system

Abstract

The system according to this embodiment aims to automatically generate, monitor, and evaluate optimal rehabilitation plans tailored to the individual patient's recovery status. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a support unit, and a monitoring unit. The collection unit collects individual information. The analysis unit analyzes exercise data based on the individual information collected by the collection unit. The generation unit generates a rehabilitation plan based on the exercise data analyzed by the analysis unit. The support unit assists in the execution of the rehabilitation plan generated by the generation unit. The monitoring unit monitors and evaluates the execution of the rehabilitation plan supported by the support unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, an optimal rehabilitation plan according to the recovery status of an individual patient has not been fully automatically generated, monitored, and evaluated, and there is room for improvement.

[0005] The system according to the embodiment aims to automatically generate an optimal rehabilitation plan according to the recovery status of an individual patient, and monitor and evaluate it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a generation unit, a support unit, and a monitoring unit. The data collection unit collects individual information. The analysis unit analyzes exercise data based on the individual information collected by the data collection unit. The generation unit generates a rehabilitation plan based on the exercise data analyzed by the analysis unit. The support unit assists in the execution of the rehabilitation plan generated by the generation unit. The monitoring unit monitors and evaluates the execution of the rehabilitation plan supported by the support unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate, monitor, and evaluate an optimal rehabilitation plan tailored to the individual patient's recovery status. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The rehabilitation coach AI agent system according to an embodiment of the present invention is a system that automatically provides guidance, monitoring, and evaluation of optimal exercises and rehabilitation plans according to the individual patient's recovery status. This rehabilitation coach AI agent system collects the patient's individual information (age, gender, occupation, medical history, emotional state, specific rehabilitation goals, etc.), and the AI ​​agent stores this information. Next, the rehabilitation coach AI agent system collects exercise data from sensors and robotic devices in real time and monitors the patient's recovery status. As a result, the rehabilitation coach AI agent system grasps the patient's rehabilitation progress in detail and automatically generates optimal exercises and rehabilitation plans. Furthermore, the rehabilitation coach AI agent system supports the execution of the generated rehabilitation plan and monitors and evaluates the patient's exercises in real time. For example, when the patient performs an exercise, it analyzes the data obtained from sensors and robotic devices to check whether the exercise is being performed with the correct form. If necessary, the rehabilitation coach AI agent system provides feedback and guidance on correcting the exercise. This mechanism makes rehabilitation progress management effortless and provides high-quality rehabilitation tailored to individual needs. In addition, physical therapists can care for many patients at once, improving the quality of rehabilitation. This enables the rehabilitation coach AI agent system to generate and support the implementation of optimal rehabilitation plans tailored to each individual patient's recovery status.

[0029] The rehabilitation coach AI agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a support unit, and a monitoring unit. The collection unit collects personal information. Personal information includes, but is not limited to, age, gender, occupation, medical history, emotional state, and specific rehabilitation goals. The collection unit provides, for example, an interface for inputting the patient's age and gender. The collection unit may also include a database for recording the patient's occupation and medical history. Furthermore, the collection unit may include sensors for estimating the patient's emotional state. For example, the collection unit provides a form for inputting the patient's age and gender, allowing the patient to input their own information. The collection unit includes a database for recording the patient's occupation and medical history, and can store the patient's information. The collection unit includes sensors for estimating the patient's emotional state and can estimate emotions by analyzing the patient's facial expressions and voice. The analysis unit analyzes movement data based on the personal information collected by the collection unit. Movement data includes, but is not limited to, data from sensors and data from robotic devices. The analysis unit analyzes motion data obtained from sensors to understand the patient's movement patterns. The analysis unit can also analyze motion data obtained from robotic devices to evaluate the patient's motor abilities. Furthermore, the analysis unit can monitor the patient's rehabilitation progress based on the motion data. For example, the analysis unit analyzes motion data obtained from sensors to understand the patient's movement patterns. The analysis unit can analyze motion data obtained from robotic devices to evaluate the patient's motor abilities. The analysis unit can monitor the patient's rehabilitation progress based on the motion data. The generation unit generates a rehabilitation plan based on the motion data analyzed by the analysis unit. The rehabilitation plan may include, but is not limited to, the type, frequency, and intensity of exercises. The generation unit may, for example, select the optimal exercises based on the analyzed motion data. The generation unit can also adjust the frequency and intensity of the exercises. Furthermore, the generation unit can customize the rehabilitation plan to meet the individual needs of the patient.For example, the generation unit selects the optimal exercises based on the analyzed exercise data. The generation unit can adjust the frequency and intensity of the exercises. The generation unit can customize the rehabilitation plan to meet the individual needs of the patient. The support unit assists in the execution of the rehabilitation plan generated by the generation unit. For example, the support unit provides feedback when the patient performs the exercises. The support unit can also monitor the patient's movements in real time and provide guidance on modifying the exercises as needed. Furthermore, the support unit can record the patient's rehabilitation progress and report it to the physical therapist. For example, the support unit provides feedback when the patient performs the exercises. The support unit can monitor the patient's movements in real time and provide guidance on modifying the exercises as needed. The support unit can record the patient's rehabilitation progress and report it to the physical therapist. The monitoring unit monitors and evaluates the execution of the rehabilitation plan supported by the support unit. For example, the monitoring unit monitors the patient's exercise data in real time and evaluates the execution of the rehabilitation plan. The monitoring unit can also provide feedback as needed and provide guidance on modifying the exercises. Furthermore, the monitoring unit can evaluate the effectiveness of the rehabilitation plan and reflect this in the generation of the next rehabilitation plan. For example, the monitoring unit can monitor the patient's exercise data in real time and evaluate the progress of the rehabilitation plan. The monitoring unit can provide feedback as needed and guide the patient in modifying their exercises. The monitoring unit can evaluate the effectiveness of the rehabilitation plan and reflect this in the generation of the next rehabilitation plan. As a result, the rehabilitation coach AI agent system according to this embodiment can generate and support the execution of an optimal rehabilitation plan tailored to the individual patient's recovery status.

[0030] The data collection unit collects personal information. This personal information includes, but is not limited to, age, gender, occupation, medical history, emotional state, and specific rehabilitation goals. For example, the data collection unit provides an interface for inputting the patient's age and gender. It may also include a database for recording the patient's occupation and medical history. Furthermore, the data collection unit may include sensors for estimating the patient's emotional state. For example, the data collection unit provides a form for inputting the patient's age and gender, allowing the patient to enter their information. The data collection unit includes a database for recording the patient's occupation and medical history, allowing it to store patient information. The data collection unit includes sensors for estimating the patient's emotional state, analyzing the patient's facial expressions and voice to estimate their emotions. The data collection unit provides an interface for inputting the patient's age and gender. Specifically, it allows patients to easily input their information using devices such as tablets or smartphones. This enables the data collection unit to quickly and accurately collect basic personal information about the patient. The data collection unit may also include a database for recording the patient's occupation and medical history. For example, detailed records of a patient's medical history and occupation can be used to customize rehabilitation plans. Furthermore, the data collection unit can be equipped with sensors to estimate the patient's emotional state. For instance, facial recognition technology can be used to analyze the patient's facial expressions, and speech recognition technology can be used to analyze the patient's tone of voice and speaking style to estimate their emotional state. This allows the data collection unit to grasp the patient's emotional state in real time and use this information to adjust rehabilitation plans. The data collection unit can centrally manage this information and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes movement data based on individual information collected by the collection unit. Movement data includes, but is not limited to, data from sensors and robotic devices. For example, the analysis unit analyzes movement data obtained from sensors to understand the patient's movement patterns. It can also analyze movement data obtained from robotic devices to evaluate the patient's motor abilities. Furthermore, the analysis unit can monitor the patient's rehabilitation progress based on the movement data. For example, the analysis unit analyzes movement data obtained from sensors to understand the patient's movement patterns. The analysis unit can analyze movement data obtained from robotic devices to evaluate the patient's motor abilities. The analysis unit can monitor the patient's rehabilitation progress based on the movement data. The analysis unit analyzes movement data based on individual information collected by the collection unit. Specifically, it analyzes movement data obtained from sensors and robotic devices in detail, taking into account individual information such as the patient's age, gender, occupation, medical history, and emotional state. For example, it analyzes movement data obtained from sensors to understand the patient's movement patterns. This includes using acceleration and gyroscope sensors to measure the speed and direction of the patient's movements and evaluate the quality and pattern of those movements. The analysis unit can also analyze motion data obtained from robotic devices to evaluate the patient's motor abilities. For example, it can use robotic assist devices to evaluate how much force the patient can exert and what kind of movement support is needed. Furthermore, the analysis unit can monitor the patient's rehabilitation progress based on the motion data. For example, it can analyze regularly collected motion data to evaluate the improvement in the patient's motor abilities and the progress of their rehabilitation. This allows the analysis unit to grasp the patient's rehabilitation progress in real time and adjust the rehabilitation plan as needed. In addition, the analysis unit can utilize past data and statistical information to evaluate the long-term effectiveness of rehabilitation and perform trend analysis. For example, it can evaluate how effective a particular rehabilitation plan was based on past motion data and use this information to improve future rehabilitation plans.This allows the analysis unit to not only grasp the situation in real time, but also to evaluate the long-term effects of rehabilitation and analyze trends, thereby improving the reliability and effectiveness of the entire system.

[0032] The generation unit generates a rehabilitation plan based on the exercise data analyzed by the analysis unit. The rehabilitation plan includes, but is not limited to, the type, frequency, and intensity of exercises. For example, the generation unit selects the optimal exercises based on the analyzed exercise data. The generation unit can also adjust the frequency and intensity of the exercises. Furthermore, the generation unit can customize the rehabilitation plan to meet the individual needs of the patient. For example, the generation unit selects the optimal exercises based on the analyzed exercise data. The generation unit can adjust the frequency and intensity of the exercises. The generation unit can customize the rehabilitation plan to meet the individual needs of the patient. The generation unit generates a rehabilitation plan based on the exercise data analyzed by the analysis unit. Specifically, it selects the optimal type, frequency, and intensity of exercises while considering the patient's exercise ability and rehabilitation progress. For example, based on the analyzed exercise data, it selects exercises suitable for the patient's current exercise ability and incorporates them into the rehabilitation plan. The generation unit can also adjust the frequency and intensity of the exercises. For example, by increasing or decreasing the frequency of exercises or adjusting the intensity according to the patient's rehabilitation progress, it provides an optimal rehabilitation plan. Furthermore, the generation unit can customize rehabilitation plans to meet the individual needs of patients. For example, it can select exercises tailored to the patient's occupation and daily living activities and incorporate them into the rehabilitation plan, resulting in more effective rehabilitation. The generation unit uses AI to analyze this data and simulate multiple scenarios to identify the most effective rehabilitation plan. This allows the generation unit to provide highly accurate rehabilitation plans that meet the individual needs of patients. In addition, the generation unit can continuously revise the rehabilitation plan based on real-time updated data to adapt to the latest situations. For example, if the patient's motor skills or rehabilitation progress changes, the generation unit immediately incorporates the new data and updates the rehabilitation plan. The generation unit can also provide more accurate rehabilitation plans by considering regional characteristics and past rehabilitation history.This allows the generation unit to always provide highly accurate rehabilitation plans based on the latest information, supporting rapid and appropriate rehabilitation.

