking into account the geographical conditions of the area where the patient lives. This makes it possible to provide a more realistic and appropriate monitoring method by taking into account the geographical conditions of the patient. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can execute a process in which the generation AI sets a monitoring method taking into account the geographical conditions of the patient.

[0052] The data collection unit can collect the patient's rehabilitation progress in real time during data collection. For example, the data collection unit collects the patient's rehabilitation progress in real time, and the generating AI adjusts the goals as appropriate. Furthermore, if the patient's rehabilitation progress is slow, the data collection unit can cause the generating AI to reset the goals and provide appropriate support. Furthermore, if the patient's rehabilitation progress is going well, the data collection unit can cause the generating AI to raise the goals and encourage further challenges. In this way, collecting the patient's rehabilitation progress in real time enables appropriate goal adjustment. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can cause the generating AI to collect the patient's rehabilitation progress in real time and execute a process to adjust the goals.

[0053] The data collection unit can customize the data collection method when collecting data, taking into account the patient's living environment and support system. For example, the data collection unit can set a data collection method that can be done at home, taking into account the patient's home environment. The data collection unit can also set a data collection method that can obtain the cooperation of family and friends, taking into account the patient's support system. The data collection unit can also set a data collection method that requires hospital visits or outings, taking into account the patient's living environment. This makes it possible to provide a more realistic and appropriate data collection method by taking into account the patient's living environment and support system. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can execute a process in which the generation AI customizes the data collection method, taking into account the patient's living environment and support system.

[0054] The data collection unit can set a data collection method based on the patient's occupation or hobbies when collecting data. For example, the data collection unit can set a method for collecting data on activities related to the patient's occupation. The data collection unit can also set a method for collecting data on activities related to the patient's hobbies. The data collection unit can also set a data collection method that increases motivation based on the patient's occupation or hobbies. In this way, setting a data collection method based on the patient's occupation or hobbies can increase motivation. Some or all of the above-mentioned processing in the data collection unit may be performed using AI or may be performed without using AI. For example, the data collection unit can execute a process in which the generation AI sets a data collection method based on the patient's occupation or hobbies.

[0055] The data collection unit can set a data collection method taking into consideration the support of the patient's family and friends when collecting data. For example, the data collection unit sets a data collection method that allows the patient's family to cooperate. The data collection unit can also set a data collection method that allows the patient's friends to support. The data collection unit can also set a data collection method that can be carried out jointly, taking into consideration the support of the patient's family and friends. This makes it possible to provide a more realistic and appropriate data collection method by taking into consideration the support of the patient's family and friends. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can execute a process in which the generation AI sets a data collection method taking into consideration the support of the patient's family and friends.

[0056] When collecting data, the data collection unit can set a data collection method taking into account the patient's geographical conditions. The data collection unit can, for example, set an appropriate data collection method taking into account the climate of the area where the patient lives. The data collection unit can also set a data collection method that requires visiting a medical facility in the patient's area taking into account access to medical facilities in the patient's area. The data collection unit can also set a data collection method that can be done outdoors taking into account the geographical conditions of the patient's area. This makes it possible to provide a more realistic and appropriate data collection method by taking into account the patient's geographical conditions. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can execute a process in which the generation AI sets a data collection method taking into account the patient's geographical conditions.

[0057] The data analysis unit can analyze the patient's rehabilitation progress in real time during data analysis. For example, the data analysis unit analyzes the patient's rehabilitation progress in real time, and the generating AI adjusts the goals as appropriate. Furthermore, if the patient's rehabilitation progress is slow, the data analysis unit can cause the generating AI to reset the goals and provide appropriate support. Furthermore, if the patient's rehabilitation progress is going well, the data analysis unit can cause the generating AI to raise the goals and encourage further challenges. This enables appropriate goal adjustment by analyzing the patient's rehabilitation progress in real time. Some or all of the above-mentioned processing in the data analysis unit may be performed using AI, or may be performed without using AI. For example, the data analysis unit can cause the generating AI to analyze the patient's rehabilitation progress in real time and adjust the goals.

[0058] During data analysis, the data analysis unit can customize the analysis method by taking into account the patient's living environment and support system. For example, the data analysis unit can set a data analysis method that can be performed at home by taking into account the patient's home environment. The data analysis unit can also set a data analysis method that can be performed with the cooperation of family and friends by taking into account the patient's support system. The data analysis unit can also set a data analysis method that requires hospital visits or outings by taking into account the patient's living environment. This makes it possible to provide a more realistic and appropriate data analysis method by taking into account the patient's living environment and support system. Some or all of the above-described processing in the data analysis unit may be performed using AI, or may be performed without using AI. For example, the data analysis unit can execute a process in which the generation AI customizes the data analysis method by taking into account the patient's living environment and support system.

[0059] During data analysis, the data analysis unit can set an analysis method based on the patient's occupation or hobbies. For example, the data analysis unit can set a method for analyzing data on activities related to the patient's occupation. The data analysis unit can also set a method for analyzing data on activities related to the patient's hobbies. The data analysis unit can also set a data analysis method that enhances motivation based on the patient's occupation or hobbies. In this way, motivation can be enhanced by setting an analysis method based on the patient's occupation or hobbies. Some or all of the above-described processing in the data analysis unit may be performed using AI or without AI. For example, the data analysis unit can execute a process in which the generation AI sets an analysis method based on the patient's occupation or hobbies.

[0060] The data analysis unit can set an analysis method taking into consideration the support of the patient's family and friends when analyzing data. For example, the data analysis unit sets a data analysis method that allows the patient's family to cooperate. The data analysis unit can also set a data analysis method that allows the patient's friends to support. The data analysis unit can also set a data analysis method that can be performed jointly, taking into consideration the support of the patient's family and friends. This makes it possible to provide a more realistic and appropriate data analysis method by taking into consideration the support of the patient's family and friends. Some or all of the above-mentioned processing in the data analysis unit may be performed using AI, or may be performed without using AI. For example, the data analysis unit can execute a process in which the generation AI sets an analysis method taking into consideration the support of the patient's family and friends.

[0061] When analyzing data, the data analysis unit can set an analysis method taking into account the patient's geographical conditions. The data analysis unit can set an appropriate data analysis method, for example, taking into account the climate of the area where the patient lives. The data analysis unit can also set a data analysis method that requires a hospital visit, taking into account access to medical facilities in the patient's area. The data analysis unit can also set a data analysis method that can be performed outdoors, taking into account the geographical conditions of the area where the patient lives. This makes it possible to provide a more realistic and appropriate data analysis method by taking into account the patient's geographical conditions. Some or all of the above-mentioned processing in the data analysis unit may be performed using AI, or may be performed without using AI. For example, the data analysis unit can execute a process in which the generation AI sets an analysis method taking into account the patient's geographical conditions.

[0062] The providing unit can update the provided content to reflect the patient's rehabilitation progress in real time when providing the content. For example, the providing unit reflects the patient's rehabilitation progress in real time, and the generating AI adjusts the goals as appropriate. Furthermore, if the patient's rehabilitation progress is slow, the providing unit can have the generating AI reset the goals and provide appropriate support. Furthermore, if the patient's rehabilitation progress is going well, the providing unit can have the generating AI raise the goals and encourage further challenges. This enables appropriate goal adjustment by reflecting the patient's rehabilitation progress in real time. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can execute a process in which the generating AI reflects the patient's rehabilitation progress in real time and updates the provided content.

[0063] When providing the information, the providing unit can customize the provision method by taking into account the patient's living environment and support system. For example, the providing unit can provide target tasks that can be done at home by taking into account the patient's home environment. The providing unit can also provide target tasks that can be done at home by taking into account the patient's support system. The providing unit can also provide target tasks that require hospital visits or outings by taking into account the patient's living environment. This makes it possible to provide a more realistic and appropriate provision method by taking into account the patient's living environment and support system. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can execute a process in which the generation AI customizes the provision method by taking into account the patient's living environment and support system.

[0064] The providing unit can set the content to be provided based on the patient's occupation or hobbies at the time of providing. For example, the providing unit can provide a target task that improves skills related to the patient's occupation. The providing unit can also provide a target task that incorporates activities related to the patient's hobbies. The providing unit can also provide a target task that increases motivation based on the patient's occupation or hobbies. In this way, setting the content to be provided based on the patient's occupation or hobbies can increase motivation. Some or all of the above-mentioned processing in the providing unit may be performed using AI or may be performed without using AI. For example, the providing unit can execute a process in which the generation AI sets the content to be provided based on the patient's occupation or hobbies.

