system

Abstract

The system according to this embodiment aims to propose an individualized treatment plan to patients with lower back pain and to track the progress of treatment in real time. [Solution] The system according to the embodiment comprises a data collection unit, a proposal unit, a guidance unit, a treatment center proposal unit, and a tracking unit. The data collection unit collects data on the patient's symptoms and lifestyle. The proposal unit analyzes the data collected by the data collection unit and proposes an individualized treatment plan. The guidance unit provides guidance on the correct exercises based on the treatment plan proposed by the proposal unit. The treatment center proposal unit proposes the most suitable treatment center based on the treatment plan proposed by the proposal unit. The tracking unit tracks the effectiveness and progress of the treatment in real time based on the treatment plan proposed by the proposal unit.

Description

Technical Field

[0004]

[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 individualized treatment plan for patients with low back pain and the progress of treatment have not been sufficiently tracked, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an individualized treatment plan for patients with low back pain and track the progress of treatment in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a proposal unit, a guidance unit, a treatment center proposal unit, and a tracking unit. The data collection unit collects data on the patient's symptoms and lifestyle. The proposal unit analyzes the data collected by the data collection unit and proposes an individualized treatment plan. The guidance unit provides guidance on the correct exercises based on the treatment plan proposed by the proposal unit. The treatment center proposal unit proposes the most suitable treatment center based on the treatment plan proposed by the proposal unit. The tracking unit tracks the effectiveness and progress of the treatment in real time based on the treatment plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose an individualized treatment plan to patients with lower back pain and track the progress of treatment in real time. [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, and the like. 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 lower back pain treatment system according to an embodiment of the present invention is a system in which AI proposes an individualized treatment plan to a patient with lower back pain and manages the process until improvement. The lower back pain treatment system uses generative AI to guide the user in the correct exercises, enabling the user to have an effective treatment experience even at home. Specifically, it consists of the following steps. First, the lower back pain treatment system uses AI to collect data such as the patient's symptoms and lifestyle habits and proposes an individualized treatment plan. Next, the lower back pain treatment system uses generative AI to guide the user in the correct exercises, and the user performs the exercises at home. Furthermore, the lower back pain treatment system uses AI to find the optimal treatment center and tracks the effectiveness and progress of the treatment in real time. It provides support with appropriate feedback when needed. This dramatically changes lower back pain treatment. For example, the lower back pain treatment system collects detailed data such as what symptoms the patient has and what lifestyle habits they have. For example, it collects data such as what movements cause the patient pain and what postures they often spend time in. This allows the system to understand the patient's symptoms and lifestyle habits. Next, based on the collected data, the lower back pain treatment system uses AI to propose an individualized treatment plan. The AI ​​analyzes the collected data and proposes the optimal treatment plan for the patient. For example, it suggests specific exercises, stretches, and lifestyle improvements. This allows patients to receive a treatment plan tailored to their needs. Furthermore, the back pain treatment system uses AI to guide patients through the correct exercises. The AI ​​instructs patients on how to perform the exercises correctly, and patients perform them at home. For example, the AI ​​can demonstrate the exercise steps through a video, allowing patients to follow along. This enables patients to receive effective treatment even at home. The back pain treatment system also uses AI to find the optimal treatment center. The AI ​​suggests the best treatment center based on the patient's symptoms and treatment plan. For example, it may suggest a treatment center specializing in the patient's symptoms or one located within the patient's living area. This allows patients to find a treatment center that suits them. In addition, the back pain treatment system uses AI to track the effectiveness and progress of the treatment in real time. The AI ​​monitors the patient's treatment progress and evaluates the effectiveness of the treatment.For example, it tracks changes in pain after a patient performs exercises and the effectiveness of treatment at a clinic. This allows for tracking the patient's treatment progress. Finally, the back pain treatment system provides support with appropriate feedback when needed. AI provides appropriate feedback according to the patient's treatment progress. For example, it can modify exercise methods or revise the treatment plan. This ensures that patients always receive the optimal treatment. This allows the back pain treatment system to dramatically change back pain treatment. Patients can receive effective treatment at home and receive treatment at the optimal clinic. They can also track the progress of their treatment in real time and receive necessary feedback. This improves the effectiveness of back pain treatment and improves the patient's quality of life. This allows the back pain treatment system to provide individualized treatment plans based on the patient's symptoms and lifestyle, and to track the effectiveness and progress of treatment in real time.

[0029] The lower back pain treatment system according to this embodiment comprises a data collection unit, a proposal unit, a guidance unit, a treatment center proposal unit, and a tracking unit. The data collection unit collects data on the patient's symptoms and lifestyle. For example, the data collection unit collects data such as what movements cause the patient pain and what postures the patient often maintains. The data collection unit can collect detailed data to understand the patient's symptoms and lifestyle. The proposal unit analyzes the data collected by the data collection unit and proposes an individualized treatment plan. For example, the proposal unit proposes specific exercises, stretches, and lifestyle improvements. The proposal unit can analyze the collected data to provide the patient with the optimal treatment plan. The guidance unit instructs the patient on the correct exercises based on the treatment plan proposed by the proposal unit. The guidance unit uses a generative AI to instruct the patient on the correct way to perform the exercises. For example, the generative AI can demonstrate the exercise procedure through a video, allowing the patient to perform the exercises accordingly. The guidance unit can instruct the patient on the correct exercises so that they can receive effective treatment at home. The treatment center proposal unit proposes the optimal treatment center based on the treatment plan proposed by the proposal unit. The treatment center suggestion unit suggests, for example, treatment centers specializing in the patient's symptoms or treatment centers located within the patient's living area. The treatment center suggestion unit can suggest the most suitable treatment center so that the patient can find one that suits them. The tracking unit tracks the effectiveness and progress of treatment in real time based on the treatment plan suggested by the suggestion unit. The tracking unit tracks, for example, changes in pain after the patient performs exercises or the effectiveness of treatment at the treatment center. The tracking unit can track the effectiveness and progress of treatment in real time in order to understand the progress of the patient's treatment. As a result, the lumbar pain treatment system according to this embodiment can provide an individualized treatment plan based on the patient's symptoms and lifestyle, and can track the effectiveness and progress of treatment in real time.