[0033] The support unit assists in the implementation of the rehabilitation plan generated by the generation unit. For example, the support unit provides feedback when the patient performs exercises. The support unit can also monitor the patient's movements in real time and provide guidance to modify the exercises as needed. Furthermore, the support unit can record the patient's rehabilitation progress and report it to the physical therapist. For example, the support unit provides feedback when the patient performs exercises. The support unit can monitor the patient's movements in real time and provide guidance to modify the exercises as needed. The support unit can record the patient's rehabilitation progress and report it to the physical therapist. The support unit assists in the implementation of the rehabilitation plan generated by the generation unit. Specifically, it provides feedback when the patient performs exercises. For example, it monitors in real time whether the patient is performing the exercises correctly and provides guidance to correct them as needed. This includes using sensors and cameras to analyze the patient's movements in detail and provide feedback to maintain correct form and movement. The support unit can also monitor the patient's movements in real time and provide guidance to modify the exercises as needed. For example, if the patient makes an incorrect movement during exercise, the support unit can immediately provide guidance to correct it and encourage the correct movement. Furthermore, the support department can record the patient's rehabilitation progress and report it to the physical therapist. For example, by recording the patient's exercise performance and progress in detail and reporting it regularly to the physical therapist, the effectiveness of the rehabilitation plan can be evaluated and the plan revised as needed. The support department can centrally manage this information and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and generation departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions can be made. As a result, the support department can collect data efficiently and effectively and improve the overall performance of the system.

[0034] The monitoring unit monitors and evaluates the execution of rehabilitation plans supported by the support unit. For example, the monitoring unit monitors the patient's exercise data in real time and evaluates the progress of the rehabilitation plan. The monitoring unit can also provide feedback as needed and guide the patient in modifying their exercises. Furthermore, the monitoring unit can evaluate the effectiveness of the rehabilitation plan and incorporate this into the generation of the next rehabilitation plan. For example, the monitoring unit monitors the patient's exercise data in real time and evaluates the progress of the rehabilitation plan. The monitoring unit can provide feedback as needed and guide the patient in modifying their exercises. The monitoring unit can evaluate the effectiveness of the rehabilitation plan and incorporate this into the generation of the next rehabilitation plan. The monitoring unit monitors and evaluates the execution of rehabilitation plans supported by the support unit. Specifically, it monitors the patient's exercise data in real time and evaluates the progress of the rehabilitation plan. For example, it uses sensors and cameras to analyze the patient's movements in detail and confirm that the rehabilitation plan is being executed appropriately. The monitoring unit can also provide feedback as needed and guide the patient in modifying their exercises. For example, if a patient makes an incorrect movement during exercise, the monitoring unit immediately provides corrective guidance to encourage the correct movement. Furthermore, the monitoring unit can evaluate the effectiveness of rehabilitation plans and incorporate this into the generation of subsequent rehabilitation plans. For example, it can evaluate how effective a rehabilitation plan was based on the patient's exercise data and use this information to improve future rehabilitation plans. The monitoring unit centrally manages this information and can collaborate with other systems and departments as needed. For instance, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the monitoring unit to collect data efficiently and effectively, improving the overall system performance. Additionally, the monitoring unit can utilize historical data and statistical information to evaluate long-term rehabilitation effectiveness and conduct trend analysis. For example, it can evaluate how effective a particular rehabilitation plan was based on past exercise data and use this information to improve future rehabilitation plans.This allows the monitoring unit to not only grasp the situation in real time, but also to evaluate the long-term effects of rehabilitation and analyze trends, thereby improving the reliability and effectiveness of the entire system.

[0035] The data collection unit can collect individual information such as the patient's age, gender, occupation, medical history, emotional state, and specific rehabilitation goals. For example, the data collection unit may provide an interface for inputting the patient's age and gender. The data collection unit may include a database for recording the patient's occupation and medical history. The data collection unit may include sensors for estimating the patient's emotional state. For example, the data collection unit may provide a form for inputting the patient's age and gender, allowing the patient to input their own information. The data collection unit may include a database for recording the patient's occupation and medical history, allowing it to store patient information. The data collection unit may include sensors for estimating the patient's emotional state, analyzing the patient's facial expressions and voice to estimate their emotions. This allows for detailed collection of the patient's individual information, improving the accuracy of individual rehabilitation plans. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit may input a form for inputting the patient's age and gender into a generating AI, allowing the generating AI to collect the patient's information.

[0036] The analysis unit can analyze motion data obtained from sensors and robotic devices. For example, the analysis unit can analyze motion data obtained from sensors to understand the patient's movement patterns. The analysis unit can analyze motion data obtained from robotic devices to evaluate the patient's motor ability. The analysis unit can monitor the patient's rehabilitation progress based on the motion data. For example, the analysis unit can analyze motion data obtained from sensors to understand the patient's movement patterns. The analysis unit can analyze motion data obtained from robotic devices to evaluate the patient's motor ability. The analysis unit can monitor the patient's rehabilitation progress based on the motion data. As a result, the accuracy of the rehabilitation plan is improved by analyzing motion data obtained from sensors and robotic devices. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input motion data obtained from sensors into a generating AI and have the generating AI perform the analysis of the motion data.

[0037] The generation unit can generate optimal exercises and rehabilitation plans based on the analyzed exercise data. For example, the generation unit can select the optimal exercises based on the analyzed exercise data. The generation unit can adjust the frequency and intensity of the exercises. The generation unit can customize the rehabilitation plan to meet the individual needs of the patient. For example, the generation unit can select the optimal exercises based on the analyzed exercise data. The generation unit can adjust the frequency and intensity of the exercises. The generation unit can customize the rehabilitation plan to meet the individual needs of the patient. As a result, the effectiveness of rehabilitation is improved by generating an optimal rehabilitation plan based on the analyzed exercise data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analyzed exercise data into a generation AI and have the generation AI generate the rehabilitation plan.

[0038] The support unit can assist in the implementation of the generated rehabilitation plan. For example, the support unit can provide feedback when the patient is performing exercises. The support unit can monitor the patient's movements in real time and provide guidance on modifying the exercises as needed. The support unit can record the patient's rehabilitation progress and report it to the physical therapist. For example, the support unit can provide feedback when the patient is performing exercises. The support unit can monitor the patient's movements in real time and provide guidance on modifying the exercises as needed. The support unit can record the patient's rehabilitation progress and report it to the physical therapist. This makes the rehabilitation process smoother by supporting the implementation of the generated rehabilitation plan. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the patient's movement data into a generating AI and have the generating AI provide feedback.

[0039] The monitoring unit can monitor and evaluate the execution of the rehabilitation plan in real time and provide feedback as needed. For example, the monitoring unit can monitor the patient's exercise data in real time and evaluate the execution of the rehabilitation plan. The monitoring unit can provide feedback as needed and provide guidance on modifying exercises. The monitoring unit can evaluate the effectiveness of the rehabilitation plan and reflect this in the generation of the next rehabilitation plan. For example, the monitoring unit can monitor the patient's exercise data in real time and evaluate the execution of the rehabilitation plan. The monitoring unit can provide feedback as needed and provide guidance on modifying exercises. The monitoring unit can evaluate the effectiveness of the rehabilitation plan and reflect this in the generation of the next rehabilitation plan. This maximizes the effectiveness of rehabilitation by monitoring and evaluating the execution of the rehabilitation plan in real time. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the patient's exercise data into a generating AI and have the generating AI perform the monitoring and evaluation of the rehabilitation plan.

[0040] The data collection unit can analyze the patient's past rehabilitation history and select the optimal data collection method. For example, the data collection unit can suggest the optimal data collection method based on the data collection methods the patient has used in the past. The data collection unit can select an effective data collection method from the patient's rehabilitation history. The data collection unit can analyze the patient's past rehabilitation history and customize the data collection method. For example, the data collection unit can suggest the optimal data collection method based on the data collection methods the patient has used in the past. The data collection unit can select an effective data collection method from the patient's rehabilitation history. The data collection unit can analyze the patient's past rehabilitation history and customize the data collection method. This allows the optimal data collection method to be selected by analyzing the patient's past rehabilitation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's past rehabilitation history into a generating AI and have the generating AI select the optimal data collection method.

[0041] The data collection unit can filter the collected personality information based on the patient's current living situation and areas of interest. For example, the data collection unit can prioritize collecting highly relevant personality information based on the patient's current living situation. The data collection unit can filter the personality information to be collected based on the patient's areas of interest. The data collection unit can select the personality information to be collected considering the patient's living situation and areas of interest. For example, the data collection unit can prioritize collecting highly relevant personality information based on the patient's current living situation. The data collection unit can filter the personality information to be collected based on the patient's areas of interest. The data collection unit can select the personality information to be collected considering the patient's living situation and areas of interest. By filtering personality information based on the patient's living situation and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the patient's living situation and areas of interest into a generating AI and have the generating AI perform the filtering of personality information.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the patient's geographical location when collecting individual information. For example, the data collection unit can prioritize the collection of highly relevant individual information based on the patient's current location. The data collection unit can select the individual information to collect by considering the patient's geographical location. The data collection unit can determine the priority of the individual information to collect based on the patient's geographical location. For example, the data collection unit can prioritize the collection of highly relevant individual information based on the patient's current location. The data collection unit can select the individual information to collect by considering the patient's geographical location. The data collection unit can determine the priority of the individual information to collect based on the patient's geographical location. This allows for the priority collection of highly relevant information by considering the patient's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's geographical location into a generating AI and have the generating AI perform the collection of individual information.

[0043] The data collection unit can analyze the patient's social media activity and collect relevant information when collecting personality information. For example, the data collection unit can analyze the patient's social media activity and collect relevant personality information. The data collection unit can select the personality information to collect from the patient's social media activity. The data collection unit can determine the priority of the personality information to collect based on the patient's social media activity. For example, the data collection unit can analyze the patient's social media activity and collect relevant personality information. The data collection unit can select the personality information to collect from the patient's social media activity. The data collection unit can determine the priority of the personality information to collect based on the patient's social media activity. This allows relevant information to be collected by analyzing the patient's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's social media activity data into a generating AI and have the generating AI collect relevant information.