[0065] The providing unit can set the content to be provided taking into consideration the support of the patient's family and friends when providing the content. For example, the providing unit provides a goal task that the patient's family can cooperate with. The providing unit can also provide a goal task that incorporates activities that the patient's friends can support. The providing unit can also provide a goal task that can be achieved jointly, taking into consideration the support of the patient's family and friends. In this way, more realistic and appropriate content to be provided can be provided by taking into consideration the support of the patient's family and friends. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can execute a process in which the generation AI sets the content to be provided taking into consideration the support of the patient's family and friends.

[0066] The providing unit can set the content to be provided taking into account the geographical conditions of the patient when providing the content. The providing unit can, for example, provide an appropriate target task by taking into account the climate of the area where the patient lives. The providing unit can also provide a target task that requires a hospital visit by taking into account the accessibility of medical facilities in the area where the patient lives. The providing unit can also provide a target task that can be done outdoors by taking into account the geographical conditions of the area where the patient lives. In this way, more realistic and appropriate content can be provided by taking into account the geographical conditions of the patient. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can execute a process in which a generation AI sets the content to be provided by taking into account the geographical conditions of the patient.

[0067] When correcting goals, the goal correction unit can reflect the patient's rehabilitation progress in real time and correct the goals. For example, the goal correction unit reflects the patient's rehabilitation progress in real time, and the generating AI corrects the goals as appropriate. Furthermore, if the patient's rehabilitation progress is slow, the goal correction unit can cause the generating AI to reset the goals and provide appropriate support. Furthermore, if the patient's rehabilitation progress is going well, the goal correction unit can cause the generating AI to raise the goals and encourage further challenges. This enables appropriate goal correction by reflecting the patient's rehabilitation progress in real time. Some or all of the above-mentioned processing in the goal correction unit may be performed using AI, or may be performed without using AI. For example, the goal correction unit can execute a process in which the generating AI reflects the patient's rehabilitation progress in real time and corrects the goals.

[0068] The goal correction unit can customize the goal correction method by taking into account the patient's living environment and support system when correcting goals. For example, the goal correction unit can set a goal correction method that can be performed at home by taking into account the patient's home environment. The goal correction unit can also set a goal correction method that requires cooperation from family and friends by taking into account the patient's support system. The goal correction unit can also set a goal correction method that requires hospital visits or outings by taking into account the patient's living environment. This makes it possible to provide a more realistic and appropriate goal correction method by taking into account the patient's living environment and support system. Some or all of the above-mentioned processing in the goal correction unit may be performed using AI, or may be performed without using AI. For example, the goal correction unit can execute a process in which the generation AI customizes the goal correction method by taking into account the patient's living environment and support system.

[0069] When correcting a goal, the goal correction unit can correct the goal based on the patient's occupation or hobbies. For example, the goal correction unit corrects a goal task to improve skills related to the patient's occupation. The goal correction unit can also correct a goal task that incorporates activities related to the patient's hobbies. The goal correction unit can also correct a goal task to increase motivation based on the patient's occupation or hobbies. In this way, by correcting the goal based on the patient's occupation or hobbies, motivation can be increased. Some or all of the above-mentioned processing in the goal correction unit may be performed using AI, or may be performed without using AI. For example, the goal correction unit can execute a process in which the generation AI corrects the goal based on the patient's occupation or hobbies.

[0070] When correcting a goal, the goal correction unit can correct the goal taking into consideration the support of the patient's family and friends. For example, the goal correction unit corrects a goal task that the patient's family can cooperate with. The goal correction unit can also correct a goal task that incorporates activities that the patient's friends can support. The goal correction unit can also correct a goal task that can be achieved jointly, taking into consideration the support of the patient's family and friends. In this way, by taking into consideration the support of the patient's family and friends, it is possible to provide a more realistic and appropriate goal. Some or all of the above-mentioned processing in the goal correction unit may be performed using AI, or may be performed without using AI. For example, the goal correction unit can execute a process in which the generation AI corrects the goal taking into consideration the support of the patient's family and friends.

[0071] The goal correction unit can correct the goal by taking into account the patient's geographical conditions when correcting the goal. For example, the goal correction unit can correct an appropriate goal task by taking into account the climate of the area where the patient lives. The goal correction unit can also correct a goal task that requires medical visits by taking into account access to medical facilities in the area where the patient lives. The goal correction unit can also correct a goal task that can be done outdoors by taking into account the geographical conditions of the area where the patient lives. In this way, by taking into account the patient's geographical conditions, more realistic and appropriate goals can be provided. Some or all of the above-mentioned processing in the goal correction unit may be performed using AI, or may be performed without using AI. For example, the goal correction unit can execute a process in which the generation AI corrects the goal by taking into account the patient's geographical conditions.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The support system's generative AI can also share a patient's rehabilitation plan with other patients based on the patient's rehabilitation progress. For example, by referring to the rehabilitation plans of other patients with similar symptoms, more effective rehabilitation methods can be found. Patients can also share their rehabilitation progress and encourage each other, increasing their motivation. Furthermore, the generative AI can share the patient's rehabilitation plan anonymously and receive feedback from other patients. This can reduce patients' anxiety about rehabilitation and enable more effective rehabilitation.

[0074] The support system's generating AI can also customize a patient's rehabilitation plan based on the patient's rehabilitation progress. For example, the generating AI can change the content of rehabilitation and suggest more effective rehabilitation methods depending on the patient's rehabilitation progress. The generating AI can also adjust the frequency and intensity of rehabilitation depending on the patient's rehabilitation progress. Furthermore, the generating AI can also reset rehabilitation goals depending on the patient's rehabilitation progress, allowing them to feel a sense of accomplishment. This makes it easier for patients to maintain their motivation for rehabilitation.

[0075] The support system's generative AI can also share the patient's rehabilitation plan with other medical professionals based on the patient's rehabilitation progress. For example, the patient's rehabilitation progress can be shared with doctors and physical therapists to find more effective rehabilitation methods. In addition, by receiving feedback from medical professionals, the generative AI can revise the rehabilitation plan and provide more effective rehabilitation. Furthermore, sharing the patient's rehabilitation progress with other medical professionals can also allow for an objective evaluation of the rehabilitation progress. This can reduce patients' anxiety about rehabilitation and enable more effective rehabilitation.

[0076] The support system also allows the generative AI to share the patient's rehabilitation plan with family members based on the patient's rehabilitation progress. For example, the patient's rehabilitation progress can be shared with family members, who can then support the rehabilitation. In addition, by receiving feedback from family members, the generative AI can revise the rehabilitation plan, enabling more effective rehabilitation. Furthermore, by sharing the patient's rehabilitation progress with family members, family members can understand the progress of rehabilitation and encourage the patient. This reduces the patient's anxiety about rehabilitation and allows for more effective rehabilitation.

[0077] The support system's generative AI can also share a patient's rehabilitation plan with other patients based on the patient's rehabilitation progress. For example, by referring to the rehabilitation plans of other patients with similar symptoms, more effective rehabilitation methods can be found. Patients can also share their rehabilitation progress and encourage each other, increasing their motivation. Furthermore, the generative AI can share the patient's rehabilitation plan anonymously and receive feedback from other patients. This can reduce patients' anxiety about rehabilitation and enable more effective rehabilitation.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The goal setting unit sets goal tasks based on the patient's current condition or treatment plan. For example, the goal setting unit sets rehabilitation goals based on the patient's medical records and diagnostic results. Generative AI can also be used to set gradual goals according to the patient's level of physical recovery. Step 2: The position setting unit determines the patient's position after recovery based on the target tasks set by the goal setting unit and sets goals. For example, the position setting unit sets goals according to the patient's work content and working style after returning to work. In addition, using generation AI, it is also possible to set goals that take into account the patient's living environment and support system. Step 3: The monitoring unit monitors the patient's progress in real time based on the goals set by the position setting unit and modifies the target tasks as necessary. For example, the monitoring unit analyzes the patient's rehabilitation progress in real time, and the generation AI modifies the goals as appropriate. It can also estimate the patient's emotions and adjust the monitoring frequency based on the estimated emotions.