[0030] The data collection unit collects data on the patient's symptoms and lifestyle. Specifically, it collects data such as what movements cause the patient pain and what postures the patient often maintains. The data collection unit can utilize wearable devices and smartphone apps to record the patient's movements and postures in detail in their daily life. For example, wearable devices can monitor the patient's movements and postures in real time and collect data. This allows for an accurate understanding of what movements and postures cause the patient pain. Smartphone apps also record the movements and postures the patient performs on a daily basis and collect data. Patients can input the degree and frequency of pain through the app, which allows the data collection unit to understand the patient's symptoms in detail. Furthermore, the data collection unit also collects data on the patient's lifestyle. For example, it can collect data on the patient's sleep patterns, diet, and exercise habits to evaluate the impact of the patient's lifestyle on lower back pain. This allows the data collection unit to comprehensively understand the patient's symptoms and lifestyle and use this information to create an individualized treatment plan.

[0031] The proposal department analyzes data collected by the data collection department and proposes personalized treatment plans. Specifically, based on the collected data, it proposes the most suitable exercises, stretches, and lifestyle improvements for the patient. The proposal department uses AI to analyze data and create optimal treatment plans based on the patient's symptoms and lifestyle. For example, the AI ​​analyzes data on the patient's movements and posture to evaluate whether specific exercises or stretches are effective. It can also analyze the patient's lifestyle data and propose improvements to diet, sleep, and exercise habits. Furthermore, the proposal department can customize treatment plans according to the patient's symptoms and lifestyle. For example, if a patient experiences pain during a specific movement, it will propose exercises to improve that movement. It can also propose feasible improvements that are within the patient's lifestyle. In this way, the proposal department can provide the patient with the most suitable treatment plan and achieve effective treatment.

[0032] The instruction department provides guidance on the correct exercises based on the treatment plan proposed by the proposal department. Specifically, it uses generative AI to instruct patients on the correct way to perform the exercises. The generative AI shows the exercise steps through video and audio, supporting patients so that they can perform the exercises as instructed. For example, the generative AI monitors the patient's movements in real time and provides feedback to help maintain correct posture and movement. When the patient performs the exercises, the generative AI can show the correct steps through video and give instructions by voice. This allows patients to receive effective treatment even at home. In addition, the generative AI can record the patient's progress in the exercises and adjust the content of the exercises as needed. For example, if a patient is unable to perform a particular exercise correctly, the generative AI can suggest an alternative exercise. This allows the instruction department to support patients so that they can perform the exercises correctly and receive effective treatment.

[0033] The Treatment Center Recommendation Department proposes the most suitable treatment center based on the treatment plan proposed by the department. Specifically, it recommends treatment centers that specialize in the patient's symptoms or those located within the patient's living area. The Treatment Center Recommendation Department utilizes a database of treatment centers to select the most suitable center based on the patient's symptoms and lifestyle. For example, it can recommend the most suitable treatment center to the patient based on data such as the treatment center's area of ​​expertise, treatment record, and patient reviews. In addition, the Treatment Center Recommendation Department prioritizes recommending treatment centers within the patient's living area, making it easier for patients to find a treatment center that is convenient for them. Furthermore, the Treatment Center Recommendation Department can also provide information such as the reservation status and operating hours of the recommended treatment centers. This allows patients to find a treatment center that suits them and receive treatment smoothly.

[0034] The tracking unit tracks the effectiveness and progress of treatment in real time based on the treatment plan proposed by the suggestion unit. Specifically, it tracks changes in pain after the patient performs exercises and the effectiveness of treatment at the clinic. The tracking unit collects patient feedback and data from wearable devices to understand the progress of treatment. For example, it records whether the patient's pain has decreased after performing exercises and evaluates the effectiveness of the treatment. It can also track the effectiveness of treatment at the clinic and confirm whether the patient's symptoms are improving. Furthermore, the tracking unit can adjust the treatment plan according to the progress of treatment. For example, if no effect is observed from the treatment, it can work with the suggestion unit to create a new treatment plan. This allows the tracking unit to understand the progress of the patient's treatment in real time and continue effective treatment.

[0035] The instruction unit can use generative AI to instruct on the correct way to perform exercises. For example, the instruction unit can have the generative AI demonstrate the exercise procedure through a video, allowing the patient to perform the exercise accordingly. The instruction unit can have the generative AI generate a video demonstrating the exercise procedure, allowing the patient to perform the exercise while watching the video. The instruction unit can have the generative AI generate text demonstrating the exercise procedure, allowing the patient to perform the exercise while referring to the text. In this way, the correct way to perform exercises can be effectively instructed by using generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the instruction unit may be performed using, for example, generative AI, or not using generative AI. For example, the instruction unit can input prompts indicating the exercise procedure into the generative AI, and then instruct on how to perform the exercise using the video or text generated by the generative AI.