[0044] The analysis unit can improve the accuracy of its analysis by referring to the patient's past exercise history when analyzing exercise data. For example, the analysis unit can improve the accuracy of its analysis based on the patient's past exercise history. The analysis unit can select an effective analysis method from the patient's exercise history. The analysis unit can improve the accuracy of its analysis by referring to the patient's past exercise history. For example, the analysis unit can improve the accuracy of its analysis based on the patient's past exercise history. The analysis unit can select an effective analysis method from the patient's exercise history. The analysis unit can improve the accuracy of its analysis by referring to the patient's past exercise history. As a result, the accuracy of the analysis is improved by referring to the patient's past exercise history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the patient's past exercise history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0045] The analysis unit can apply different analysis algorithms to the patient's specific rehabilitation goals when analyzing exercise data. For example, the analysis unit can select the optimal analysis algorithm according to the patient's rehabilitation goals. The analysis unit can adjust the analysis algorithm based on the patient's specific rehabilitation goals. The analysis unit can apply different analysis algorithms according to the patient's rehabilitation goals. For example, the analysis unit can select the optimal analysis algorithm according to the patient's rehabilitation goals. The analysis unit can adjust the analysis algorithm based on the patient's specific rehabilitation goals. The analysis unit can apply different analysis algorithms according to the patient's rehabilitation goals. This improves the accuracy of the analysis by applying the analysis algorithm according to the patient's rehabilitation goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's rehabilitation goal data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0046] The analysis unit can perform analysis of exercise data while considering the geographical distribution of patients. For example, the analysis unit can adjust the analysis method based on the geographical distribution of patients. The analysis unit can improve the accuracy of the analysis by considering the geographical distribution of patients. The analysis unit can display the analysis results based on the geographical distribution of patients. For example, the analysis unit can adjust the analysis method based on the geographical distribution of patients. The analysis unit can improve the accuracy of the analysis by considering the geographical distribution of patients. The analysis unit can display the analysis results based on the geographical distribution of patients. This improves the accuracy of the analysis by considering the geographical distribution of patients. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of patients into a generating AI and have the generating AI perform the analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature when analyzing motion data. For example, the analysis unit can select an analysis method by referring to relevant literature. The analysis unit can improve the accuracy of its analysis based on the relevant literature. The analysis unit can supplement the analysis results by referring to relevant literature. For example, the analysis unit can select an analysis method by referring to relevant literature. The analysis unit can improve the accuracy of its analysis based on the relevant literature. The analysis unit can supplement the analysis results by referring to relevant literature. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0048] The generation unit can generate an optimal rehabilitation plan by referring to the patient's past rehabilitation history when generating a rehabilitation plan. For example, the generation unit generates an optimal rehabilitation plan based on the patient's past rehabilitation history. The generation unit can select an effective rehabilitation plan from the patient's rehabilitation history. The generation unit can customize the rehabilitation plan by referring to the patient's past rehabilitation history. For example, the generation unit generates an optimal rehabilitation plan based on the patient's past rehabilitation history. The generation unit can select an effective rehabilitation plan from the patient's rehabilitation history. The generation unit can customize the rehabilitation plan by referring to the patient's past rehabilitation history. This allows the generation unit to generate an optimal rehabilitation plan by referring to the patient's past rehabilitation history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the patient's past rehabilitation history data into a generation AI and have the generation AI perform the generation of an optimal rehabilitation plan.

[0049] The generation unit can apply different generation algorithms depending on the patient's specific rehabilitation goals when generating a rehabilitation plan. For example, the generation unit can select the optimal generation algorithm depending on the patient's rehabilitation goals. The generation unit can adjust the generation algorithm based on the patient's specific rehabilitation goals. The generation unit can apply different generation algorithms depending on the patient's rehabilitation goals. For example, the generation unit can select the optimal generation algorithm depending on the patient's rehabilitation goals. The generation unit can adjust the generation algorithm based on the patient's specific rehabilitation goals. The generation unit can apply different generation algorithms depending on the patient's rehabilitation goals. This improves the accuracy of the rehabilitation plan by applying the generation algorithm according to the patient's rehabilitation goals. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the patient's rehabilitation goal data into a generation AI and have the generation AI perform the application of the generation algorithm.

[0050] The generation unit can generate an optimal rehabilitation plan by considering the patient's geographical location information. For example, the generation unit can generate an optimal rehabilitation plan based on the patient's current location. The generation unit can select a rehabilitation plan by considering the patient's geographical location information. The generation unit can determine the priority of rehabilitation plans based on the patient's geographical location information. For example, the generation unit can generate an optimal rehabilitation plan based on the patient's current location. The generation unit can select a rehabilitation plan by considering the patient's geographical location information. The generation unit can determine the priority of rehabilitation plans based on the patient's geographical location information. This makes it possible to generate an optimal rehabilitation plan by considering the patient's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the patient's geographical location information into a generation AI and have the generation AI perform the generation of the rehabilitation plan.

[0051] The generation unit can improve the accuracy of the rehabilitation plan generation by referring to relevant literature. For example, the generation unit generates a rehabilitation plan by referring to relevant literature. The generation unit can improve the accuracy of the rehabilitation plan based on the relevant literature. The generation unit can supplement the rehabilitation plan by referring to relevant literature. For example, the generation unit generates a rehabilitation plan by referring to relevant literature. The generation unit can improve the accuracy of the rehabilitation plan based on the relevant literature. The generation unit can supplement the rehabilitation plan by referring to relevant literature. As a result, the accuracy of the rehabilitation plan is improved by referring to relevant literature. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input relevant literature data into a generation AI and have the generation AI perform the generation of the rehabilitation plan.

[0052] The support unit can select the optimal support method by referring to the patient's past rehabilitation history when assisting in the implementation of a rehabilitation plan. For example, the support unit can propose the optimal support method based on the patient's past rehabilitation history. The support unit can select an effective support method from the patient's rehabilitation history. The support unit can customize the support method by referring to the patient's past rehabilitation history. For example, the support unit can propose the optimal support method based on the patient's past rehabilitation history. The support unit can select an effective support method from the patient's rehabilitation history. The support unit can customize the support method by referring to the patient's past rehabilitation history. This allows the optimal support method to be selected by referring to the patient's past rehabilitation history. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the patient's past rehabilitation history data into a generating AI and have the generating AI select the optimal support method.

[0053] The support unit can apply different support measures according to the patient's specific rehabilitation goals when assisting in the implementation of a rehabilitation plan. For example, the support unit can select the optimal support measure according to the patient's rehabilitation goals. The support unit can adjust the support measures based on the patient's specific rehabilitation goals. The support unit can apply different support measures according to the patient's rehabilitation goals. For example, the support unit can select the optimal support measure according to the patient's rehabilitation goals. The support unit can adjust the support measures based on the patient's specific rehabilitation goals. The support unit can apply different support measures according to the patient's rehabilitation goals. This improves the accuracy of support by applying support measures according to the patient's rehabilitation goals. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the patient's rehabilitation goal data into a generating AI and have the generating AI execute the application of support measures.

[0054] The support unit can select the optimal support method when assisting in the implementation of a rehabilitation plan, taking into account the patient's geographical location information. For example, the support unit can provide the optimal support method based on the patient's current location. The support unit can select a support method considering the patient's geographical location information. The support unit can determine the priority of support methods based on the patient's geographical location information. For example, the support unit can provide the optimal support method based on the patient's current location. The support unit can select a support method considering the patient's geographical location information. The support unit can determine the priority of support methods based on the patient's geographical location information. This allows the support unit to provide the optimal support method by taking into account the patient's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI. For example, the support unit can input the patient's geographical location information into a generating AI and have the generating AI select a support method.

[0055] The support department can analyze a patient's social media activity and propose support measures when assisting in the implementation of a rehabilitation plan. For example, the support department can analyze a patient's social media activity and propose relevant support measures. The support department can select support measures from the patient's social media activity. The support department can determine the priority of support measures based on the patient's social media activity. For example, the support department can analyze a patient's social media activity and propose relevant support measures. The support department can select support measures from the patient's social media activity. The support department can determine the priority of support measures based on the patient's social media activity. This allows the support department to propose relevant support measures by analyzing the patient's social media activity. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the patient's social media activity data into a generating AI and have the generating AI propose support measures.

[0056] The monitoring unit can select the optimal monitoring and evaluation method by referring to the patient's past rehabilitation history when monitoring and evaluating the rehabilitation plan. For example, the monitoring unit can propose the optimal monitoring and evaluation method based on the patient's past rehabilitation history. The monitoring unit can select an effective monitoring and evaluation method from the patient's rehabilitation history. The monitoring unit can customize the monitoring and evaluation method by referring to the patient's past rehabilitation history. For example, the monitoring unit can propose the optimal monitoring and evaluation method based on the patient's past rehabilitation history. The monitoring unit can select an effective monitoring and evaluation method from the patient's rehabilitation history. The monitoring unit can customize the monitoring and evaluation method by referring to the patient's past rehabilitation history. This allows the optimal monitoring and evaluation method to be selected by referring to the patient's past rehabilitation history. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the patient's past rehabilitation history data into a generating AI and have the generating AI select the optimal monitoring and evaluation method.

[0057] The monitoring unit can apply different monitoring and evaluation methods according to the patient's specific rehabilitation goals when monitoring and evaluating the rehabilitation plan. For example, the monitoring unit can select the optimal monitoring and evaluation method according to the patient's rehabilitation goals. The monitoring unit can adjust the monitoring and evaluation methods based on the patient's specific rehabilitation goals. The monitoring unit can apply different monitoring and evaluation methods according to the patient's rehabilitation goals. For example, the monitoring unit can select the optimal monitoring and evaluation method according to the patient's rehabilitation goals. The monitoring unit can adjust the monitoring and evaluation methods based on the patient's specific rehabilitation goals. The monitoring unit can apply different monitoring and evaluation methods according to the patient's rehabilitation goals. This improves the accuracy of monitoring and evaluation by applying monitoring and evaluation methods according to the patient's rehabilitation goals. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input patient rehabilitation goal data into a generating AI and have the generating AI execute the application of monitoring and evaluation methods.

[0058] The monitoring unit can select the optimal monitoring and evaluation method when monitoring and evaluating the rehabilitation plan, taking into account the patient's geographical location information. For example, the monitoring unit can provide the optimal monitoring and evaluation method based on the patient's current location. The monitoring unit can select a monitoring and evaluation method considering the patient's geographical location information. The monitoring unit can determine the priority of monitoring and evaluation methods based on the patient's geographical location information. For example, the monitoring unit can provide the optimal monitoring and evaluation method based on the patient's current location. The monitoring unit can select a monitoring and evaluation method considering the patient's geographical location information. The monitoring unit can determine the priority of monitoring and evaluation methods based on the patient's geographical location information. This allows the monitoring unit to provide the optimal monitoring and evaluation method by taking into account the patient's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the patient's geographical location information into a generating AI and have the generating AI perform the selection of monitoring and evaluation methods.

[0059] The monitoring unit can analyze a patient's social media activity and propose monitoring and evaluation methods when monitoring and evaluating a rehabilitation plan. For example, the monitoring unit can analyze a patient's social media activity and propose relevant monitoring and evaluation methods. The monitoring unit can select monitoring and evaluation methods from the patient's social media activity. The monitoring unit can determine the priority of monitoring and evaluation methods based on the patient's social media activity. For example, the monitoring unit can analyze a patient's social media activity and propose relevant monitoring and evaluation methods. The monitoring unit can select monitoring and evaluation methods from the patient's social media activity. The monitoring unit can determine the priority of monitoring and evaluation methods based on the patient's social media activity. This allows the monitoring unit to propose relevant monitoring and evaluation methods by analyzing the patient's social media activity. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input patient social media activity data into a generating AI and have the generating AI execute the proposal of monitoring and evaluation methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The rehabilitation coach AI agent system can also collect and analyze patient lifestyle data. For example, it can collect data on the patient's diet, sleep patterns, and daily activity levels, and adjust the rehabilitation plan based on this data. This allows for the provision of a rehabilitation plan that takes into account the patient's overall health condition. Furthermore, it can more accurately monitor the patient's rehabilitation progress based on lifestyle data and modify the plan as needed. In addition, by analyzing lifestyle data, it becomes possible to predict the patient's health risks and provide preventative advice.