[0080] (Example 2) An assistance system according to an embodiment of the present invention supports people who have taken time off work or whose social life has been suspended due to a fracture or injury. This assistance system uses generative AI to propose goal tasks for those undergoing long-term hospitalization or rehabilitation who are uncertain about their goals, consider their post-recovery goals, and set specific goals. For example, the assistance system uses generative AI to set appropriate goal tasks based on the patient's current condition and treatment plan. Next, the generative AI considers their post-recovery goals and sets specific goals. Furthermore, the assistance system monitors the patient's progress in real time and modifies the goal tasks as needed. This enables patients to recover and restart more effectively while maintaining hope and direction. The assistance system thus supports effective recovery and restart by setting goal tasks based on the patient's current condition and treatment plan, considering their post-recovery goals, and monitoring their progress. For example, progressing rehabilitation toward the goals set by the generative AI can speed up recovery and enable earlier return to work. Furthermore, having specific post-recovery goals reduces anxiety about restarting work and supports work style.

[0081] The support system according to the embodiment includes a goal setting unit, a standing position setting unit, and a monitoring unit. The goal setting unit sets a goal task based on the patient's current condition or treatment plan. For example, the goal setting unit sets rehabilitation goals based on the patient's medical records and diagnosis results. The goal setting unit can also use a generation AI to set gradual goals according to the patient's degree of physical recovery. The standing position setting unit determines a standing position after recovery and sets a goal based on the target task set by the goal setting unit. For example, the standing position setting unit sets a goal according to the patient's work content and working style after returning to work. The standing position setting unit can also use a generation AI to set a goal that takes into account the patient's living environment and support system. The monitoring unit monitors the patient's progress in real time based on the goals set by the standing position setting unit and modifies the goal task as necessary. For example, the monitoring unit analyzes the patient's rehabilitation progress in real time, and the generation AI modifies the goal as appropriate. The monitoring unit can also estimate the patient's emotions and adjust the monitoring frequency based on the estimated emotions. As a result, the support system according to the embodiment can set target tasks based on the patient's current condition and treatment plan, consider where they will stand after recovery, and monitor their progress, thereby supporting effective recovery and restart.

[0082] The assistance system includes a data collection unit that collects the patient's rehabilitation progress and the degree of physical recovery. The data collection unit collects the patient's rehabilitation progress and the degree of physical recovery. For example, the data collection unit collects an evaluation of athletic ability and the degree of rehabilitation achievement. The data collection unit can also collect the results of physical fitness tests and evaluations of exercise tolerance. By collecting the rehabilitation progress and the degree of physical recovery, more appropriate target tasks can be set. Some or all of the above-described processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can input evaluation data of the patient's athletic ability into a generation AI, which can analyze the data and evaluate the rehabilitation progress.

[0083] The assistance system includes a data analysis unit that analyzes data collected by the data collection unit. The data analysis unit analyzes the data collected by the data collection unit. For example, the data analysis unit analyzes collected motor skill evaluation data to evaluate the progress of rehabilitation. The data analysis unit can also analyze the results of a physical fitness test to evaluate the degree of physical fitness recovery. Furthermore, the data analysis unit can use a generation AI to set or modify target tasks based on the collected data. This allows for more accurate setting and modification of target tasks by analyzing the collected data. Some or all of the above-described processing in the data analysis unit may be performed using AI, or may be performed without using AI. For example, the data analysis unit can input collected data to a generation AI, which can analyze the data and set target tasks.

[0084] The assistance system includes a providing unit that provides the patient with a target task or post-recovery position proposed by the generation AI. The providing unit provides the patient with the target task or post-recovery position proposed by the generation AI. For example, the providing unit provides the patient with a rehabilitation goal set by the generation AI. The providing unit can also provide the patient with a goal of returning to work after recovery proposed by the generation AI. Furthermore, when providing the patient with the target task or post-recovery position proposed by the generation AI, the providing unit can estimate the patient's emotions and adjust the method of providing the information based on the estimated emotions. In this way, by providing the patient with the target task or post-recovery position proposed by the generation AI, the patient can engage in rehabilitation with a specific goal in mind. Some or all of the above-described processing in the providing unit may be performed using AI or without AI. For example, the providing unit can provide the patient with the target task set by the generation AI and modify the goal according to the patient's progress.

[0085] The assistance system includes a goal correction unit including a specific process by which the generation AI corrects the target task. The goal correction unit includes a specific process by which the generation AI corrects the target task. For example, the goal correction unit includes a process by which the generation AI analyzes the patient's rehabilitation progress and resets the goal. The goal correction unit may also include a process by which the generation AI estimates the patient's emotions and corrects the goal based on the estimated emotions. Furthermore, the goal correction unit may also include a process by which the generation AI corrects the goal taking into account the patient's living environment and support system. In this way, the inclusion of a specific process by which the generation AI corrects the target task enables appropriate goal correction according to the patient's progress. Some or all of the above-mentioned processing in the goal correction unit may be performed using AI, or may be performed without using AI. For example, the goal correction unit may execute a process by which the generation AI analyzes the patient's rehabilitation progress and resets the goal.

[0086] The goal setting unit can estimate the patient's emotions and adjust the difficulty of the target task based on the estimated patient's emotions. For example, if the patient feels anxious, the goal setting unit causes the generation AI to set an easy target task, allowing the patient to feel a sense of accomplishment. Furthermore, if the patient feels confident, the goal setting unit can cause the generation AI to set a more difficult target task, encouraging the patient to take on the challenge. Furthermore, if the patient feels tired, the goal setting unit can set a target task that includes rest, promoting a more natural rehabilitation. This allows the patient to be provided with a rehabilitation task that is appropriate for the patient by adjusting the difficulty of the target task based on the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the goal setting unit may be performed using AI, or may be performed without AI. For example, the goal setting unit can execute a process in which the generation AI estimates the patient's emotions and adjusts the difficulty of the target task.

[0087] The goal setting unit can analyze the patient's past treatment history and select the optimal target task. For example, the goal setting unit allows the generation AI to set target tasks based on similar cases based on the patient's past rehabilitation history. The goal setting unit can also allow the generation AI to analyze the patient's response to a specific treatment from the patient's treatment history and select the optimal target task. The goal setting unit can also allow the generation AI to set step-by-step target tasks according to the progress of rehabilitation based on the patient's past treatment history. This makes it possible to set more appropriate target tasks by analyzing the patient's past treatment history. Some or all of the above-mentioned processing in the goal setting unit may be performed using AI, or may be performed without using AI. For example, the goal setting unit can execute a process in which the generation AI analyzes the patient's past treatment history and selects the optimal target task.

[0088] When setting goals, the goal setting unit can customize the goals by taking into account the patient's living environment and support system. For example, the goal setting unit can take into account the patient's home environment and set rehabilitation tasks that the generation AI can perform at home. The goal setting unit can also take into account the patient's support system and set goal tasks that the generation AI can complete with the cooperation of family and friends. The goal setting unit can also take into account the patient's living environment and set goal tasks that require hospital visits or outings. This makes it possible to set more realistic and achievable goals by taking into account the patient's living environment and support system. Some or all of the above-mentioned processing in the goal setting unit may be performed using AI, or may be performed without using AI. For example, the goal setting unit can execute a process in which the generation AI customizes goals by taking into account the patient's living environment and support system.

[0089] When setting goals, the goal setting unit can set gradual goals according to the patient's rehabilitation progress. For example, the goal setting unit allows the generation AI to set short-term and long-term goals based on the patient's rehabilitation progress. The goal setting unit can also allow the generation AI to set goal tasks of gradually increasing difficulty according to the patient's rehabilitation progress. The goal setting unit can also monitor the patient's rehabilitation progress in real time, and the generation AI can modify the goals as appropriate. This allows for setting gradual goals according to the patient's rehabilitation progress, thereby promoting a reasonable rehabilitation. Some or all of the above-mentioned processing in the goal setting unit may be performed using AI, or may be performed without using AI. For example, the goal setting unit can execute a process in which the generation AI sets gradual goals based on the patient's rehabilitation progress.

[0090] The goal setting unit can estimate the patient's emotions and prioritize target tasks based on the estimated patient's emotions. For example, if the patient is feeling anxious, the goal setting unit causes the generation AI to prioritize easy target tasks. Furthermore, if the patient is feeling confident, the goal setting unit can also cause the generation AI to prioritize difficult target tasks. Furthermore, if the patient is tired, the goal setting unit can also cause the generation AI to prioritize target tasks that include rest. This allows the patient to be provided with rehabilitation tasks that are appropriate for the patient by prioritizing the target tasks based on the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the goal setting unit can be performed using AI, or can be performed without AI. For example, the goal setting unit can execute a process in which the generation AI estimates the patient's emotions and prioritizes the target tasks.