[0036] The clinic recommendation department can suggest the most suitable clinic based on the patient's symptoms and treatment plan. For example, it can suggest clinics specializing in the patient's symptoms or clinics located within the patient's living area. The clinic recommendation department can suggest the most suitable clinic so that the patient can find one that suits them. The clinic recommendation department may use AI or not to suggest the most suitable clinic based on the patient's symptoms and treatment plan. For example, the clinic recommendation department can suggest the most suitable clinic using an AI model that takes the patient's symptoms and treatment plan as input and outputs the most suitable clinic. This allows the clinic to suggest the most suitable clinic based on the patient's symptoms and treatment plan.

[0037] The tracking unit can monitor the progress of a patient's treatment and evaluate the effectiveness of the treatment. For example, the tracking unit can track changes in pain after a patient performs exercise or the effectiveness of treatment at a clinic. The tracking unit can track the effectiveness and progress of treatment in real time to understand the progress of the patient's treatment. The tracking unit may or may not use AI to monitor the progress of the patient's treatment and evaluate the effectiveness of the treatment. For example, the tracking unit can evaluate the effectiveness of the treatment using an AI model that takes the progress of the patient's treatment as input and outputs the effectiveness of the treatment. This allows for monitoring the progress of the patient's treatment and evaluating the effectiveness of the treatment.

[0038] The tracking unit can track changes in pain after a patient performs exercise and the effectiveness of treatment at a clinic. For example, the tracking unit can track changes in pain after a patient performs exercise. The tracking unit can track the effects of treatment after a patient receives treatment at a clinic. The tracking unit can track the effectiveness and progress of treatment in real time to understand the progress of the patient's treatment. The tracking unit may use AI or not to track changes in pain after a patient performs exercise and the effectiveness of treatment at a clinic. For example, the tracking unit can track the effectiveness of treatment by using an AI model that takes changes in pain after a patient performs exercise and the effectiveness of treatment at a clinic as input and outputs the progress of treatment. This allows tracking changes in pain after a patient performs exercise and the effectiveness of treatment at a clinic.

[0039] The tracking unit can provide appropriate feedback according to the progress of the patient's treatment. For example, the tracking unit can modify the exercise method according to the progress of the patient's treatment. The tracking unit can revise the treatment plan according to the progress of the patient's treatment. The tracking unit may or may not use AI to provide appropriate feedback according to the progress of the patient's treatment. For example, the tracking unit can provide feedback using an AI model that takes the progress of the patient's treatment as input and outputs appropriate feedback. This makes it possible to provide appropriate feedback according to the progress of the patient's treatment.

[0040] The data collection unit can analyze a patient's past treatment history and select the optimal data collection method. For example, the data collection unit can analyze the types and frequency of treatments a patient has received in the past and prioritize the collection of data necessary when receiving similar treatments. If a particular treatment was effective based on the patient's past treatment history, the data collection unit can focus on collecting data related to that treatment. Based on the patient's past treatment history, the data collection unit can collect data at the timing when treatment is most likely to be effective. This allows the optimal data collection method to be selected by analyzing the patient's past treatment 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 select a data collection method using an AI model that takes the patient's past treatment history as input and outputs the optimal data collection method.

[0041] The data collection unit can filter data based on the patient's current lifestyle and activity level during data collection. For example, the data collection unit can prioritize collecting data during activities, taking into account the patient's daily activities (work, exercise, etc.). The data collection unit can adjust the timing of data collection based on the patient's daily rhythm to obtain more accurate data. The data collection unit can adjust the frequency and method of data collection according to the patient's activity level, collecting data within a reasonable range. This allows for the collection of more relevant data by filtering data based on the patient's current lifestyle and activity level. 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 perform data collection using an AI model that takes the patient's lifestyle and activity level as input and outputs filtered data collection results.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location during data collection. For example, if the patient is in a specific area, the data collection unit can collect environmental data (temperature, humidity, etc.) for that area and use it to aid in treatment. If the patient is on the move, the data collection unit can collect environmental data of the destination and incorporate it into the treatment plan. If the patient is in a specific facility (gym, hospital, etc.), the data collection unit can collect environmental data of that facility and use it to aid in treatment. In this way, by considering the patient's geographical location, highly relevant data can be prioritized for collection. 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 perform data collection using an AI model that takes the patient's geographical location as input and outputs highly relevant data.

[0043] The data collection unit can analyze patients' social media activity and collect relevant data during data collection. For example, the data collection unit can collect health information (exercise records, dietary information, etc.) shared by patients on social media and incorporate it into treatment plans. The data collection unit can analyze emotions (stress, joy, etc.) expressed by patients on social media and use this information to aid in treatment. The data collection unit can collect relevant information from health-related accounts that patients follow on social media. This allows for the collection of relevant data by analyzing patients' 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 perform data collection using an AI model that takes patients' social media activity as input and outputs relevant data.