[0062] The rehabilitation coach AI agent system can collect and analyze patients' social network data. For example, it can collect data on the frequency and content of communication between patients and their family and friends, and adjust rehabilitation plans based on this data. This allows for the provision of rehabilitation plans that take into account the patient's social support situation. Furthermore, it can estimate the patient's motivation and stress levels based on social network data and modify the plan as needed. In addition, by analyzing social network data, it becomes possible to predict the patient's risk of isolation and provide appropriate support.

[0063] The rehabilitation coach AI agent system can collect and analyze patient biometric data. For example, it can collect biometric data such as heart rate, blood pressure, and body temperature, and adjust rehabilitation plans based on this data. This allows for the provision of rehabilitation plans that take into account the patient's physiological state. Furthermore, based on biometric data, the system can more accurately monitor the patient's rehabilitation progress and modify the plan as needed. In addition, by analyzing biometric data, it becomes possible to predict the patient's health risks and provide preventative advice.

[0064] The rehabilitation coach AI agent system can collect and analyze patient environmental data. For example, it can collect data on the patient's living environment, work environment, and climate conditions, and adjust the rehabilitation plan based on this data. This allows for the provision of a rehabilitation plan that takes the patient's environmental conditions into consideration. Furthermore, based on the environmental data, the system can more accurately monitor the patient's rehabilitation progress and modify the plan as needed. In addition, by analyzing the environmental data, it becomes possible to predict the patient's health risks and provide preventative advice.

[0065] The rehabilitation coach AI agent system can collect and analyze patients' genetic information. For example, it can collect patients' genetic risk factors and genetic characteristics and adjust rehabilitation plans based on this data. This allows for the provision of rehabilitation plans that take into account the patient's genetic background. Furthermore, based on genetic information, the system can more accurately monitor the patient's rehabilitation progress and modify the plan as needed. In addition, by analyzing genetic information, it becomes possible to predict the patient's health risks and provide preventative advice.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects personal information. This personal information includes age, gender, occupation, medical history, emotional state, and specific rehabilitation goals. The data collection unit provides an interface for inputting the patient's age and gender, and has a database for recording occupation and medical history. It also has sensors to estimate emotional state, and can estimate emotions by analyzing the patient's facial expressions and voice. Step 2: The analysis unit analyzes the movement data based on the individual information collected by the data acquisition unit. The movement data includes data from sensors and robotic devices. The analysis unit analyzes the movement data obtained from sensors to understand the patient's movement patterns and analyzes the movement data obtained from robotic devices to evaluate the patient's motor ability. It also monitors the patient's rehabilitation progress based on the movement data. Step 3: The generation unit generates a rehabilitation plan based on the exercise data analyzed by the analysis unit. The rehabilitation plan includes the type, frequency, and intensity of exercises. The generation unit selects the optimal exercises based on the analyzed exercise data and adjusts the frequency and intensity of the exercises. It also customizes the rehabilitation plan to meet the individual needs of the patient. Step 4: The support department assists in implementing the rehabilitation plan generated by the generation department. The support department provides feedback to the patient as they perform exercises, monitors their movements in real time, and provides guidance on modifying the exercises as needed. They also record the patient's rehabilitation progress and report it to the physical therapist. Step 5: The monitoring department monitors and evaluates the implementation of the rehabilitation plan supported by the support department. The monitoring department monitors the patient's exercise data in real time and evaluates the progress of the rehabilitation plan. It provides feedback as needed and instructs on modifying the exercises. It also evaluates the effectiveness of the rehabilitation plan and incorporates the findings into the creation of the next rehabilitation plan.

[0068] (Example of form 2) The rehabilitation coach AI agent system according to an embodiment of the present invention is a system that automatically provides guidance, monitoring, and evaluation of optimal exercises and rehabilitation plans according to the individual patient's recovery status. This rehabilitation coach AI agent system collects the patient's individual information (age, gender, occupation, medical history, emotional state, specific rehabilitation goals, etc.), and the AI ​​agent stores this information. Next, the rehabilitation coach AI agent system collects exercise data from sensors and robotic devices in real time and monitors the patient's recovery status. As a result, the rehabilitation coach AI agent system grasps the patient's rehabilitation progress in detail and automatically generates optimal exercises and rehabilitation plans. Furthermore, the rehabilitation coach AI agent system supports the execution of the generated rehabilitation plan and monitors and evaluates the patient's exercises in real time. For example, when the patient performs an exercise, it analyzes the data obtained from sensors and robotic devices to check whether the exercise is being performed with the correct form. If necessary, the rehabilitation coach AI agent system provides feedback and guidance on correcting the exercise. This mechanism makes rehabilitation progress management effortless and provides high-quality rehabilitation tailored to individual needs. In addition, physical therapists can care for many patients at once, improving the quality of rehabilitation. This enables the rehabilitation coach AI agent system to generate and support the implementation of optimal rehabilitation plans tailored to each individual patient's recovery status.

[0069] The rehabilitation coach AI agent system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a support unit, and a monitoring unit. The collection unit collects personal information. Personal information includes, but is not limited to, age, gender, occupation, medical history, emotional state, and specific rehabilitation goals. The collection unit provides, for example, an interface for inputting the patient's age and gender. The collection unit may also include a database for recording the patient's occupation and medical history. Furthermore, the collection unit may include sensors for estimating the patient's emotional state. For example, the collection unit provides a form for inputting the patient's age and gender, allowing the patient to input their own information. The collection unit includes a database for recording the patient's occupation and medical history, and can store the patient's information. The collection unit includes sensors for estimating the patient's emotional state and can estimate emotions by analyzing the patient's facial expressions and voice. The analysis unit analyzes movement data based on the personal information collected by the collection unit. Movement data includes, but is not limited to, data from sensors and data from robotic devices. The analysis unit analyzes motion data obtained from sensors to understand the patient's movement patterns. The analysis unit can also analyze motion data obtained from robotic devices to evaluate the patient's motor abilities. Furthermore, the analysis unit can monitor the patient's rehabilitation progress based on the motion data. For example, the analysis unit analyzes motion data obtained from sensors to understand the patient's movement patterns. The analysis unit can analyze motion data obtained from robotic devices to evaluate the patient's motor abilities. The analysis unit can monitor the patient's rehabilitation progress based on the motion data. The generation unit generates a rehabilitation plan based on the motion data analyzed by the analysis unit. The rehabilitation plan may include, but is not limited to, the type, frequency, and intensity of exercises. The generation unit may, for example, select the optimal exercises based on the analyzed motion data. The generation unit can also adjust the frequency and intensity of the exercises. Furthermore, the generation unit can customize the rehabilitation plan to meet the individual needs of the patient.For example, the generation unit selects the optimal exercises based on the analyzed exercise data. The generation unit can adjust the frequency and intensity of the exercises. The generation unit can customize the rehabilitation plan to meet the individual needs of the patient. The support unit assists in the execution of the rehabilitation plan generated by the generation unit. For example, the support unit provides feedback when the patient performs the exercises. The support unit can also monitor the patient's movements in real time and provide guidance on modifying the exercises as needed. Furthermore, the support unit can record the patient's rehabilitation progress and report it to the physical therapist. For example, the support unit provides feedback when the patient performs the exercises. The support unit can monitor the patient's movements in real time and provide guidance on modifying the exercises as needed. The support unit can record the patient's rehabilitation progress and report it to the physical therapist. The monitoring unit monitors and evaluates the execution of the rehabilitation plan supported by the support unit. For example, the monitoring unit monitors the patient's exercise data in real time and evaluates the execution of the rehabilitation plan. The monitoring unit can also provide feedback as needed and provide guidance on modifying the exercises. Furthermore, the monitoring unit can evaluate the effectiveness of the rehabilitation plan and reflect this in the generation of the next rehabilitation plan. For example, the monitoring unit can monitor the patient's exercise data in real time and evaluate the progress of the rehabilitation plan. The monitoring unit can provide feedback as needed and guide the patient in modifying their exercises. The monitoring unit can evaluate the effectiveness of the rehabilitation plan and reflect this in the generation of the next rehabilitation plan. As a result, the rehabilitation coach AI agent system according to this embodiment can generate and support the execution of an optimal rehabilitation plan tailored to the individual patient's recovery status.

[0070] The data collection unit collects personal information. This personal information includes, but is not limited to, age, gender, occupation, medical history, emotional state, and specific rehabilitation goals. For example, the data collection unit provides an interface for inputting the patient's age and gender. It may also include a database for recording the patient's occupation and medical history. Furthermore, the data collection unit may include sensors for estimating the patient's emotional state. For example, the data collection unit provides a form for inputting the patient's age and gender, allowing the patient to enter their information. The data collection unit includes a database for recording the patient's occupation and medical history, allowing it to store patient information. The data collection unit includes sensors for estimating the patient's emotional state, analyzing the patient's facial expressions and voice to estimate their emotions. The data collection unit provides an interface for inputting the patient's age and gender. Specifically, it allows patients to easily input their information using devices such as tablets or smartphones. This enables the data collection unit to quickly and accurately collect basic personal information about the patient. The data collection unit may also include a database for recording the patient's occupation and medical history. For example, detailed records of a patient's medical history and occupation can be used to customize rehabilitation plans. Furthermore, the data collection unit can be equipped with sensors to estimate the patient's emotional state. For instance, facial recognition technology can be used to analyze the patient's facial expressions, and speech recognition technology can be used to analyze the patient's tone of voice and speaking style to estimate their emotional state. This allows the data collection unit to grasp the patient's emotional state in real time and use this information to adjust rehabilitation plans. The data collection unit can centrally manage this information and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0071] The analysis unit analyzes movement data based on individual information collected by the collection unit. Movement data includes, but is not limited to, data from sensors and robotic devices. For example, the analysis unit analyzes movement data obtained from sensors to understand the patient's movement patterns. It can also analyze movement data obtained from robotic devices to evaluate the patient's motor abilities. Furthermore, the analysis unit can monitor the patient's rehabilitation progress based on the movement data. For example, the analysis unit analyzes movement data obtained from sensors to understand the patient's movement patterns. The analysis unit can analyze movement data obtained from robotic devices to evaluate the patient's motor abilities. The analysis unit can monitor the patient's rehabilitation progress based on the movement data. The analysis unit analyzes movement data based on individual information collected by the collection unit. Specifically, it analyzes movement data obtained from sensors and robotic devices in detail, taking into account individual information such as the patient's age, gender, occupation, medical history, and emotional state. For example, it analyzes movement data obtained from sensors to understand the patient's movement patterns. This includes using acceleration and gyroscope sensors to measure the speed and direction of the patient's movements and evaluate the quality and pattern of those movements. The analysis unit can also analyze motion data obtained from robotic devices to evaluate the patient's motor abilities. For example, it can use robotic assist devices to evaluate how much force the patient can exert and what kind of movement support is needed. Furthermore, the analysis unit can monitor the patient's rehabilitation progress based on the motion data. For example, it can analyze regularly collected motion data to evaluate the improvement in the patient's motor abilities and the progress of their rehabilitation. This allows the analysis unit to grasp the patient's rehabilitation progress in real time and adjust the rehabilitation plan as needed. In addition, the analysis unit can utilize past data and statistical information to evaluate the long-term effectiveness of rehabilitation and perform trend analysis. For example, it can evaluate how effective a particular rehabilitation plan was based on past motion data and use this information to improve future rehabilitation plans.This allows the analysis unit to not only grasp the situation in real time, but also to evaluate the long-term effects of rehabilitation and analyze trends, thereby improving the reliability and effectiveness of the entire system.