[0091] When setting goals, the goal setting unit can set goals based on the patient's occupation or hobbies. For example, the goal setting unit allows the generation AI to set goal tasks to improve skills related to the patient's occupation. The goal setting unit can also allow the generation AI to set goal tasks that incorporate activities related to the patient's hobbies. The goal setting unit can also allow the generation AI to set goal tasks that increase motivation based on the patient's occupation or hobbies. In this way, setting goals based on the patient's occupation or hobbies can increase motivation. Some or all of the above-mentioned processing in the goal setting unit may be performed using AI, or may be performed without using AI. For example, the goal setting unit can execute a process in which the generation AI sets goals based on the patient's occupation or hobbies.

[0092] The goal setting unit can set goals taking into consideration the support of the patient's family and friends when setting goals. For example, the goal setting unit causes the generation AI to set rehabilitation tasks that the patient's family can cooperate with. The goal setting unit can also cause the generation AI to set goal tasks that incorporate activities that the patient's friends can support. The goal setting unit can also set goal tasks that the generation AI can achieve together, taking into consideration the support of the patient's family and friends. This makes it possible to set more realistic and achievable goals by taking into consideration the support of the patient's family and friends. Some or all of the above-mentioned processing in the goal setting unit may be performed using AI, or may be performed without using AI. For example, the goal setting unit can cause the generation AI to execute a process of setting goals taking into consideration the support of the patient's family and friends.

[0093] When setting goals, the goal setting unit can set goals taking into account the patient's geographical conditions. For example, the goal setting unit can cause the generation AI to set appropriate rehabilitation tasks taking into account the climate of the area where the patient lives. The goal setting unit can also cause the generation AI to set goal tasks that require medical visits taking into account access to medical facilities in the area where the patient lives. The goal setting unit can also cause the generation AI to set rehabilitation tasks that can be done outdoors taking into account the geographical conditions of the area where the patient lives. In this way, more realistic and achievable goals can be set by taking into account the patient's geographical conditions. Some or all of the above-mentioned processing in the goal setting unit may be performed using AI, or may be performed without using AI. For example, the goal setting unit can execute a process in which the generation AI sets goals taking into account the patient's geographical conditions.

[0094] The standing position setting unit can estimate the patient's emotions and adjust the patient's post-recovery standing position based on the estimated patient emotions. For example, if the patient feels anxious, the generation AI can suggest easy tasks and gradually increase the workload. Alternatively, if the patient feels confident, the generation AI can suggest more difficult tasks to encourage the patient's willingness to take on new challenges. Alternatively, if the patient feels tired, the generation AI can suggest tasks that include rest, encouraging a smooth return to work. This allows the patient's post-recovery standing position to be adjusted based on the patient's emotions, providing a return-to-work plan appropriate for the patient. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the standing position setting unit may be performed using AI, or may be performed without AI. For example, the standing position setting unit can execute a process in which the generation AI estimates the patient's emotions and adjusts the patient's post-recovery standing position.

[0095] When setting a standing position, the standing position setting unit can set specific goals by taking into account the patient's work environment and work content. For example, the standing position setting unit allows the generation AI to propose appropriate work content by taking into account the patient's work environment. The standing position setting unit can also allow the generation AI to set goals aimed at improving skills by taking into account the patient's work content. The standing position setting unit can also allow the generation AI to set gradual goals based on the patient's work environment and work content. This makes it possible to set more realistic and achievable goals by taking into account the patient's work environment and work content. Some or all of the above-mentioned processing in the standing position setting unit may be performed using AI, or may be performed without using AI. For example, the standing position setting unit can execute a process in which the generation AI sets specific goals by taking into account the patient's work environment and work content.

[0096] The position setting unit can set goals taking into account the patient's skills and experience when setting the position. For example, the position setting unit can take into account the patient's skills and set goals that the generation AI aims to improve. The position setting unit can also take into account the patient's experience and suggest work content that allows the generation AI to utilize that experience. The position setting unit can also allow the generation AI to set gradual goals based on the patient's skills and experience. This makes it possible to set more realistic and achievable goals by taking into account the patient's skills and experience. Some or all of the above-mentioned processing in the position setting unit may be performed using AI, or may be performed without using AI. For example, the position setting unit can execute a process in which the generation AI sets goals taking into account the patient's skills and experience.

[0097] When setting the standing position, the standing position setting unit can set gradual goals according to the patient's rehabilitation progress. For example, the standing position setting unit allows the generation AI to set short-term and long-term goals based on the patient's rehabilitation progress. The standing position setting unit can also allow the generation AI to set goal tasks of gradually increasing difficulty according to the patient's rehabilitation progress. The standing position setting unit can also monitor the patient's rehabilitation progress in real time, and the generation AI can modify the goals as appropriate. This makes it possible to set gradual goals according to the patient's rehabilitation progress, thereby providing a reasonable return plan. Some or all of the above-mentioned processing in the standing position setting unit may be performed using AI, or may be performed without using AI. For example, the standing position setting unit can execute a process in which the generation AI sets gradual goals based on the patient's rehabilitation progress.

[0098] The standing position setting unit can estimate the patient's emotions and prioritize the standing positions after recovery based on the estimated patient emotions. For example, if the patient is feeling anxious, the standing position setting unit can cause the generation AI to prioritize easy tasks. Furthermore, if the patient is feeling confident, the standing position setting unit can cause the generation AI to prioritize difficult tasks. Furthermore, if the patient is tired, the standing position setting unit can cause the generation AI to prioritize tasks that include rest. This allows the patient to be provided with a return-to-work plan appropriate for their recovery by prioritizing the standing positions after recovery based on the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the standing position setting unit can be performed using AI, or without AI. For example, the standing position setting unit can execute a process in which the generation AI estimates the patient's emotions and prioritizes the standing positions after recovery.

[0099] The standing position setting unit can set goals based on the patient's occupation and hobbies when setting the standing position. For example, the generation AI in the standing position setting unit sets goal tasks to improve skills related to the patient's occupation. The standing position setting unit can also set goal tasks that incorporate activities related to the patient's hobbies. The standing position setting unit can also set goal tasks that increase motivation based on the patient's occupation and hobbies. In this way, motivation can be increased by setting goals based on the patient's occupation and hobbies. Some or all of the above-mentioned processing in the standing position setting unit may be performed using AI, or may be performed without using AI. For example, the standing position setting unit can execute a process in which the generation AI sets goals based on the patient's occupation and hobbies.

[0100] When setting the standing position, the standing position setting unit can set goals taking into consideration the support of the patient's family and friends. For example, the standing position setting unit causes the generation AI to set rehabilitation tasks that the patient's family can cooperate with. The standing position setting unit can also cause the generation AI to set goal tasks that incorporate activities that the patient's friends can support. The standing position setting unit can also set goal tasks that the generation AI can achieve together, taking into consideration the support of the patient's family and friends. This makes it possible to set more realistic and achievable goals by taking into consideration the support of the patient's family and friends. Some or all of the above-mentioned processing in the standing position setting unit may be performed using AI, or may be performed without using AI. For example, the standing position setting unit can cause the generation AI to execute a process of setting goals taking into consideration the support of the patient's family and friends.

[0101] When setting the standing position, the standing position setting unit can set a goal taking into account the geographical conditions of the patient. For example, the standing position setting unit can cause the generation AI to set an appropriate rehabilitation task taking into account the climate of the area where the patient lives. The standing position setting unit can also cause the generation AI to set a goal task that requires medical visits taking into account access to medical facilities in the area where the patient lives. The standing position setting unit can also cause the generation AI to set a rehabilitation task that can be performed outdoors taking into account the geographical conditions of the area where the patient lives. In this way, by taking the patient's geographical conditions into account, more realistic and achievable goals can be set. Some or all of the above-mentioned processing in the standing position setting unit may be performed using AI, or may be performed without using AI. For example, the standing position setting unit can execute a process in which the generation AI sets a goal taking into account the patient's geographical conditions.