[0044] The suggestion unit can adjust the level of detail in its treatment plan proposals based on the severity of the symptoms. For example, if the symptoms are mild, the suggestion unit may suggest simple exercises or lifestyle improvements. If the symptoms are moderate, the suggestion unit may suggest a detailed exercise plan or stretching method. If the symptoms are severe, the suggestion unit may suggest specialized treatment or a visit to a medical institution. By adjusting the level of detail in the suggestions based on the severity of the symptoms, a more appropriate treatment plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can adjust the level of detail in the treatment plan using an AI model that takes the severity of the patient's symptoms as input and outputs the level of detail in the suggestions.

[0045] The proposal unit can apply different treatment algorithms depending on the patient's lifestyle when proposing a treatment plan. For example, if the patient primarily works at a desk, the proposal unit can propose a treatment plan that emphasizes posture improvement and stretching. If the patient is sedentary, the proposal unit can propose a treatment plan that includes light exercise and physical activity. If the patient leads an active lifestyle, the proposal unit can propose a treatment plan that emphasizes muscle training and flexibility improvement. By applying different treatment algorithms according to the patient's lifestyle, a more effective treatment plan can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can propose a treatment plan using an AI model that takes patient lifestyle data as input and outputs a treatment algorithm.

[0046] The suggestion unit can determine the priority of treatment plans based on the timing of symptom onset. For example, if symptoms have recently appeared, the suggestion unit can suggest a plan that emphasizes early treatment. If symptoms have persisted chronically, the suggestion unit can suggest a long-term treatment plan. If symptoms occur during a specific time period, the suggestion unit can suggest a treatment plan tailored to that time period. By prioritizing suggestions based on the timing of symptom onset, a more effective treatment plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can determine the priority of treatment plans using an AI model that takes the timing of the patient's symptom onset as input and outputs the priority of suggestions.

[0047] The suggestion unit can adjust the order of treatment plan suggestions by referring to the patient's relevant medical data. For example, the suggestion unit can suggest the most effective treatment plan based on the patient's past diagnostic data. The suggestion unit can suggest treatments that should be prioritized, taking into account the patient's current health condition. The suggestion unit can refer to the patient's allergy information and suggest a treatment plan that avoids allergies. In this way, a more effective treatment plan can be provided by referring to the patient's relevant medical data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can adjust the order of treatment plans using an AI model that takes the patient's medical data as input and outputs the order of treatment plans.

[0048] The instruction system can adjust the intensity of exercises based on the patient's fitness level during exercise instruction. For example, if the patient is a beginner, the instruction system can suggest a plan that starts with light exercises. If the patient is an intermediate level, the instruction system can suggest exercises of moderate intensity. If the patient is an advanced level, the instruction system can suggest high-intensity exercises. By adjusting the exercise intensity based on the patient's fitness level, more appropriate exercise instruction can be provided. Some or all of the above processing in the instruction system may be performed using AI, for example, or without AI. For example, the instruction system can adjust the exercise intensity using an AI model that takes the patient's fitness level as input and outputs the exercise intensity.

[0049] The instruction unit can suggest exercise timings according to the patient's lifestyle rhythm during exercise instruction. For example, if the patient is a morning person, the instruction unit can suggest morning exercises. If the patient is a night owl, the instruction unit can suggest evening exercises. If the patient has an irregular lifestyle rhythm, the instruction unit can suggest exercises at flexible times. This allows for more effective exercise instruction by suggesting exercise timings according to the patient's lifestyle rhythm. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can suggest exercise timings using an AI model that takes the patient's lifestyle rhythm data as input and outputs exercise timings.

[0050] The instruction department can suggest the optimal exercise location when providing exercise instruction, taking into account the patient's geographical location. For example, if the patient is at home, the instruction department can suggest exercises that can be done at home. If the patient is at a gym, the instruction department can suggest exercises that utilize the gym's equipment. If the patient is in a park, the instruction department can suggest exercises that can be done in the park. In this way, the optimal exercise location can be suggested by considering the patient's geographical location. Some or all of the above processing in the instruction department may be performed using AI, for example, or without AI. For example, the instruction department can suggest an exercise location using an AI model that takes the patient's geographical location as input and outputs the optimal exercise location.

[0051] The instruction department can customize exercise instruction by referring to the patient's past exercise history. For example, the instruction department can analyze the effectiveness of exercises the patient has performed in the past and suggest effective exercises. The instruction department can avoid exercises the patient has previously struggled with and focus on exercises the patient excels at. Based on the patient's past exercise history, the instruction department can suggest exercises that are appropriate to the patient's progress. In this way, by referring to the patient's past exercise history, more effective exercise instruction can be provided. Some or all of the above processes in the instruction department may be performed using AI, for example, or not. For example, the instruction department can customize exercise instruction using an AI model that takes the patient's past exercise history as input and outputs instruction content.

[0052] The clinic recommendation department can select the most suitable clinic by referring to the patient's past treatment history when recommending clinics. For example, the clinic recommendation department can analyze the effectiveness of treatments the patient has received in the past and recommend clinics that offer similar treatments. If a particular treatment was effective based on the patient's past treatment history, the clinic recommendation department can recommend clinics that offer that treatment. Based on the patient's past treatment history, the clinic recommendation department can recommend clinics where treatment is likely to be effective. In this way, the most suitable clinic can be selected by referring to the patient's past treatment history. Some or all of the above processes in the clinic recommendation department may be performed using AI, for example, or without AI. For example, the clinic recommendation department can select a clinic using an AI model that takes the patient's past treatment history as input and outputs the most suitable clinic.