[0072] The generation unit generates a rehabilitation plan based on the exercise data analyzed by the analysis unit. The rehabilitation plan includes, but is not limited to, the type, frequency, and intensity of exercises. For example, the generation unit selects the optimal exercises based on the analyzed exercise data. The generation unit can also adjust the frequency and intensity of the exercises. Furthermore, the generation unit can customize the rehabilitation plan to meet the individual needs of the patient. For example, the generation unit selects the optimal exercises based on the analyzed exercise data. The generation unit can adjust the frequency and intensity of the exercises. The generation unit can customize the rehabilitation plan to meet the individual needs of the patient. The generation unit generates a rehabilitation plan based on the exercise data analyzed by the analysis unit. Specifically, it selects the optimal type, frequency, and intensity of exercises while considering the patient's exercise ability and rehabilitation progress. For example, based on the analyzed exercise data, it selects exercises suitable for the patient's current exercise ability and incorporates them into the rehabilitation plan. The generation unit can also adjust the frequency and intensity of the exercises. For example, by increasing or decreasing the frequency of exercises or adjusting the intensity according to the patient's rehabilitation progress, it provides an optimal rehabilitation plan. Furthermore, the generation unit can customize rehabilitation plans to meet the individual needs of patients. For example, it can select exercises tailored to the patient's occupation and daily living activities and incorporate them into the rehabilitation plan, resulting in more effective rehabilitation. The generation unit uses AI to analyze this data and simulate multiple scenarios to identify the most effective rehabilitation plan. This allows the generation unit to provide highly accurate rehabilitation plans that meet the individual needs of patients. In addition, the generation unit can continuously revise the rehabilitation plan based on real-time updated data to adapt to the latest situations. For example, if the patient's motor skills or rehabilitation progress changes, the generation unit immediately incorporates the new data and updates the rehabilitation plan. The generation unit can also provide more accurate rehabilitation plans by considering regional characteristics and past rehabilitation history.This allows the generation unit to always provide highly accurate rehabilitation plans based on the latest information, supporting rapid and appropriate rehabilitation.

[0073] The support unit assists in the implementation of the rehabilitation plan generated by the generation unit. For example, the support unit provides feedback when the patient performs exercises. The support unit can also monitor the patient's movements in real time and provide guidance to modify the exercises as needed. Furthermore, the support unit can record the patient's rehabilitation progress and report it to the physical therapist. For example, the support unit provides feedback when the patient performs exercises. The support unit can monitor the patient's movements in real time and provide guidance to modify the exercises as needed. The support unit can record the patient's rehabilitation progress and report it to the physical therapist. The support unit assists in the implementation of the rehabilitation plan generated by the generation unit. Specifically, it provides feedback when the patient performs exercises. For example, it monitors in real time whether the patient is performing the exercises correctly and provides guidance to correct them as needed. This includes using sensors and cameras to analyze the patient's movements in detail and provide feedback to maintain correct form and movement. The support unit can also monitor the patient's movements in real time and provide guidance to modify the exercises as needed. For example, if the patient makes an incorrect movement during exercise, the support unit can immediately provide guidance to correct it and encourage the correct movement. Furthermore, the support department can record the patient's rehabilitation progress and report it to the physical therapist. For example, by recording the patient's exercise performance and progress in detail and reporting it regularly to the physical therapist, the effectiveness of the rehabilitation plan can be evaluated and the plan revised as needed. The support department can centrally manage this information and collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the analysis and generation departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions can be made. As a result, the support department can collect data efficiently and effectively and improve the overall performance of the system.

[0074] The monitoring unit monitors and evaluates the execution of rehabilitation plans supported by the support unit. For example, the monitoring unit monitors the patient's exercise data in real time and evaluates the progress of the rehabilitation plan. The monitoring unit can also provide feedback as needed and guide the patient in modifying their exercises. Furthermore, the monitoring unit can evaluate the effectiveness of the rehabilitation plan and incorporate this into the generation of the next rehabilitation plan. For example, the monitoring unit monitors the patient's exercise data in real time and evaluates the progress of the rehabilitation plan. The monitoring unit can provide feedback as needed and guide the patient in modifying their exercises. The monitoring unit can evaluate the effectiveness of the rehabilitation plan and incorporate this into the generation of the next rehabilitation plan. The monitoring unit monitors and evaluates the execution of rehabilitation plans supported by the support unit. Specifically, it monitors the patient's exercise data in real time and evaluates the progress of the rehabilitation plan. For example, it uses sensors and cameras to analyze the patient's movements in detail and confirm that the rehabilitation plan is being executed appropriately. The monitoring unit can also provide feedback as needed and guide the patient in modifying their exercises. For example, if a patient makes an incorrect movement during exercise, the monitoring unit immediately provides corrective guidance to encourage the correct movement. Furthermore, the monitoring unit can evaluate the effectiveness of rehabilitation plans and incorporate this into the generation of subsequent rehabilitation plans. For example, it can evaluate how effective a rehabilitation plan was based on the patient's exercise data and use this information to improve future rehabilitation plans. The monitoring unit centrally manages this information and can collaborate with other systems and departments as needed. For instance, collected data can be stored on a cloud server and made accessible to the analysis and generation units. Adjusting the frequency and accuracy of data collection allows for flexible responses to specific situations and conditions. This enables the monitoring unit to collect data efficiently and effectively, improving the overall system performance. Additionally, the monitoring unit can utilize historical data and statistical information to evaluate long-term rehabilitation effectiveness and conduct trend analysis. For example, it can evaluate how effective a particular rehabilitation plan was based on past exercise data and use this information to improve future rehabilitation plans.This allows the monitoring unit to not only grasp the situation in real time, but also to evaluate the long-term effects of rehabilitation and analyze trends, thereby improving the reliability and effectiveness of the entire system.

[0075] The data collection unit can collect individual information such as the patient's age, gender, occupation, medical history, emotional state, and specific rehabilitation goals. For example, the data collection unit may provide an interface for inputting the patient's age and gender. The data collection unit may include a database for recording the patient's occupation and medical history. The data collection unit may include sensors for estimating the patient's emotional state. For example, the data collection unit may provide a form for inputting the patient's age and gender, allowing the patient to input their own information. The data collection unit may include a database for recording the patient's occupation and medical history, allowing it to store patient information. The data collection unit may include sensors for estimating the patient's emotional state, analyzing the patient's facial expressions and voice to estimate their emotions. This allows for detailed collection of the patient's individual information, improving the accuracy of individual rehabilitation plans. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit may input a form for inputting the patient's age and gender into a generating AI, allowing the generating AI to collect the patient's information.

[0076] The analysis unit can analyze motion data obtained from sensors and robotic devices. For example, the analysis unit can analyze motion data obtained from sensors to understand the patient's movement patterns. The analysis unit can analyze motion data obtained from robotic devices to evaluate the patient's motor ability. The analysis unit can monitor the patient's rehabilitation progress based on the motion data. For example, the analysis unit can analyze motion data obtained from sensors to understand the patient's movement patterns. The analysis unit can analyze motion data obtained from robotic devices to evaluate the patient's motor ability. The analysis unit can monitor the patient's rehabilitation progress based on the motion data. As a result, the accuracy of the rehabilitation plan is improved by analyzing motion data obtained from sensors and robotic devices. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input motion data obtained from sensors into a generating AI and have the generating AI perform the analysis of the motion data.

[0077] The generation unit can generate optimal exercises and rehabilitation plans based on the analyzed exercise data. For example, the generation unit can select the optimal exercises based on the analyzed exercise data. The generation unit can adjust the frequency and intensity of the exercises. The generation unit can customize the rehabilitation plan to meet the individual needs of the patient. For example, the generation unit can select the optimal exercises based on the analyzed exercise data. The generation unit can adjust the frequency and intensity of the exercises. The generation unit can customize the rehabilitation plan to meet the individual needs of the patient. As a result, the effectiveness of rehabilitation is improved by generating an optimal rehabilitation plan based on the analyzed exercise data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the analyzed exercise data into a generation AI and have the generation AI generate the rehabilitation plan.

[0078] The support unit can assist in the implementation of the generated rehabilitation plan. For example, the support unit can provide feedback when the patient is performing exercises. The support unit can monitor the patient's movements in real time and provide guidance on modifying the exercises as needed. The support unit can record the patient's rehabilitation progress and report it to the physical therapist. For example, the support unit can provide feedback when the patient is performing exercises. The support unit can monitor the patient's movements in real time and provide guidance on modifying the exercises as needed. The support unit can record the patient's rehabilitation progress and report it to the physical therapist. This makes the rehabilitation process smoother by supporting the implementation of the generated rehabilitation plan. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input the patient's movement data into a generating AI and have the generating AI provide feedback.

[0079] The monitoring unit can monitor and evaluate the execution of the rehabilitation plan in real time and provide feedback as needed. For example, the monitoring unit can monitor the patient's exercise data in real time and evaluate the execution of the rehabilitation plan. The monitoring unit can provide feedback as needed and provide guidance on modifying exercises. The monitoring unit can evaluate the effectiveness of the rehabilitation plan and reflect this in the generation of the next rehabilitation plan. For example, the monitoring unit can monitor the patient's exercise data in real time and evaluate the execution of the rehabilitation plan. The monitoring unit can provide feedback as needed and provide guidance on modifying exercises. The monitoring unit can evaluate the effectiveness of the rehabilitation plan and reflect this in the generation of the next rehabilitation plan. This maximizes the effectiveness of rehabilitation by monitoring and evaluating the execution of the rehabilitation plan in real time. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the patient's exercise data into a generating AI and have the generating AI perform the monitoring and evaluation of the rehabilitation plan.

[0080] The data collection unit can estimate the patient's emotions and adjust the timing of collecting personality information based on the estimated emotions. For example, if the patient is stressed, the data collection unit can delay collecting personality information until the patient is relaxed. If the patient is relaxed, the data collection unit can adjust the timing of collection to collect detailed personality information. If the patient is in a hurry, the data collection unit can quickly collect only the minimum necessary personality information. For example, if the patient is stressed, the data collection unit can delay collecting personality information until the patient is relaxed. If the patient is relaxed, the data collection unit can adjust the timing of collection to collect detailed personality information. If the patient is in a hurry, the data collection unit can quickly collect only the minimum necessary personality information. This allows for more appropriate information collection by adjusting the timing of personality information collection according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input patient emotion data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0081] The data collection unit can analyze the patient's past rehabilitation history and select the optimal data collection method. For example, the data collection unit can suggest the optimal data collection method based on the data collection methods the patient has used in the past. The data collection unit can select an effective data collection method from the patient's rehabilitation history. The data collection unit can analyze the patient's past rehabilitation history and customize the data collection method. For example, the data collection unit can suggest the optimal data collection method based on the data collection methods the patient has used in the past. The data collection unit can select an effective data collection method from the patient's rehabilitation history. The data collection unit can analyze the patient's past rehabilitation history and customize the data collection method. This allows the optimal data collection method to be selected by analyzing the patient's past rehabilitation history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's past rehabilitation history into a generating AI and have the generating AI select the optimal data collection method.