[0102] The monitoring unit can estimate the patient's emotions and adjust the monitoring frequency based on the estimated patient emotions. For example, if the patient feels anxious, the monitoring unit can have the generation AI monitor the patient more frequently to provide a sense of security. If the patient feels confident, the monitoring unit can also have the generation AI reduce the monitoring frequency to promote self-management. If the patient feels tired, the monitoring unit can also adjust the monitoring frequency to promote reasonable rehabilitation. This allows monitoring appropriate for the patient to be provided by adjusting the monitoring frequency based on the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the monitoring unit may be performed using AI, or may be performed without AI. For example, the monitoring unit can execute a process in which the generation AI estimates the patient's emotions and adjusts the monitoring frequency.

[0103] During monitoring, the monitoring unit can analyze the patient's rehabilitation progress in real time and revise goals as necessary. For example, the monitoring unit can analyze the patient's rehabilitation progress in real time, and the generating AI can revise goals as appropriate. Furthermore, if the patient's rehabilitation progress is slow, the monitoring unit can have the generating AI reset goals and provide appropriate support. Furthermore, if the patient's rehabilitation progress is going well, the monitoring unit can have the generating AI raise goals and encourage further challenges. This enables appropriate goal revision by analyzing the patient's rehabilitation progress in real time. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can execute a process in which the generating AI analyzes the patient's rehabilitation progress in real time and revise goals.

[0104] The monitoring unit can customize the monitoring method during monitoring, taking into account the patient's living environment and support system. For example, the monitoring unit can set a monitoring method that the generation AI can perform at home, taking into account the patient's home environment. The monitoring unit can also set a monitoring method that allows the generation AI to obtain cooperation from family and friends, taking into account the patient's support system. The monitoring unit can also set a monitoring method that requires the generation AI to visit a hospital or go outside, taking into account the patient's living environment. This makes it possible to provide a more realistic and appropriate monitoring method by taking into account the patient's living environment and support system. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can execute a process in which the generation AI customizes the monitoring method, taking into account the patient's living environment and support system.

[0105] During monitoring, the monitoring unit can set step-by-step goals according to the patient's rehabilitation progress. For example, the monitoring unit allows the generation AI to set short-term and long-term goals based on the patient's rehabilitation progress. The monitoring unit can also allow the generation AI to set goal tasks of gradually increasing difficulty according to the patient's rehabilitation progress. The monitoring unit can also monitor the patient's rehabilitation progress in real time, and the generation AI can modify the goals as appropriate. This allows for step-by-step goals to be set according to the patient's rehabilitation progress, thereby promoting a reasonable rehabilitation. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can execute a process in which the generation AI sets step-by-step goals based on the patient's rehabilitation progress.

[0106] The monitoring unit can estimate the patient's emotions and determine monitoring priorities based on the estimated patient emotions. For example, if the patient feels anxious, the generation AI can monitor the patient more frequently to provide a sense of security. If the patient feels confident, the monitoring unit can also reduce the frequency of monitoring to promote self-management. If the patient feels tired, the monitoring unit can also adjust the frequency of monitoring to promote reasonable rehabilitation. This allows monitoring appropriate for the patient to be determined based on the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the monitoring unit may be performed using AI, or may be performed without AI. For example, the monitoring unit can execute a process in which the generation AI estimates the patient's emotions and determines monitoring priorities.

[0107] During monitoring, the monitoring unit can set a monitoring method based on the patient's occupation or hobbies. For example, the monitoring unit allows the generation AI to set a method for monitoring activities related to the patient's occupation. The monitoring unit can also allow the generation AI to set a method for monitoring activities related to the patient's hobbies. The monitoring unit can also allow the generation AI to set a monitoring method that increases motivation based on the patient's occupation or hobbies. In this way, motivation can be increased by setting a monitoring method based on the patient's occupation or hobbies. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI or without AI. For example, the monitoring unit can execute a process in which the generation AI sets a monitoring method based on the patient's occupation or hobbies.

[0108] The monitoring unit can set a monitoring method during monitoring, taking into consideration the support of the patient's family and friends. For example, the monitoring unit allows the generation AI to set a monitoring method that allows the patient's family to cooperate. The monitoring unit can also allow the generation AI to set a monitoring method that allows the patient's friends to provide support. The monitoring unit can also set a monitoring method that the generation AI can use together, taking into consideration the support of the patient's family and friends. This makes it possible to provide a more realistic and appropriate monitoring method by taking into consideration the support of the patient's family and friends. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can execute a process in which the generation AI sets a monitoring method, taking into consideration the support of the patient's family and friends.

[0109] During monitoring, the monitoring unit can set a monitoring method taking into account the geographical conditions of the patient. For example, the monitoring unit can cause the generation AI to set an appropriate monitoring method taking into account the climate of the area where the patient lives. The monitoring unit can also set a monitoring method that requires medical visits to the hospital taking into account access to medical facilities in the area where the patient lives. The monitoring unit can also set a monitoring method that can be performed outdoors taking into account the geographical conditions of the area where the patient lives. This makes it possible to provide a more realistic and appropriate monitoring method by taking into account the geographical conditions of the patient. Some or all of the above-mentioned processing in the monitoring unit may be performed using AI, or may be performed without using AI. For example, the monitoring unit can execute a process in which the generation AI sets a monitoring method taking into account the geographical conditions of the patient.

[0110] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated patient emotions. For example, if the patient feels anxious, the data collection unit can increase the frequency of data collection to provide a sense of security. If the patient feels confident, the data collection unit can also reduce the frequency of data collection to promote self-management. If the patient feels tired, the data collection unit can also adjust the timing of data collection to promote reasonable rehabilitation. This allows data collection tailored to the patient by adjusting the timing of data collection based on the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or may be performed without AI. For example, the data collection unit can execute a process in which the generative AI estimates the patient's emotions and adjusts the timing of data collection.

[0111] The data collection unit can collect the patient's rehabilitation progress in real time during data collection. For example, the data collection unit collects the patient's rehabilitation progress in real time, and the generating AI adjusts the goals as appropriate. Furthermore, if the patient's rehabilitation progress is slow, the data collection unit can cause the generating AI to reset the goals and provide appropriate support. Furthermore, if the patient's rehabilitation progress is going well, the data collection unit can cause the generating AI to raise the goals and encourage further challenges. In this way, collecting the patient's rehabilitation progress in real time enables appropriate goal adjustment. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can cause the generating AI to collect the patient's rehabilitation progress in real time and execute a process to adjust the goals.

[0112] The data collection unit can customize the data collection method when collecting data, taking into account the patient's living environment and support system. For example, the data collection unit can set a data collection method that can be done at home, taking into account the patient's home environment. The data collection unit can also set a data collection method that can obtain the cooperation of family and friends, taking into account the patient's support system. The data collection unit can also set a data collection method that requires hospital visits or outings, taking into account the patient's living environment. This makes it possible to provide a more realistic and appropriate data collection method by taking into account the patient's living environment and support system. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can execute a process in which the generation AI customizes the data collection method, taking into account the patient's living environment and support system.

[0113] The data collection unit can estimate the patient's emotions and prioritize data collection based on the estimated patient emotions. For example, if the patient feels anxious, the data collection unit can increase the frequency of data collection to provide a sense of security. If the patient feels confident, the data collection unit can also reduce the frequency of data collection to promote self-management. If the patient feels tired, the data collection unit can also adjust the timing of data collection to promote gentle rehabilitation. This allows data collection appropriate for the patient to be prioritized based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or may be performed without AI. For example, the data collection unit can execute a process in which the generative AI estimates the patient's emotions and prioritizes data collection.

[0114] The data collection unit can set a data collection method based on the patient's occupation or hobbies when collecting data. For example, the data collection unit can set a method for collecting data on activities related to the patient's occupation. The data collection unit can also set a method for collecting data on activities related to the patient's hobbies. The data collection unit can also set a data collection method that increases motivation based on the patient's occupation or hobbies. In this way, setting a data collection method based on the patient's occupation or hobbies can increase motivation. Some or all of the above-mentioned processing in the data collection unit may be performed using AI or may be performed without using AI. For example, the data collection unit can execute a process in which the generation AI sets a data collection method based on the patient's occupation or hobbies.