[0053] The clinic recommendation department can prioritize suggesting clinics located within the patient's living area. For example, it can prioritize suggesting clinics close to the patient's home, clinics close to the patient's workplace, or clinics close to places the patient frequently visits (such as gyms or schools). By prioritizing clinics within the patient's living area, it can suggest more convenient clinics. Some or all of the above processing in the clinic recommendation department may be performed using AI, or not. For example, the clinic recommendation department can suggest clinics using an AI model that takes information about the patient's living area as input and outputs the most suitable clinic.

[0054] The clinic recommendation department can suggest the most suitable clinic by considering the patient's geographical location information. For example, the clinic recommendation department can prioritize suggesting clinics close to the patient's home, clinics close to the patient's workplace, or clinics close to places the patient frequently visits (such as gyms or schools). In this way, the clinic recommendation department can suggest the most suitable clinic by considering the patient's geographical location information. Some or all of the above processing in the clinic recommendation department may be performed using AI, for example, or without AI. For example, the clinic recommendation department can suggest clinics using an AI model that takes the patient's geographical location information as input and outputs the most suitable clinic.

[0055] The clinic recommendation department can analyze a patient's social media activity and suggest relevant clinics when making recommendations. For example, the clinic recommendation department can suggest clinics that the patient follows on social media. The clinic recommendation department can suggest highly-rated clinics based on reviews of clinics shared by the patient on social media. The clinic recommendation department can analyze the emotions (stress, joy, etc.) expressed by the patient on social media and suggest relevant clinics. In this way, relevant clinics can be suggested by analyzing the patient's social media activity. Some or all of the above processes in the clinic recommendation department may be performed using AI, for example, or not. For example, the clinic recommendation department can suggest clinics using an AI model that takes a patient's social media activity as input and outputs relevant clinics.

[0056] The tracking unit can predict the progress of treatment by referring to past treatment data when tracking the progress of treatment. For example, the tracking unit can predict the progress of treatment and suggest the next step based on the patient's past treatment data. The tracking unit can predict the timing when treatment is most likely to be effective based on the patient's past treatment data. The tracking unit can analyze the patient's past treatment data and display the progress of treatment in real time. This allows the tracking unit to predict the progress of treatment and suggest the next step by referring to past treatment data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can take the patient's past treatment data as input and predict the progress of treatment using an AI model that predicts the progress of treatment.

[0057] The tracking unit can apply different tracking algorithms depending on the patient's lifestyle when tracking the progress of treatment. For example, if the patient primarily does desk work, the tracking unit can apply a tracking algorithm that emphasizes posture improvement and stretching. If the patient is sedentary, the tracking unit can apply a tracking algorithm that includes light exercise and physical activity. If the patient leads an active lifestyle, the tracking unit can apply a tracking algorithm that emphasizes muscle training and flexibility improvement. By applying different tracking algorithms according to the patient's lifestyle, it is possible to provide more effective tracking of the progress of treatment. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can track the progress of treatment using an AI model that takes patient lifestyle data as input and outputs a tracking algorithm.

[0058] The tracking unit can display the progress of treatment while considering the patient's geographical location. For example, if the patient is at home, the tracking unit can prioritize displaying the progress of treatment that can be performed at home. If the patient is at a treatment center, the tracking unit can prioritize displaying the progress of treatment at the treatment center. If the patient is on the move, the tracking unit can prioritize displaying the progress of treatment that can be performed at the destination. This allows for a more appropriate display of treatment progress by considering the patient's geographical location. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can display the progress using an AI model that takes the patient's geographical location as input and outputs the progress of treatment.

[0059] The tracking unit can evaluate the progress of treatment by referring to the patient's relevant medical data when tracking the progress of treatment. For example, the tracking unit can evaluate the progress of treatment based on the patient's past diagnostic data. The tracking unit can evaluate the progress of treatment by considering the patient's current health status. The tracking unit can refer to the patient's allergy information and evaluate the progress of treatment to avoid allergies. This allows for a more accurate evaluation of the progress of treatment by referring to the patient's relevant medical data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can use an AI model that evaluates the progress of treatment, taking the patient's medical data as input.

[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 back pain treatment system can further analyze the patient's past treatment history and select the optimal data collection method. For example, it can analyze the types and frequency of treatments the patient has received in the past and prioritize the collection of data necessary when receiving similar treatments. If a particular treatment was effective based on the patient's past treatment history, data related to that treatment can be collected with emphasis. Based on the patient's past treatment history, data can be collected at times when treatment is likely to be effective. In this way, the optimal data collection method can be selected by analyzing the patient's past treatment history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can select a data collection method using an AI model that takes the patient's past treatment history as input and outputs the optimal data collection method.

[0062] The back pain treatment system can further prioritize the collection of highly relevant data by considering the patient's geographical location. For example, if the patient is in a specific area, environmental data for that area (temperature, humidity, etc.) can be collected and used for treatment. If the patient is on the move, environmental data for the destination can be collected and reflected in the treatment plan. If the patient is in a specific facility (gym, hospital, etc.), environmental data for that facility can be collected and used for treatment. In this way, by considering the patient's geographical location, highly relevant data can be prioritized for collection. 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 perform data collection using an AI model that takes the patient's geographical location as input and outputs highly relevant data.