[0082] The data collection unit can filter the collected personality information based on the patient's current living situation and areas of interest. For example, the data collection unit can prioritize collecting highly relevant personality information based on the patient's current living situation. The data collection unit can filter the personality information to be collected based on the patient's areas of interest. The data collection unit can select the personality information to be collected considering the patient's living situation and areas of interest. For example, the data collection unit can prioritize collecting highly relevant personality information based on the patient's current living situation. The data collection unit can filter the personality information to be collected based on the patient's areas of interest. The data collection unit can select the personality information to be collected considering the patient's living situation and areas of interest. By filtering personality information based on the patient's living situation and areas of interest, more relevant information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the patient's living situation and areas of interest into a generating AI and have the generating AI perform the filtering of personality information.

[0083] The data collection unit can estimate the patient's emotions and determine the priority of personality information to collect based on the estimated emotions. For example, if the patient is stressed, the data collection unit will prioritize collecting important personality information. If the patient is relaxed, the data collection unit will prioritize collecting detailed personality information. If the patient is in a hurry, the data collection unit will prioritize collecting only the essential personality information. For example, if the patient is stressed, the data collection unit will prioritize collecting important personality information. If the patient is relaxed, the data collection unit will prioritize collecting detailed personality information. If the patient is in a hurry, the data collection unit will prioritize collecting only the essential personality information. This allows for the priority collection of important information by determining the priority of personality information to collect according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input patient emotion data into a generating AI, allowing the AI ​​to perform emotion estimation.

[0084] The data collection unit can prioritize the collection of highly relevant information by considering the patient's geographical location when collecting individual information. For example, the data collection unit can prioritize the collection of highly relevant individual information based on the patient's current location. The data collection unit can select the individual information to collect by considering the patient's geographical location. The data collection unit can determine the priority of the individual information to collect based on the patient's geographical location. For example, the data collection unit can prioritize the collection of highly relevant individual information based on the patient's current location. The data collection unit can select the individual information to collect by considering the patient's geographical location. The data collection unit can determine the priority of the individual information to collect based on the patient's geographical location. This allows for the priority collection of highly relevant information by considering the patient's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's geographical location into a generating AI and have the generating AI perform the collection of individual information.

[0085] The data collection unit can analyze the patient's social media activity and collect relevant information when collecting personality information. For example, the data collection unit can analyze the patient's social media activity and collect relevant personality information. The data collection unit can select the personality information to collect from the patient's social media activity. The data collection unit can determine the priority of the personality information to collect based on the patient's social media activity. For example, the data collection unit can analyze the patient's social media activity and collect relevant personality information. The data collection unit can select the personality information to collect from the patient's social media activity. The data collection unit can determine the priority of the personality information to collect based on the patient's social media activity. This allows relevant information to be collected by analyzing the patient's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the patient's social media activity data into a generating AI and have the generating AI collect relevant information.

[0086] The analysis unit can estimate the patient's emotions and adjust the method of analyzing the motor data based on the estimated emotions. For example, if the patient is relaxed, the analysis unit can perform a detailed analysis. If the patient is stressed, the analysis unit can perform a simplified analysis. If the patient is in a hurry, the analysis unit can perform a rapid analysis. For example, if the patient is relaxed, the analysis unit can perform a detailed analysis. If the patient is stressed, the analysis unit can perform a simplified analysis. If the patient is in a hurry, the analysis unit can perform a rapid analysis. This allows for more appropriate analysis by adjusting the method of analyzing the motor data according to 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can improve the accuracy of its analysis by referring to the patient's past exercise history when analyzing exercise data. For example, the analysis unit can improve the accuracy of its analysis based on the patient's past exercise history. The analysis unit can select an effective analysis method from the patient's exercise history. The analysis unit can improve the accuracy of its analysis by referring to the patient's past exercise history. For example, the analysis unit can improve the accuracy of its analysis based on the patient's past exercise history. The analysis unit can select an effective analysis method from the patient's exercise history. The analysis unit can improve the accuracy of its analysis by referring to the patient's past exercise history. As a result, the accuracy of the analysis is improved by referring to the patient's past exercise history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the patient's past exercise history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0088] The analysis unit can apply different analysis algorithms to the patient's specific rehabilitation goals when analyzing exercise data. For example, the analysis unit can select the optimal analysis algorithm according to the patient's rehabilitation goals. The analysis unit can adjust the analysis algorithm based on the patient's specific rehabilitation goals. The analysis unit can apply different analysis algorithms according to the patient's rehabilitation goals. For example, the analysis unit can select the optimal analysis algorithm according to the patient's rehabilitation goals. The analysis unit can adjust the analysis algorithm based on the patient's specific rehabilitation goals. The analysis unit can apply different analysis algorithms according to the patient's rehabilitation goals. This improves the accuracy of the analysis by applying the analysis algorithm according to the patient's rehabilitation goals. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the patient's rehabilitation goal data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0089] The analysis unit can estimate the patient's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the patient is tense, the analysis unit can provide a simple and easy-to-read display method. If the patient is relaxed, the analysis unit can provide a display method that includes detailed information. If the patient is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, if the patient is tense, the analysis unit can provide a simple and easy-to-read display method. If the patient is relaxed, the analysis unit can provide a display method that includes detailed information. If the patient is in a hurry, the analysis unit can provide a display method that gets straight to the point. This makes it possible to provide more appropriate information by adjusting the display method of the analysis results according to 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 is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input patient emotion data into a generating AI and have the generating AI perform emotion estimation.

[0090] The analysis unit can perform analysis of exercise data while considering the geographical distribution of patients. For example, the analysis unit can adjust the analysis method based on the geographical distribution of patients. The analysis unit can improve the accuracy of the analysis by considering the geographical distribution of patients. The analysis unit can display the analysis results based on the geographical distribution of patients. For example, the analysis unit can adjust the analysis method based on the geographical distribution of patients. The analysis unit can improve the accuracy of the analysis by considering the geographical distribution of patients. The analysis unit can display the analysis results based on the geographical distribution of patients. This improves the accuracy of the analysis by considering the geographical distribution of patients. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of patients into a generating AI and have the generating AI perform the analysis.

[0091] The analysis unit can improve the accuracy of its analysis by referring to relevant literature when analyzing motion data. For example, the analysis unit can select an analysis method by referring to relevant literature. The analysis unit can improve the accuracy of its analysis based on the relevant literature. The analysis unit can supplement the analysis results by referring to relevant literature. For example, the analysis unit can select an analysis method by referring to relevant literature. The analysis unit can improve the accuracy of its analysis based on the relevant literature. The analysis unit can supplement the analysis results by referring to relevant literature. As a result, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input relevant literature data into a generating AI and have the generating AI perform the improvement of the analysis accuracy.

[0092] The generation unit can estimate the patient's emotions and adjust the method of generating the rehabilitation plan based on the estimated emotions. For example, if the patient is relaxed, the generation unit can generate a detailed rehabilitation plan. If the patient is stressed, the generation unit can generate a simplified rehabilitation plan. If the patient is in a hurry, the generation unit can generate a rehabilitation plan that can be quickly implemented. For example, if the patient is relaxed, the generation unit can generate a detailed rehabilitation plan. If the patient is stressed, the generation unit can generate a simplified rehabilitation plan. If the patient is in a hurry, the generation unit can generate a rehabilitation plan that can be quickly implemented. This allows for the provision of a more appropriate rehabilitation plan by adjusting the method of generating the rehabilitation plan according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input patient emotion data into the generation AI and have the generation AI perform emotion estimation.

[0093] The generation unit can generate an optimal rehabilitation plan by referring to the patient's past rehabilitation history when generating a rehabilitation plan. For example, the generation unit generates an optimal rehabilitation plan based on the patient's past rehabilitation history. The generation unit can select an effective rehabilitation plan from the patient's rehabilitation history. The generation unit can customize the rehabilitation plan by referring to the patient's past rehabilitation history. For example, the generation unit generates an optimal rehabilitation plan based on the patient's past rehabilitation history. The generation unit can select an effective rehabilitation plan from the patient's rehabilitation history. The generation unit can customize the rehabilitation plan by referring to the patient's past rehabilitation history. This allows the generation unit to generate an optimal rehabilitation plan by referring to the patient's past rehabilitation history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the patient's past rehabilitation history data into a generation AI and have the generation AI perform the generation of an optimal rehabilitation plan.

[0094] The generation unit can apply different generation algorithms depending on the patient's specific rehabilitation goals when generating a rehabilitation plan. For example, the generation unit can select the optimal generation algorithm depending on the patient's rehabilitation goals. The generation unit can adjust the generation algorithm based on the patient's specific rehabilitation goals. The generation unit can apply different generation algorithms depending on the patient's rehabilitation goals. For example, the generation unit can select the optimal generation algorithm depending on the patient's rehabilitation goals. The generation unit can adjust the generation algorithm based on the patient's specific rehabilitation goals. The generation unit can apply different generation algorithms depending on the patient's rehabilitation goals. This improves the accuracy of the rehabilitation plan by applying the generation algorithm according to the patient's rehabilitation goals. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the patient's rehabilitation goal data into a generation AI and have the generation AI perform the application of the generation algorithm.

[0095] The generation unit can estimate the patient's emotions and determine the priority of rehabilitation plans based on the estimated emotions. For example, if the patient is stressed, the generation unit will prioritize generating important rehabilitation plans. If the patient is relaxed, the generation unit will prioritize generating detailed rehabilitation plans. If the patient is in a hurry, the generation unit will prioritize generating minimal rehabilitation plans. For example, if the patient is stressed, the generation unit will prioritize generating important rehabilitation plans. If the patient is relaxed, the generation unit will prioritize generating detailed rehabilitation plans. If the patient is in a hurry, the generation unit will prioritize generating minimal rehabilitation plans. This allows for the priority of important rehabilitation plans to be provided by determining the priority of rehabilitation plans according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input patient emotion data into the generation AI and have the generation AI perform emotion estimation.

[0096] The generation unit can generate an optimal rehabilitation plan by considering the patient's geographical location information. For example, the generation unit can generate an optimal rehabilitation plan based on the patient's current location. The generation unit can select a rehabilitation plan by considering the patient's geographical location information. The generation unit can determine the priority of rehabilitation plans based on the patient's geographical location information. For example, the generation unit can generate an optimal rehabilitation plan based on the patient's current location. The generation unit can select a rehabilitation plan by considering the patient's geographical location information. The generation unit can determine the priority of rehabilitation plans based on the patient's geographical location information. This makes it possible to generate an optimal rehabilitation plan by considering the patient's geographical location information. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input the patient's geographical location information into a generation AI and have the generation AI perform the generation of the rehabilitation plan.

[0097] The generation unit can improve the accuracy of the rehabilitation plan generation by referring to relevant literature. For example, the generation unit generates a rehabilitation plan by referring to relevant literature. The generation unit can improve the accuracy of the rehabilitation plan based on the relevant literature. The generation unit can supplement the rehabilitation plan by referring to relevant literature. For example, the generation unit generates a rehabilitation plan by referring to relevant literature. The generation unit can improve the accuracy of the rehabilitation plan based on the relevant literature. The generation unit can supplement the rehabilitation plan by referring to relevant literature. As a result, the accuracy of the rehabilitation plan is improved by referring to relevant literature. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI. For example, the generation unit can input relevant literature data into a generation AI and have the generation AI perform the generation of the rehabilitation plan.