[0115] The data collection unit can set a data collection method taking into consideration the support of the patient's family and friends when collecting data. For example, the data collection unit sets a data collection method that allows the patient's family to cooperate. The data collection unit can also set a data collection method that allows the patient's friends to support. The data collection unit can also set a data collection method that can be carried out jointly, taking into consideration the support of the patient's family and friends. This makes it possible to provide a more realistic and appropriate data collection method by taking into consideration the support of the patient's family and friends. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can execute a process in which the generation AI sets a data collection method taking into consideration the support of the patient's family and friends.

[0116] When collecting data, the data collection unit can set a data collection method taking into account the patient's geographical conditions. The data collection unit can, for example, set an appropriate data collection method taking into account the climate of the area where the patient lives. The data collection unit can also set a data collection method that requires visiting a medical facility in the patient's area taking into account access to medical facilities in the patient's area. The data collection unit can also set a data collection method that can be done outdoors taking into account the geographical conditions of the patient's area. This makes it possible to provide a more realistic and appropriate data collection method by taking into account the patient's geographical conditions. Some or all of the above-mentioned processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit can execute a process in which the generation AI sets a data collection method taking into account the patient's geographical conditions.

[0117] The data analysis unit can estimate the patient's emotions and adjust the data analysis method based on the estimated patient emotions. For example, if the patient feels anxious, the data analysis unit can increase the frequency of data analysis to provide a sense of security. If the patient feels confident, the data analysis unit can also reduce the frequency of data analysis to promote self-management. If the patient feels tired, the data analysis unit can also adjust the timing of data analysis to promote reasonable rehabilitation. This allows data analysis tailored to the patient to be provided by adjusting the data analysis method based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the data analysis unit may be performed using AI, or may be performed without AI. For example, the data analysis unit can execute a process in which generative AI estimates the patient's emotions and adjusts the data analysis method.

[0118] The data analysis unit can analyze the patient's rehabilitation progress in real time during data analysis. For example, the data analysis unit analyzes the patient's rehabilitation progress in real time, and the generating AI adjusts the goals as appropriate. Furthermore, if the patient's rehabilitation progress is slow, the data analysis unit can cause the generating AI to reset the goals and provide appropriate support. Furthermore, if the patient's rehabilitation progress is going well, the data analysis unit can cause the generating AI to raise the goals and encourage further challenges. This enables appropriate goal adjustment by analyzing the patient's rehabilitation progress in real time. Some or all of the above-mentioned processing in the data analysis unit may be performed using AI, or may be performed without using AI. For example, the data analysis unit can cause the generating AI to analyze the patient's rehabilitation progress in real time and adjust the goals.

[0119] During data analysis, the data analysis unit can customize the analysis method by taking into account the patient's living environment and support system. For example, the data analysis unit can set a data analysis method that can be performed at home by taking into account the patient's home environment. The data analysis unit can also set a data analysis method that can be performed with the cooperation of family and friends by taking into account the patient's support system. The data analysis unit can also set a data analysis method that requires hospital visits or outings by taking into account the patient's living environment. This makes it possible to provide a more realistic and appropriate data analysis method by taking into account the patient's living environment and support system. Some or all of the above-described processing in the data analysis unit may be performed using AI, or may be performed without using AI. For example, the data analysis unit can execute a process in which the generation AI customizes the data analysis method by taking into account the patient's living environment and support system.

[0120] The data analysis unit can estimate the patient's emotions and determine the priority of data analysis based on the estimated patient's emotions. For example, if the patient feels anxious, the data analysis unit can increase the frequency of data analysis to provide a sense of security. If the patient feels confident, the data analysis unit can also reduce the frequency of data analysis to promote self-management. If the patient feels tired, the data analysis unit can also adjust the timing of data analysis to promote reasonable rehabilitation. This allows data analysis appropriate for the patient to be provided by determining the priority of data analysis based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the data analysis unit may be performed using AI, or may be performed without AI. For example, the data analysis unit can execute a process in which generative AI estimates the patient's emotions and determines the priority of data analysis.

[0121] During data analysis, the data analysis unit can set an analysis method based on the patient's occupation or hobbies. For example, the data analysis unit can set a method for analyzing data on activities related to the patient's occupation. The data analysis unit can also set a method for analyzing data on activities related to the patient's hobbies. The data analysis unit can also set a data analysis method that enhances motivation based on the patient's occupation or hobbies. In this way, motivation can be enhanced by setting an analysis method based on the patient's occupation or hobbies. Some or all of the above-described processing in the data analysis unit may be performed using AI or without AI. For example, the data analysis unit can execute a process in which the generation AI sets an analysis method based on the patient's occupation or hobbies.

[0122] The data analysis unit can set an analysis method taking into consideration the support of the patient's family and friends when analyzing data. For example, the data analysis unit sets a data analysis method that allows the patient's family to cooperate. The data analysis unit can also set a data analysis method that allows the patient's friends to support. The data analysis unit can also set a data analysis method that can be performed jointly, taking into consideration the support of the patient's family and friends. This makes it possible to provide a more realistic and appropriate data analysis method by taking into consideration the support of the patient's family and friends. Some or all of the above-mentioned processing in the data analysis unit may be performed using AI, or may be performed without using AI. For example, the data analysis unit can execute a process in which the generation AI sets an analysis method taking into consideration the support of the patient's family and friends.

[0123] When analyzing data, the data analysis unit can set an analysis method taking into account the patient's geographical conditions. The data analysis unit can set an appropriate data analysis method, for example, taking into account the climate of the area where the patient lives. The data analysis unit can also set a data analysis method that requires a hospital visit, taking into account access to medical facilities in the patient's area. The data analysis unit can also set a data analysis method that can be performed outdoors, taking into account the geographical conditions of the area where the patient lives. This makes it possible to provide a more realistic and appropriate data analysis method by taking into account the patient's geographical conditions. Some or all of the above-mentioned processing in the data analysis unit may be performed using AI, or may be performed without using AI. For example, the data analysis unit can execute a process in which the generation AI sets an analysis method taking into account the patient's geographical conditions.

[0124] The providing unit can estimate the patient's emotions and adjust the method of providing the target task and post-recovery position based on the estimated patient's emotions. For example, if the patient feels anxious, the providing unit can have the generating AI provide an easy target task, allowing the patient to feel a sense of accomplishment. Furthermore, if the patient feels confident, the providing unit can have the generating AI provide a more difficult target task, encouraging the patient to take on the challenge. Furthermore, if the patient feels tired, the providing unit can provide a target task that includes rest, promoting a gentle rehabilitation. This allows the providing unit to adjust the method of providing the target task and post-recovery position appropriate for the patient by adjusting the method of providing the target task and post-recovery position based on the patient's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generating AI. The generating AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can execute a process in which the generating AI estimates the patient's emotions and adjusts the method of providing the target task and post-recovery position.

[0125] The providing unit can update the provided content to reflect the patient's rehabilitation progress in real time when providing the content. For example, the providing unit reflects the patient's rehabilitation progress in real time, and the generating AI adjusts the goals as appropriate. Furthermore, if the patient's rehabilitation progress is slow, the providing unit can have the generating AI reset the goals and provide appropriate support. Furthermore, if the patient's rehabilitation progress is going well, the providing unit can have the generating AI raise the goals and encourage further challenges. This enables appropriate goal adjustment by reflecting the patient's rehabilitation progress in real time. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can execute a process in which the generating AI reflects the patient's rehabilitation progress in real time and updates the provided content.

[0126] When providing the information, the providing unit can customize the provision method by taking into account the patient's living environment and support system. For example, the providing unit can provide target tasks that can be done at home by taking into account the patient's home environment. The providing unit can also provide target tasks that can be done at home by taking into account the patient's support system. The providing unit can also provide target tasks that require hospital visits or outings by taking into account the patient's living environment. This makes it possible to provide a more realistic and appropriate provision method by taking into account the patient's living environment and support system. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can execute a process in which the generation AI customizes the provision method by taking into account the patient's living environment and support system.

[0127] The providing unit can estimate the patient's emotions and determine the priority of providing target tasks and post-recovery positions based on the estimated patient's emotions. For example, if the patient is feeling anxious, the providing unit can cause the generation AI to prioritize providing easy target tasks. Furthermore, if the patient is feeling confident, the providing unit can cause the generation AI to prioritize providing more difficult target tasks. Furthermore, if the patient is tired, the providing unit can cause the generation AI to prioritize providing target tasks that include rest. Thus, by determining the provision priority based on the patient's emotions, it is possible to provide target tasks and post-recovery positions that are appropriate for the patient. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can execute a process in which the generation AI estimates the patient's emotions and determines the priority of providing target tasks and post-recovery positions.