[0063] The back pain treatment system can further analyze the patient's social media activity and collect relevant data. For example, it can collect health information (exercise records, diet, etc.) shared by the patient on social media and incorporate it into the treatment plan. It can analyze the emotions (stress, joy, etc.) expressed by the patient on social media and use this information to aid in treatment. It can collect relevant information from health-related accounts that the patient follows on social media. In this way, relevant data can be collected by analyzing the patient's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can collect data using an AI model that takes the patient's social media activity as input and outputs relevant data.

[0064] The back pain treatment system can further suggest exercise timings according to the patient's lifestyle. For example, if the patient is a morning person, morning exercises can be suggested. If the patient is a night owl, evening exercises can be suggested. If the patient has an irregular lifestyle, exercises can be suggested at flexible times. This allows for more effective exercise guidance by suggesting exercise timings according to the patient's lifestyle. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can suggest exercise timings using an AI model that takes the patient's lifestyle data as input and outputs exercise timings.

[0065] The back pain treatment system can further customize the instruction content by referring to the patient's past exercise history. For example, it can analyze the effectiveness of exercises the patient has performed in the past and suggest effective exercises. It can avoid exercises the patient has previously struggled with and focus on exercises they are good at. Based on the patient's past exercise history, it can suggest exercises that are appropriate to their progress. In this way, by referring to the patient's past exercise history, more effective exercise instruction can be provided. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can customize exercise instruction using an AI model that takes the patient's past exercise history as input and outputs instruction content.

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

[0067] Step 1: The data collection unit collects data on the patient's symptoms and lifestyle. For example, it collects data on what movements cause the patient pain and what postures the patient often maintains. The data collection unit can collect detailed data to understand the patient's symptoms and lifestyle. Step 2: The proposal unit analyzes the data collected by the data collection unit and proposes an individualized treatment plan. For example, it may suggest specific exercises, stretches, or lifestyle improvements. The proposal unit can analyze the collected data to provide the patient with the most suitable treatment plan. Step 3: The instruction team provides guidance on the correct exercises based on the treatment plan proposed by the proposal team. Generative AI is used to instruct patients on the correct way to perform the exercises. For example, the generative AI can demonstrate the exercise procedure through a video, allowing the patient to perform the exercises accordingly. The instruction team can then guide patients on the correct exercises so that they can receive effective treatment at home. Step 4: The clinic recommendation department proposes the most suitable clinic based on the treatment plan proposed by the department. For example, they may propose a clinic specializing in the patient's symptoms or a clinic located within the patient's living area. The clinic recommendation department can propose the most suitable clinic so that the patient can find one that is right for them. Step 5: The tracking unit tracks the effectiveness and progress of the treatment in real time based on the treatment plan proposed by the suggestion unit. For example, it tracks changes in pain after the patient performs exercises and the effectiveness of treatment at the clinic. The tracking unit can track the effectiveness and progress of the treatment in real time to understand the progress of the patient's treatment.

[0068] (Example of form 2) The lower back pain treatment system according to an embodiment of the present invention is a system in which AI proposes an individualized treatment plan to a patient with lower back pain and manages the process until improvement. The lower back pain treatment system uses generative AI to guide the user in the correct exercises, enabling the user to have an effective treatment experience even at home. Specifically, it consists of the following steps. First, the lower back pain treatment system uses AI to collect data such as the patient's symptoms and lifestyle habits and proposes an individualized treatment plan. Next, the lower back pain treatment system uses generative AI to guide the user in the correct exercises, and the user performs the exercises at home. Furthermore, the lower back pain treatment system uses AI to find the optimal treatment center and tracks the effectiveness and progress of the treatment in real time. It provides support with appropriate feedback when needed. This dramatically changes lower back pain treatment. For example, the lower back pain treatment system collects detailed data such as what symptoms the patient has and what lifestyle habits they have. For example, it collects data such as what movements cause the patient pain and what postures they often spend time in. This allows the system to understand the patient's symptoms and lifestyle habits. Next, based on the collected data, the lower back pain treatment system uses AI to propose an individualized treatment plan. The AI ​​analyzes the collected data and proposes the optimal treatment plan for the patient. For example, it suggests specific exercises, stretches, and lifestyle improvements. This allows patients to receive a treatment plan tailored to their needs. Furthermore, the back pain treatment system uses AI to guide patients through the correct exercises. The AI ​​instructs patients on how to perform the exercises correctly, and patients perform them at home. For example, the AI ​​can demonstrate the exercise steps through a video, allowing patients to follow along. This enables patients to receive effective treatment even at home. The back pain treatment system also uses AI to find the optimal treatment center. The AI ​​suggests the best treatment center based on the patient's symptoms and treatment plan. For example, it may suggest a treatment center specializing in the patient's symptoms or one located within the patient's living area. This allows patients to find a treatment center that suits them. In addition, the back pain treatment system uses AI to track the effectiveness and progress of the treatment in real time. The AI ​​monitors the patient's treatment progress and evaluates the effectiveness of the treatment.For example, it tracks changes in pain after a patient performs exercises and the effectiveness of treatment at a clinic. This allows for tracking the patient's treatment progress. Finally, the back pain treatment system provides support with appropriate feedback when needed. AI provides appropriate feedback according to the patient's treatment progress. For example, it can modify exercise methods or revise the treatment plan. This ensures that patients always receive the optimal treatment. This allows the back pain treatment system to dramatically change back pain treatment. Patients can receive effective treatment at home and receive treatment at the optimal clinic. They can also track the progress of their treatment in real time and receive necessary feedback. This improves the effectiveness of back pain treatment and improves the patient's quality of life. This allows the back pain treatment system to provide individualized treatment plans based on the patient's symptoms and lifestyle, and to track the effectiveness and progress of treatment in real time.