[0098] The support unit can estimate the patient's emotions and adjust the method of supporting the implementation of the rehabilitation plan based on the estimated emotions. For example, if the patient is relaxed, the support unit can provide detailed support methods. If the patient is stressed, the support unit can provide simplified support methods. If the patient is in a hurry, the support unit can provide quickly implementable support methods. For example, if the patient is relaxed, the support unit can provide detailed support methods. If the patient is stressed, the support unit can provide simplified support methods. If the patient is in a hurry, the support unit can provide quickly implementable support methods. This allows for more appropriate support by adjusting the method of supporting the implementation of the rehabilitation plan according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support department can input patient emotional data into a generative AI and have the AI ​​perform emotion estimation.

[0099] The support unit can select the optimal support method by referring to the patient's past rehabilitation history when assisting in the implementation of a rehabilitation plan. For example, the support unit can propose the optimal support method based on the patient's past rehabilitation history. The support unit can select an effective support method from the patient's rehabilitation history. The support unit can customize the support method by referring to the patient's past rehabilitation history. For example, the support unit can propose the optimal support method based on the patient's past rehabilitation history. The support unit can select an effective support method from the patient's rehabilitation history. The support unit can customize the support method by referring to the patient's past rehabilitation history. This allows the optimal support method to be selected by referring to the patient's past rehabilitation history. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the patient's past rehabilitation history data into a generating AI and have the generating AI select the optimal support method.

[0100] The support unit can apply different support measures according to the patient's specific rehabilitation goals when assisting in the implementation of a rehabilitation plan. For example, the support unit can select the optimal support measure according to the patient's rehabilitation goals. The support unit can adjust the support measures based on the patient's specific rehabilitation goals. The support unit can apply different support measures according to the patient's rehabilitation goals. For example, the support unit can select the optimal support measure according to the patient's rehabilitation goals. The support unit can adjust the support measures based on the patient's specific rehabilitation goals. The support unit can apply different support measures according to the patient's rehabilitation goals. This improves the accuracy of support by applying support measures according to the patient's rehabilitation goals. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the patient's rehabilitation goal data into a generating AI and have the generating AI execute the application of support measures.

[0101] The support unit can estimate the patient's emotions and determine the priority of support for implementing the rehabilitation plan based on the estimated emotions. For example, if the patient is stressed, the support unit can prioritize providing important support. If the patient is relaxed, the support unit can prioritize providing detailed support. If the patient is in a hurry, the support unit can prioritize providing the minimum necessary support. For example, if the patient is stressed, the support unit can prioritize providing important support. If the patient is relaxed, the support unit can prioritize providing detailed support. If the patient is in a hurry, the support unit can prioritize providing the minimum necessary support. This allows for the prioritization of important support by determining the priority of support for implementing the rehabilitation plan according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support department can input patient emotional data into a generative AI and have the AI ​​perform emotion estimation.

[0102] The support unit can select the optimal support method when assisting in the implementation of a rehabilitation plan, taking into account the patient's geographical location information. For example, the support unit can provide the optimal support method based on the patient's current location. The support unit can select a support method considering the patient's geographical location information. The support unit can determine the priority of support methods based on the patient's geographical location information. For example, the support unit can provide the optimal support method based on the patient's current location. The support unit can select a support method considering the patient's geographical location information. The support unit can determine the priority of support methods based on the patient's geographical location information. This allows the support unit to provide the optimal support method by taking into account the patient's geographical location information. Some or all of the above processing in the support unit may be performed using AI, for example, or without using AI. For example, the support unit can input the patient's geographical location information into a generating AI and have the generating AI select a support method.

[0103] The support department can analyze a patient's social media activity and propose support measures when assisting in the implementation of a rehabilitation plan. For example, the support department can analyze a patient's social media activity and propose relevant support measures. The support department can select support measures from the patient's social media activity. The support department can determine the priority of support measures based on the patient's social media activity. For example, the support department can analyze a patient's social media activity and propose relevant support measures. The support department can select support measures from the patient's social media activity. The support department can determine the priority of support measures based on the patient's social media activity. This allows the support department to propose relevant support measures by analyzing the patient's social media activity. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the patient's social media activity data into a generating AI and have the generating AI propose support measures.

[0104] The monitoring unit can estimate the patient's emotions and adjust the monitoring and evaluation methods of the rehabilitation plan based on the estimated emotions. For example, if the patient is relaxed, the monitoring unit can perform detailed monitoring and evaluation. If the patient is stressed, the monitoring unit can perform simplified monitoring and evaluation. If the patient is in a hurry, the monitoring unit can perform rapid monitoring and evaluation. For example, if the patient is relaxed, the monitoring unit can perform detailed monitoring and evaluation. If the patient is stressed, the monitoring unit can perform simplified monitoring and evaluation. If the patient is in a hurry, the monitoring unit can perform rapid monitoring and evaluation. This allows for more appropriate monitoring and evaluation by adjusting the monitoring and evaluation methods of the rehabilitation plan according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input patient emotion data into a generating AI and have the AI ​​perform emotion estimation.

[0105] The monitoring unit can select the optimal monitoring and evaluation method by referring to the patient's past rehabilitation history when monitoring and evaluating the rehabilitation plan. For example, the monitoring unit can propose the optimal monitoring and evaluation method based on the patient's past rehabilitation history. The monitoring unit can select an effective monitoring and evaluation method from the patient's rehabilitation history. The monitoring unit can customize the monitoring and evaluation method by referring to the patient's past rehabilitation history. For example, the monitoring unit can propose the optimal monitoring and evaluation method based on the patient's past rehabilitation history. The monitoring unit can select an effective monitoring and evaluation method from the patient's rehabilitation history. The monitoring unit can customize the monitoring and evaluation method by referring to the patient's past rehabilitation history. This allows the optimal monitoring and evaluation method to be selected by referring to the patient's past rehabilitation history. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the patient's past rehabilitation history data into a generating AI and have the generating AI select the optimal monitoring and evaluation method.

[0106] The monitoring unit can apply different monitoring and evaluation methods according to the patient's specific rehabilitation goals when monitoring and evaluating the rehabilitation plan. For example, the monitoring unit can select the optimal monitoring and evaluation method according to the patient's rehabilitation goals. The monitoring unit can adjust the monitoring and evaluation methods based on the patient's specific rehabilitation goals. The monitoring unit can apply different monitoring and evaluation methods according to the patient's rehabilitation goals. For example, the monitoring unit can select the optimal monitoring and evaluation method according to the patient's rehabilitation goals. The monitoring unit can adjust the monitoring and evaluation methods based on the patient's specific rehabilitation goals. The monitoring unit can apply different monitoring and evaluation methods according to the patient's rehabilitation goals. This improves the accuracy of monitoring and evaluation by applying monitoring and evaluation methods according to the patient's rehabilitation goals. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input patient rehabilitation goal data into a generating AI and have the generating AI execute the application of monitoring and evaluation methods.

[0107] The monitoring unit can estimate the patient's emotions and determine the priority of monitoring and evaluation of the rehabilitation plan based on the estimated emotions. For example, if the patient is stressed, the monitoring unit will prioritize important monitoring and evaluation. If the patient is relaxed, the monitoring unit will prioritize detailed monitoring and evaluation. If the patient is in a hurry, the monitoring unit will prioritize the minimum necessary monitoring and evaluation. For example, if the patient is stressed, the monitoring unit will prioritize important monitoring and evaluation. If the patient is relaxed, the monitoring unit will prioritize detailed monitoring and evaluation. If the patient is in a hurry, the monitoring unit will prioritize the minimum necessary monitoring and evaluation. This allows for prioritizing important monitoring and evaluation of the rehabilitation plan according to the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input patient emotion data into a generating AI and have the generating AI perform emotion estimation.

[0108] The monitoring unit can select the optimal monitoring and evaluation method when monitoring and evaluating the rehabilitation plan, taking into account the patient's geographical location information. For example, the monitoring unit can provide the optimal monitoring and evaluation method based on the patient's current location. The monitoring unit can select a monitoring and evaluation method considering the patient's geographical location information. The monitoring unit can determine the priority of monitoring and evaluation methods based on the patient's geographical location information. For example, the monitoring unit can provide the optimal monitoring and evaluation method based on the patient's current location. The monitoring unit can select a monitoring and evaluation method considering the patient's geographical location information. The monitoring unit can determine the priority of monitoring and evaluation methods based on the patient's geographical location information. This allows the monitoring unit to provide the optimal monitoring and evaluation method by taking into account the patient's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the patient's geographical location information into a generating AI and have the generating AI perform the selection of monitoring and evaluation methods.

[0109] The monitoring unit can analyze a patient's social media activity and propose monitoring and evaluation methods when monitoring and evaluating a rehabilitation plan. For example, the monitoring unit can analyze a patient's social media activity and propose relevant monitoring and evaluation methods. The monitoring unit can select monitoring and evaluation methods from the patient's social media activity. The monitoring unit can determine the priority of monitoring and evaluation methods based on the patient's social media activity. For example, the monitoring unit can analyze a patient's social media activity and propose relevant monitoring and evaluation methods. The monitoring unit can select monitoring and evaluation methods from the patient's social media activity. The monitoring unit can determine the priority of monitoring and evaluation methods based on the patient's social media activity. This allows the monitoring unit to propose relevant monitoring and evaluation methods by analyzing the patient's social media activity. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input patient social media activity data into a generating AI and have the generating AI execute the proposal of monitoring and evaluation methods.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The rehabilitation coach AI agent system can also collect and analyze patient lifestyle data. For example, it can collect data on the patient's diet, sleep patterns, and daily activity levels, and adjust the rehabilitation plan based on this data. This allows for the provision of a rehabilitation plan that takes into account the patient's overall health condition. Furthermore, it can more accurately monitor the patient's rehabilitation progress based on lifestyle data and modify the plan as needed. In addition, by analyzing lifestyle data, it becomes possible to predict the patient's health risks and provide preventative advice.

[0112] The rehabilitation coach AI agent system can collect and analyze patients' social network data. For example, it can collect data on the frequency and content of communication between patients and their family and friends, and adjust rehabilitation plans based on this data. This allows for the provision of rehabilitation plans that take into account the patient's social support situation. Furthermore, it can estimate the patient's motivation and stress levels based on social network data and modify the plan as needed. In addition, by analyzing social network data, it becomes possible to predict the patient's risk of isolation and provide appropriate support.

[0113] The rehabilitation coach AI agent system can collect and analyze patient biometric data. For example, it can collect biometric data such as heart rate, blood pressure, and body temperature, and adjust rehabilitation plans based on this data. This allows for the provision of rehabilitation plans that take into account the patient's physiological state. Furthermore, based on biometric data, the system can more accurately monitor the patient's rehabilitation progress and modify the plan as needed. In addition, by analyzing biometric data, it becomes possible to predict the patient's health risks and provide preventative advice.