[0128] The providing unit can set the content to be provided based on the patient's occupation or hobbies at the time of providing. For example, the providing unit can provide a target task that improves skills related to the patient's occupation. The providing unit can also provide a target task that incorporates activities related to the patient's hobbies. The providing unit can also provide a target task that increases motivation based on the patient's occupation or hobbies. In this way, setting the content to be provided based on the patient's occupation or hobbies can increase motivation. Some or all of the above-mentioned processing in the providing unit may be performed using AI or may be performed without using AI. For example, the providing unit can execute a process in which the generation AI sets the content to be provided based on the patient's occupation or hobbies.

[0129] The providing unit can set the content to be provided taking into consideration the support of the patient's family and friends when providing the content. For example, the providing unit provides a goal task that the patient's family can cooperate with. The providing unit can also provide a goal task that incorporates activities that the patient's friends can support. The providing unit can also provide a goal task that can be achieved jointly, taking into consideration the support of the patient's family and friends. In this way, more realistic and appropriate content to be provided can be provided by taking into consideration the support of the patient's family and friends. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can execute a process in which the generation AI sets the content to be provided taking into consideration the support of the patient's family and friends.

[0130] The providing unit can set the content to be provided taking into account the geographical conditions of the patient when providing the content. The providing unit can, for example, provide an appropriate target task by taking into account the climate of the area where the patient lives. The providing unit can also provide a target task that requires a hospital visit by taking into account the accessibility of medical facilities in the area where the patient lives. The providing unit can also provide a target task that can be done outdoors by taking into account the geographical conditions of the area where the patient lives. In this way, more realistic and appropriate content can be provided by taking into account the geographical conditions of the patient. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit can execute a process in which a generation AI sets the content to be provided by taking into account the geographical conditions of the patient.

[0131] The goal correction unit can estimate the patient's emotions and adjust the timing of goal correction based on the estimated patient's emotions. For example, if the patient feels anxious, the generation AI can frequently correct goals to provide a sense of security. Furthermore, if the patient feels confident, the goal correction unit can reduce the frequency of goal correction to promote self-management. Furthermore, if the patient feels tired, the generation AI can adjust the timing of goal correction to promote reasonable rehabilitation. This allows for goal correction tailored to the patient by adjusting the timing of goal correction based on the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the goal correction unit may be performed using AI, or may be performed without AI. For example, the goal correction unit can execute a process in which the generation AI estimates the patient's emotions and adjusts the timing of goal correction.

[0132] When correcting goals, the goal correction unit can reflect the patient's rehabilitation progress in real time and correct the goals. For example, the goal correction unit reflects the patient's rehabilitation progress in real time, and the generating AI corrects the goals as appropriate. Furthermore, if the patient's rehabilitation progress is slow, the goal correction unit can cause the generating AI to reset the goals and provide appropriate support. Furthermore, if the patient's rehabilitation progress is going well, the goal correction unit can cause the generating AI to raise the goals and encourage further challenges. This enables appropriate goal correction by reflecting the patient's rehabilitation progress in real time. Some or all of the above-mentioned processing in the goal correction unit may be performed using AI, or may be performed without using AI. For example, the goal correction unit can execute a process in which the generating AI reflects the patient's rehabilitation progress in real time and corrects the goals.

[0133] The goal correction unit can customize the goal correction method by taking into account the patient's living environment and support system when correcting goals. For example, the goal correction unit can set a goal correction method that can be performed at home by taking into account the patient's home environment. The goal correction unit can also set a goal correction method that requires cooperation from family and friends by taking into account the patient's support system. The goal correction unit can also set a goal correction method that requires hospital visits or outings by taking into account the patient's living environment. This makes it possible to provide a more realistic and appropriate goal correction method by taking into account the patient's living environment and support system. Some or all of the above-mentioned processing in the goal correction unit may be performed using AI, or may be performed without using AI. For example, the goal correction unit can execute a process in which the generation AI customizes the goal correction method by taking into account the patient's living environment and support system.

[0134] The goal correction unit can estimate the patient's emotions and prioritize goal correction based on the estimated patient's emotions. For example, if the patient feels anxious, the generation AI can frequently revise goals to provide a sense of security. If the patient feels confident, the goal correction unit can also reduce the frequency of goal correction to promote self-management. If the patient feels tired, the generation AI can adjust the timing of goal correction to promote a more natural rehabilitation. This allows for goal correction that is appropriate for the patient by prioritizing goal correction based on the patient's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the goal correction unit may be performed using AI, or may be performed without AI. For example, the goal correction unit can execute a process in which the generation AI estimates the patient's emotions and prioritizes goal correction.

[0135] When correcting a goal, the goal correction unit can correct the goal based on the patient's occupation or hobbies. For example, the goal correction unit corrects a goal task to improve skills related to the patient's occupation. The goal correction unit can also correct a goal task that incorporates activities related to the patient's hobbies. The goal correction unit can also correct a goal task to increase motivation based on the patient's occupation or hobbies. In this way, by correcting the goal based on the patient's occupation or hobbies, motivation can be increased. Some or all of the above-mentioned processing in the goal correction unit may be performed using AI, or may be performed without using AI. For example, the goal correction unit can execute a process in which the generation AI corrects the goal based on the patient's occupation or hobbies.

[0136] When correcting a goal, the goal correction unit can correct the goal taking into consideration the support of the patient's family and friends. For example, the goal correction unit corrects a goal task that the patient's family can cooperate with. The goal correction unit can also correct a goal task that incorporates activities that the patient's friends can support. The goal correction unit can also correct a goal task that can be achieved jointly, taking into consideration the support of the patient's family and friends. In this way, by taking into consideration the support of the patient's family and friends, it is possible to provide a more realistic and appropriate goal. Some or all of the above-mentioned processing in the goal correction unit may be performed using AI, or may be performed without using AI. For example, the goal correction unit can execute a process in which the generation AI corrects the goal taking into consideration the support of the patient's family and friends.

[0137] The goal correction unit can correct the goal by taking into account the patient's geographical conditions when correcting the goal. For example, the goal correction unit can correct an appropriate goal task by taking into account the climate of the area where the patient lives. The goal correction unit can also correct a goal task that requires medical visits by taking into account access to medical facilities in the area where the patient lives. The goal correction unit can also correct a goal task that can be done outdoors by taking into account the geographical conditions of the area where the patient lives. In this way, by taking into account the patient's geographical conditions, more realistic and appropriate goals can be provided. Some or all of the above-mentioned processing in the goal correction unit may be performed using AI, or may be performed without using AI. For example, the goal correction unit can execute a process in which the generation AI corrects the goal by taking into account the patient's geographical conditions. === Hard Collateral 1-1 === For example, each of a plurality of elements including a goal setting unit, a standing position setting unit, a monitoring unit, a data collection unit, a data analysis unit, a providing unit, and a goal correction unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the goal setting unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The standing position setting unit is realized by the specific processing unit 290 of the data processing device 12. The monitoring unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The data collection unit is realized using the camera 42 and the microphone 38B of the smart device 14. The data analysis unit is realized by the specific processing unit 290 of the data processing device 12. The providing unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The goal correction unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === For example, each of a plurality of elements including a goal setting unit, a standing position setting unit, a monitoring unit, a data collection unit, a data analysis unit, a providing unit, and a goal correction unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the goal setting unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The standing position setting unit is realized by the specific processing unit 290 of the data processing device 12. The monitoring unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The data collection unit is realized using the camera 42 and the microphone 238 of the smart glasses 214. The data analysis unit is realized by the specific processing unit 290 of the data processing device 12. The providing unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The goal correction unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === For example, each of a plurality of elements including the goal setting unit, standing position setting unit, monitoring unit, data collection unit, data analysis unit, provision unit, and goal correction unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the goal setting unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The standing position setting unit is realized by the specific processing unit 290 of the data processing device 12. The monitoring unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The data collection unit is realized using the camera 42 and the microphone 238 of the headset type terminal 314. The data analysis unit is realized by the specific processing unit 290 of the data processing device 12. The provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The goal correction unit is realized by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === For example, each of a plurality of elements including the goal setting unit, the position setting unit, the monitoring unit, the data collection unit, the data analysis unit, the provision unit, and the goal correction unit is realized by at least one of the robot 414 and the data processing device 12. For example, the goal setting unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The position setting unit is realized by the specific processing unit 290 of the data processing device 12. The monitoring unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The data collection unit is realized using the camera 42 and the microphone 238 of the robot 414. The data analysis unit is realized by the specific processing unit 290 of the data processing device 12. The provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The goal correction unit is realized by the specific processing unit 290 of the data processing device 12.