[0069] The lower back pain treatment system according to this embodiment comprises a data collection unit, a proposal unit, a guidance unit, a treatment center proposal unit, and a tracking unit. The data collection unit collects data on the patient's symptoms and lifestyle. For example, the data collection unit collects data such as what movements cause the patient pain and what postures the patient often maintains. The data collection unit can collect detailed data to understand the patient's symptoms and lifestyle. The proposal unit analyzes the data collected by the data collection unit and proposes an individualized treatment plan. For example, the proposal unit proposes specific exercises, stretches, and lifestyle improvements. The proposal unit can analyze the collected data to provide the patient with the optimal treatment plan. The guidance unit instructs the patient on the correct exercises based on the treatment plan proposed by the proposal unit. The guidance unit uses a generative AI to instruct the patient on the correct way to perform the exercises. For example, the generative AI can demonstrate the exercise procedure through a video, allowing the patient to perform the exercises accordingly. The guidance unit can instruct the patient on the correct exercises so that they can receive effective treatment at home. The treatment center proposal unit proposes the optimal treatment center based on the treatment plan proposed by the proposal unit. The treatment center suggestion unit suggests, for example, treatment centers specializing in the patient's symptoms or treatment centers located within the patient's living area. The treatment center suggestion unit can suggest the most suitable treatment center so that the patient can find one that suits them. The tracking unit tracks the effectiveness and progress of treatment in real time based on the treatment plan suggested by the suggestion unit. The tracking unit tracks, for example, changes in pain after the patient performs exercises or the effectiveness of treatment at the treatment center. The tracking unit can track the effectiveness and progress of treatment in real time in order to understand the progress of the patient's treatment. As a result, the lumbar pain treatment system according to this embodiment can provide an individualized treatment plan based on the patient's symptoms and lifestyle, and can track the effectiveness and progress of treatment in real time.

[0070] The data collection unit collects data on the patient's symptoms and lifestyle. Specifically, it collects data such as what movements cause the patient pain and what postures the patient often maintains. The data collection unit can utilize wearable devices and smartphone apps to record the patient's movements and postures in detail in their daily life. For example, wearable devices can monitor the patient's movements and postures in real time and collect data. This allows for an accurate understanding of what movements and postures cause the patient pain. Smartphone apps also record the movements and postures the patient performs on a daily basis and collect data. Patients can input the degree and frequency of pain through the app, which allows the data collection unit to understand the patient's symptoms in detail. Furthermore, the data collection unit also collects data on the patient's lifestyle. For example, it can collect data on the patient's sleep patterns, diet, and exercise habits to evaluate the impact of the patient's lifestyle on lower back pain. This allows the data collection unit to comprehensively understand the patient's symptoms and lifestyle and use this information to create an individualized treatment plan.

[0071] The proposal department analyzes data collected by the data collection department and proposes personalized treatment plans. Specifically, based on the collected data, it proposes the most suitable exercises, stretches, and lifestyle improvements for the patient. The proposal department uses AI to analyze data and create optimal treatment plans based on the patient's symptoms and lifestyle. For example, the AI ​​analyzes data on the patient's movements and posture to evaluate whether specific exercises or stretches are effective. It can also analyze the patient's lifestyle data and propose improvements to diet, sleep, and exercise habits. Furthermore, the proposal department can customize treatment plans according to the patient's symptoms and lifestyle. For example, if a patient experiences pain during a specific movement, it will propose exercises to improve that movement. It can also propose feasible improvements that are within the patient's lifestyle. In this way, the proposal department can provide the patient with the most suitable treatment plan and achieve effective treatment.

[0072] The instruction department provides guidance on the correct exercises based on the treatment plan proposed by the proposal department. Specifically, it uses generative AI to instruct patients on the correct way to perform the exercises. The generative AI shows the exercise steps through video and audio, supporting patients so that they can perform the exercises as instructed. For example, the generative AI monitors the patient's movements in real time and provides feedback to help maintain correct posture and movement. When the patient performs the exercises, the generative AI can show the correct steps through video and give instructions by voice. This allows patients to receive effective treatment even at home. In addition, the generative AI can record the patient's progress in the exercises and adjust the content of the exercises as needed. For example, if a patient is unable to perform a particular exercise correctly, the generative AI can suggest an alternative exercise. This allows the instruction department to support patients so that they can perform the exercises correctly and receive effective treatment.

[0073] The Treatment Center Recommendation Department proposes the most suitable treatment center based on the treatment plan proposed by the department. Specifically, it recommends treatment centers that specialize in the patient's symptoms or those located within the patient's living area. The Treatment Center Recommendation Department utilizes a database of treatment centers to select the most suitable center based on the patient's symptoms and lifestyle. For example, it can recommend the most suitable treatment center to the patient based on data such as the treatment center's area of ​​expertise, treatment record, and patient reviews. In addition, the Treatment Center Recommendation Department prioritizes recommending treatment centers within the patient's living area, making it easier for patients to find a treatment center that is convenient for them. Furthermore, the Treatment Center Recommendation Department can also provide information such as the reservation status and operating hours of the recommended treatment centers. This allows patients to find a treatment center that suits them and receive treatment smoothly.