[0114] The rehabilitation coach AI agent system can collect and analyze patient environmental data. For example, it can collect data on the patient's living environment, work environment, and climate conditions, and adjust the rehabilitation plan based on this data. This allows for the provision of a rehabilitation plan that takes the patient's environmental conditions into consideration. Furthermore, based on the environmental data, the system can more accurately monitor the patient's rehabilitation progress and modify the plan as needed. In addition, by analyzing the environmental data, it becomes possible to predict the patient's health risks and provide preventative advice.

[0115] The rehabilitation coach AI agent system can collect and analyze patients' genetic information. For example, it can collect patients' genetic risk factors and genetic characteristics and adjust rehabilitation plans based on this data. This allows for the provision of rehabilitation plans that take into account the patient's genetic background. Furthermore, based on genetic information, the system can more accurately monitor the patient's rehabilitation progress and modify the plan as needed. In addition, by analyzing genetic information, it becomes possible to predict the patient's health risks and provide preventative advice.

[0116] The rehabilitation coach AI agent system can estimate a patient's emotions and adjust the motivational elements of the rehabilitation plan based on those emotions. For example, if a patient is feeling stressed, it can suggest relaxing exercises. If a patient is feeling motivated, it can suggest challenging exercises. If a patient is tired, it can suggest lighter exercises. By adjusting the motivational elements of the rehabilitation plan according to the patient's emotions, it can provide more effective rehabilitation.

[0117] The rehabilitation coach AI agent system can estimate a patient's emotions and adjust the content of feedback based on those emotions. For example, if a patient is feeling anxious, it can provide feedback that includes many words of encouragement. If a patient is confident, it can provide feedback that points out specific areas for improvement. If a patient is feeling down, it can provide positive feedback. By adjusting the content of feedback according to the patient's emotions, it can provide more effective support.

[0118] The rehabilitation coach AI agent system can estimate a patient's emotions and adjust the pace of the rehabilitation plan based on those emotions. For example, if a patient is stressed, the rehabilitation plan can be slowed down. If a patient is relaxed, the rehabilitation plan can be accelerated. If a patient is tired, the rehabilitation plan can be temporarily paused. By adjusting the pace of the rehabilitation plan according to the patient's emotions, more effective rehabilitation can be provided.

[0119] The rehabilitation coach AI agent system can estimate a patient's emotions and customize the content of the rehabilitation plan based on those emotions. For example, if a patient is feeling stressed, it can provide a plan that includes many relaxing exercises. If a patient is feeling motivated, it can provide a plan that includes many challenging exercises. If a patient is feeling tired, it can provide a plan that includes many light exercises. By customizing the content of the rehabilitation plan according to the patient's emotions, it can provide more effective rehabilitation.

[0120] The rehabilitation coach AI agent system can estimate a patient's emotions and adjust the feedback method of the rehabilitation plan based on those emotions. For example, if a patient is feeling anxious, it can provide feedback in a gentle tone. If a patient is confident, it can provide feedback that points out specific areas for improvement. If a patient is feeling down, it can provide positive feedback. By adjusting the feedback method according to the patient's emotions, it can provide more effective support.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects personal information. This personal information includes age, gender, occupation, medical history, emotional state, and specific rehabilitation goals. The data collection unit provides an interface for inputting the patient's age and gender, and has a database for recording occupation and medical history. It also has sensors to estimate emotional state, and can estimate emotions by analyzing the patient's facial expressions and voice. Step 2: The analysis unit analyzes the movement data based on the individual information collected by the data acquisition unit. The movement data includes data from sensors and robotic devices. The analysis unit analyzes the movement data obtained from sensors to understand the patient's movement patterns and analyzes the movement data obtained from robotic devices to evaluate the patient's motor ability. It also monitors the patient's rehabilitation progress based on the movement data. Step 3: The generation unit generates a rehabilitation plan based on the exercise data analyzed by the analysis unit. The rehabilitation plan includes the type, frequency, and intensity of exercises. The generation unit selects the optimal exercises based on the analyzed exercise data and adjusts the frequency and intensity of the exercises. It also customizes the rehabilitation plan to meet the individual needs of the patient. Step 4: The support department assists in implementing the rehabilitation plan generated by the generation department. The support department provides feedback to the patient as they perform exercises, monitors their movements in real time, and provides guidance on modifying the exercises as needed. They also record the patient's rehabilitation progress and report it to the physical therapist. Step 5: The monitoring department monitors and evaluates the implementation of the rehabilitation plan supported by the support department. The monitoring department monitors the patient's exercise data in real time and evaluates the progress of the rehabilitation plan. It provides feedback as needed and instructs on modifying the exercises. It also evaluates the effectiveness of the rehabilitation plan and incorporates the findings into the creation of the next rehabilitation plan.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, support unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects patient individual information using the sensors and input interface of the smart device 14. The analysis unit analyzes the movement data using the identification processing unit 290 of the data processing unit 12 to understand the patient's movement patterns. The generation unit generates a rehabilitation plan using the identification processing unit 290 of the data processing unit 12. The support unit assists in the execution of the rehabilitation plan generated by the control unit 46A of the smart device 14 and provides feedback. The monitoring unit monitors the patient's movement data in real time using the sensors of the smart device 14 and evaluates the execution status of the rehabilitation plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, support unit, and monitoring unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects patient individual information using the sensors and input interface of the smart glasses 214. The analysis unit analyzes the movement data using the identification processing unit 290 of the data processing unit 12 to understand the patient's movement patterns. The generation unit generates a rehabilitation plan using the identification processing unit 290 of the data processing unit 12. The support unit assists in the execution of the rehabilitation plan generated by the control unit 46A of the smart glasses 214 and provides feedback. The monitoring unit monitors the patient's movement data in real time using the sensors of the smart glasses 214 and evaluates the execution status of the rehabilitation plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, support unit, and monitoring unit, is implemented in at least one of the following: a headset terminal 314 and a data processing unit 12. For example, the data collection unit collects patient individual information using the sensors and input interface of the headset terminal 314. The analysis unit analyzes the movement data using the identification processing unit 290 of the data processing unit 12 to understand the patient's movement patterns. The generation unit generates a rehabilitation plan using the identification processing unit 290 of the data processing unit 12. The support unit assists in the execution of the rehabilitation plan generated by the control unit 46A of the headset terminal 314 and provides feedback. The monitoring unit monitors the patient's movement data in real time using the sensors of the headset terminal 314 and evaluates the execution status of the rehabilitation plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the data collection unit, analysis unit, generation unit, support unit, and monitoring unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects patient individual information using the sensors and input interfaces of the robot 414. The analysis unit analyzes the movement data using the identification processing unit 290 of the data processing unit 12 to understand the patient's movement patterns. The generation unit generates a rehabilitation plan using the identification processing unit 290 of the data processing unit 12. The support unit assists in the execution of the rehabilitation plan generated by the control unit 46A of the robot 414 and provides feedback. The monitoring unit monitors the patient's movement data in real time using the sensors of the robot 414 and evaluates the execution status of the rehabilitation plan. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The collection department collects information on individual characteristics, An analysis unit analyzes motion data based on the individual information collected by the aforementioned collection unit, A generation unit generates a rehabilitation plan based on the exercise data analyzed by the analysis unit, A support unit that assists in the execution of the rehabilitation plan generated by the generation unit, The system includes a monitoring unit that monitors and evaluates the execution of the rehabilitation plan supported by the aforementioned support unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect individual information about the patient, such as age, gender, occupation, medical history, emotional state, and specific rehabilitation goals. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze motion data obtained from sensors and robotic devices. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Based on analyzed exercise data, it generates optimal exercise and rehabilitation plans. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned support unit, Support the implementation of the generated rehabilitation plan. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, The rehabilitation plan is monitored and evaluated in real time, and feedback is provided as needed. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the patient's emotions and adjusts the timing of collecting personality information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the patient's past rehabilitation history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting individual information, filtering is performed based on the patient's current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the collection of personality information based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting individual information, the collection of highly relevant information is prioritized, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting individual information, we analyze the patient's social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts the analysis method of the motor data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing exercise data, referencing the patient's past exercise history improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing exercise data, different analysis algorithms are applied depending on the patient's specific rehabilitation goals. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the patient's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing exercise data, the analysis should take into account the geographical distribution of patients. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing exercise data, refer to relevant literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is The system estimates the patient's emotions and adjusts the method of generating the rehabilitation plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating a rehabilitation plan, the system references the patient's past rehabilitation history to generate the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating a rehabilitation plan, different generation algorithms are applied depending on the patient's specific rehabilitation goals. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system estimates the patient's emotions and prioritizes the rehabilitation plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating a rehabilitation plan, the optimal plan is generated by taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating rehabilitation plans, we refer to relevant literature to improve the accuracy of the generation process. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit, The system estimates the patient's emotions and adjusts the methods of supporting the implementation of the rehabilitation plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit, When supporting the implementation of a rehabilitation plan, the optimal support method is selected by referring to the patient's past rehabilitation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit, When supporting the implementation of a rehabilitation plan, different support methods are applied according to the patient's specific rehabilitation goals. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit, The system estimates the patient's emotions and prioritizes support for implementing the rehabilitation plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit, When supporting the implementation of a rehabilitation plan, the most appropriate support method is selected by considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit, When supporting the implementation of a rehabilitation plan, we analyze the patient's social media activity and propose support measures. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned monitoring unit, The system estimates the patient's emotions and adjusts the monitoring and evaluation methods of the rehabilitation plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned monitoring unit, When monitoring and evaluating a rehabilitation plan, the optimal monitoring and evaluation method is selected by referring to the patient's past rehabilitation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned monitoring unit, When monitoring and evaluating a rehabilitation plan, different monitoring and evaluation methods should be applied depending on the patient's specific rehabilitation goals. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned monitoring unit, The system estimates the patient's emotions and determines the prioritization of monitoring and evaluation of the rehabilitation plan based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned monitoring unit, When monitoring and evaluating rehabilitation plans, the most appropriate monitoring and evaluation method should be selected, taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned monitoring unit, When monitoring and evaluating rehabilitation plans, we propose methods for analyzing and evaluating patients' social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects information on individual characteristics, An analysis unit analyzes motion data based on the individual information collected by the aforementioned collection unit, A generation unit generates a rehabilitation plan based on the exercise data analyzed by the analysis unit, A support unit that assists in the execution of the rehabilitation plan generated by the generation unit, The system includes a monitoring unit that monitors and evaluates the implementation of the rehabilitation plan supported by the aforementioned support unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect individual information about the patient, such as age, gender, occupation, medical history, emotional state, and specific rehabilitation goals. The system according to feature 1.

3. The aforementioned analysis unit, Analyze motion data obtained from sensors and robotic devices. The system according to feature 1.

4. The generating unit is Based on analyzed exercise data, it generates optimal exercise and rehabilitation plans. The system according to feature 1.

5. The aforementioned support unit, Support the implementation of the generated rehabilitation plan. The system according to feature 1.

6. The aforementioned monitoring unit, The rehabilitation plan is monitored and evaluated in real time, and feedback is provided as needed. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the patient's emotions and adjusts the timing of collecting personality information based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the patient's past rehabilitation history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting individual information, filtering is performed based on the patient's current living situation and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is The system estimates the patient's emotions and prioritizes the collection of personality information based on those estimated emotions. The system according to feature 1.

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