[0138] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0139] The support system can also introduce a reward system in which the generating AI increases the patient's motivation based on the patient's rehabilitation progress. For example, points can be awarded each time a rehabilitation goal is achieved, and a reward is provided when a certain number of points are accumulated. The generating AI can also send encouraging messages when the patient achieves a goal they have set. Furthermore, the generating AI can suggest new challenges based on the patient's progress, allowing them to feel a sense of accomplishment. This makes it easier for patients to maintain their motivation for rehabilitation.

[0140] The support system's generative AI can also share a patient's rehabilitation plan with other patients based on the patient's rehabilitation progress. For example, by referring to the rehabilitation plans of other patients with similar symptoms, more effective rehabilitation methods can be found. Patients can also share their rehabilitation progress and encourage each other, increasing their motivation. Furthermore, the generative AI can share the patient's rehabilitation plan anonymously and receive feedback from other patients. This can reduce patients' anxiety about rehabilitation and enable more effective rehabilitation.

[0141] The support system's generating AI can also customize a patient's rehabilitation plan based on the patient's rehabilitation progress. For example, the generating AI can change the content of rehabilitation and suggest more effective rehabilitation methods depending on the patient's rehabilitation progress. The generating AI can also adjust the frequency and intensity of rehabilitation depending on the patient's rehabilitation progress. Furthermore, the generating AI can also reset rehabilitation goals depending on the patient's rehabilitation progress, allowing them to feel a sense of accomplishment. This makes it easier for patients to maintain their motivation for rehabilitation.

[0142] The support system's generative AI can also share the patient's rehabilitation plan with other medical professionals based on the patient's rehabilitation progress. For example, the patient's rehabilitation progress can be shared with doctors and physical therapists to find more effective rehabilitation methods. In addition, by receiving feedback from medical professionals, the generative AI can revise the rehabilitation plan and provide more effective rehabilitation. Furthermore, sharing the patient's rehabilitation progress with other medical professionals can also allow for an objective evaluation of the rehabilitation progress. This can reduce patients' anxiety about rehabilitation and enable more effective rehabilitation.

[0143] The support system also allows the generative AI to share the patient's rehabilitation plan with family members based on the patient's rehabilitation progress. For example, the patient's rehabilitation progress can be shared with family members, who can then support the rehabilitation. In addition, by receiving feedback from family members, the generative AI can revise the rehabilitation plan, enabling more effective rehabilitation. Furthermore, by sharing the patient's rehabilitation progress with family members, family members can understand the progress of rehabilitation and encourage the patient. This reduces the patient's anxiety about rehabilitation and allows for more effective rehabilitation.

[0144] The support system's generative AI can also share a patient's rehabilitation plan with other patients based on the patient's rehabilitation progress. For example, by referring to the rehabilitation plans of other patients with similar symptoms, more effective rehabilitation methods can be found. Patients can also share their rehabilitation progress and encourage each other, increasing their motivation. Furthermore, the generative AI can share the patient's rehabilitation plan anonymously and receive feedback from other patients. This can reduce patients' anxiety about rehabilitation and enable more effective rehabilitation.

[0145] The support system's generating AI can also customize a patient's rehabilitation plan based on the patient's rehabilitation progress. For example, the generating AI can change the content of rehabilitation and suggest more effective rehabilitation methods depending on the patient's rehabilitation progress. The generating AI can also adjust the frequency and intensity of rehabilitation depending on the patient's rehabilitation progress. Furthermore, the generating AI can also reset rehabilitation goals depending on the patient's rehabilitation progress, allowing them to feel a sense of accomplishment. This makes it easier for patients to maintain their motivation for rehabilitation.

[0146] The support system's generative AI can also share the patient's rehabilitation plan with other medical professionals based on the patient's rehabilitation progress. For example, the patient's rehabilitation progress can be shared with doctors and physical therapists to find more effective rehabilitation methods. In addition, by receiving feedback from medical professionals, the generative AI can revise the rehabilitation plan and provide more effective rehabilitation. Furthermore, sharing the patient's rehabilitation progress with other medical professionals can also allow for an objective evaluation of the rehabilitation progress. This can reduce patients' anxiety about rehabilitation and enable more effective rehabilitation.

[0147] The support system also allows the generative AI to share the patient's rehabilitation plan with family members based on the patient's rehabilitation progress. For example, the patient's rehabilitation progress can be shared with family members, who can then support the rehabilitation. In addition, by receiving feedback from family members, the generative AI can revise the rehabilitation plan, enabling more effective rehabilitation. Furthermore, by sharing the patient's rehabilitation progress with family members, family members can understand the progress of rehabilitation and encourage the patient. This reduces the patient's anxiety about rehabilitation and allows for more effective rehabilitation.

[0148] The support system's generative AI can also share a patient's rehabilitation plan with other patients based on the patient's rehabilitation progress. For example, by referring to the rehabilitation plans of other patients with similar symptoms, more effective rehabilitation methods can be found. Patients can also share their rehabilitation progress and encourage each other, increasing their motivation. Furthermore, the generative AI can share the patient's rehabilitation plan anonymously and receive feedback from other patients. This can reduce patients' anxiety about rehabilitation and enable more effective rehabilitation.

[0149] The processing flow of the second embodiment will be briefly explained below.

[0150] Step 1: The goal setting unit sets goal tasks based on the patient's current condition or treatment plan. For example, the goal setting unit sets rehabilitation goals based on the patient's medical records and diagnostic results. Generative AI can also be used to set gradual goals according to the patient's level of physical recovery. Step 2: The position setting unit determines the patient's position after recovery based on the target tasks set by the goal setting unit and sets goals. For example, the position setting unit sets goals according to the patient's work content and working style after returning to work. In addition, using generation AI, it is also possible to set goals that take into account the patient's living environment and support system. Step 3: The monitoring unit monitors the patient's progress in real time based on the goals set by the position setting unit and modifies the target tasks as necessary. For example, the monitoring unit analyzes the patient's rehabilitation progress in real time, and the generation AI modifies the goals as appropriate. It can also estimate the patient's emotions and adjust the monitoring frequency based on the estimated emotions.

[0151] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0152] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0156] 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.

[0157] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0163] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0164] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0167] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0169] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0172] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0173] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0174] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0175] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0177] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0178] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0180] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0184] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0187] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0188] 7, a 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.

[0189] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0190] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0191] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0192] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0193] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0194] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0195] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0196] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0197] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0198] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0199] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0200] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0201] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0202] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0203] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0204] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0205] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0206] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0207] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0208] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0209] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0210] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0211] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0212] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0213] 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.

[0214] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0215] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0216] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0217] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0218] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0219] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0220] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0221] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0222] [Explanation of symbols]

[0223] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a goal setting unit that sets a goal task based on the patient's current condition or treatment plan; a standing position setting unit that determines a standing position after recovery based on the target task set by the target setting unit and sets a target; a monitoring unit that monitors the progress of the patient in real time based on the goal set by the standing position setting unit and corrects the goal task as necessary. A system characterized by:

2. Equipped with a data collection unit that collects information on the patient's rehabilitation progress and level of physical recovery 2. The system of claim 1.

3. a data analysis unit that analyzes the data collected by the data collection unit; 3. The system of claim 2.

4. Equipped with a provision unit that provides the patient with the target task or post-recovery position proposed by the generation AI 2. The system of claim 1.

5. Equipped with a goal correction unit that includes a specific process for the generation AI to correct the target task 2. The system of claim 1.

6. The goal setting unit Estimate the patient's emotions and adjust the difficulty of the target task based on the estimated patient emotions.

2. The system of claim 1.

7. The goal setting unit Analyze the patient's past treatment history and select the optimal target task 2. The system of claim 1.

8. The goal setting unit When setting goals, customize them to take into account the patient's living environment and support system.

2. The system of claim 1.

9. The goal setting unit When setting goals, set gradual goals according to the patient's rehabilitation progress.

2. The system of claim 1.

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