[0074] The tracking unit tracks the effectiveness and progress of treatment in real time based on the treatment plan proposed by the suggestion unit. Specifically, it tracks changes in pain after the patient performs exercises and the effectiveness of treatment at the clinic. The tracking unit collects patient feedback and data from wearable devices to understand the progress of treatment. For example, it records whether the patient's pain has decreased after performing exercises and evaluates the effectiveness of the treatment. It can also track the effectiveness of treatment at the clinic and confirm whether the patient's symptoms are improving. Furthermore, the tracking unit can adjust the treatment plan according to the progress of treatment. For example, if no effect is observed from the treatment, it can work with the suggestion unit to create a new treatment plan. This allows the tracking unit to understand the progress of the patient's treatment in real time and continue effective treatment.

[0075] The instruction unit can use generative AI to instruct on the correct way to perform exercises. For example, the instruction unit can have the generative AI demonstrate the exercise procedure through a video, allowing the patient to perform the exercise accordingly. The instruction unit can have the generative AI generate a video demonstrating the exercise procedure, allowing the patient to perform the exercise while watching the video. The instruction unit can have the generative AI generate text demonstrating the exercise procedure, allowing the patient to perform the exercise while referring to the text. In this way, the correct way to perform exercises can be effectively instructed by using generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the instruction unit may be performed using, for example, generative AI, or not using generative AI. For example, the instruction unit can input prompts indicating the exercise procedure into the generative AI, and then instruct on how to perform the exercise using the video or text generated by the generative AI.

[0076] The clinic recommendation department can suggest the most suitable clinic based on the patient's symptoms and treatment plan. For example, it can suggest clinics specializing in the patient's symptoms or clinics located within the patient's living area. The clinic recommendation department can suggest the most suitable clinic so that the patient can find one that suits them. The clinic recommendation department may use AI or not to suggest the most suitable clinic based on the patient's symptoms and treatment plan. For example, the clinic recommendation department can suggest the most suitable clinic using an AI model that takes the patient's symptoms and treatment plan as input and outputs the most suitable clinic. This allows the clinic to suggest the most suitable clinic based on the patient's symptoms and treatment plan.

[0077] The tracking unit can monitor the progress of a patient's treatment and evaluate the effectiveness of the treatment. For example, the tracking unit can track changes in pain after a patient performs exercise or the effectiveness of treatment at a clinic. The tracking unit can track the effectiveness and progress of treatment in real time to understand the progress of the patient's treatment. The tracking unit may or may not use AI to monitor the progress of the patient's treatment and evaluate the effectiveness of the treatment. For example, the tracking unit can evaluate the effectiveness of the treatment using an AI model that takes the progress of the patient's treatment as input and outputs the effectiveness of the treatment. This allows for monitoring the progress of the patient's treatment and evaluating the effectiveness of the treatment.

[0078] The tracking unit can track changes in pain after a patient performs exercise and the effectiveness of treatment at a clinic. For example, the tracking unit can track changes in pain after a patient performs exercise. The tracking unit can track the effects of treatment after a patient receives treatment at a clinic. The tracking unit can track the effectiveness and progress of treatment in real time to understand the progress of the patient's treatment. The tracking unit may use AI or not to track changes in pain after a patient performs exercise and the effectiveness of treatment at a clinic. For example, the tracking unit can track the effectiveness of treatment by using an AI model that takes changes in pain after a patient performs exercise and the effectiveness of treatment at a clinic as input and outputs the progress of treatment. This allows tracking changes in pain after a patient performs exercise and the effectiveness of treatment at a clinic.

[0079] The tracking unit can provide appropriate feedback according to the progress of the patient's treatment. For example, the tracking unit can modify the exercise method according to the progress of the patient's treatment. The tracking unit can revise the treatment plan according to the progress of the patient's treatment. The tracking unit may or may not use AI to provide appropriate feedback according to the progress of the patient's treatment. For example, the tracking unit can provide feedback using an AI model that takes the progress of the patient's treatment as input and outputs appropriate feedback. This makes it possible to provide appropriate feedback according to the progress of the patient's treatment.

[0080] The data collection unit can estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the patient is stressed, the data collection unit can collect data at night or on weekends to capture data in a relaxed state. If the patient is relaxed, the data collection unit can collect data during daytime activities to obtain more natural data. If the patient is in a hurry, the data collection unit can collect the necessary data in a short time to reduce the burden on the patient. By adjusting the timing of data collection based on the patient's emotions, more accurate data can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can adjust the timing of data collection using an AI model that takes patient emotion data as input and outputs the timing of data collection.

[0081] The data collection unit can analyze a patient's past treatment history and select the optimal data collection method. For example, the data collection unit can analyze the types and frequency of treatments a patient has received in the past and prioritize the collection of data necessary when receiving similar treatments. If a particular treatment was effective based on the patient's past treatment history, the data collection unit can focus on collecting data related to that treatment. Based on the patient's past treatment history, the data collection unit can collect data at the timing when treatment is most likely to be effective. This allows the optimal data collection method to be selected by analyzing the patient's past treatment 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 select a data collection method using an AI model that takes the patient's past treatment history as input and outputs the optimal data collection method.

[0082] The data collection unit can filter data based on the patient's current lifestyle and activity level during data collection. For example, the data collection unit can prioritize collecting data during activities, taking into account the patient's daily activities (work, exercise, etc.). The data collection unit can adjust the timing of data collection based on the patient's daily rhythm to obtain more accurate data. The data collection unit can adjust the frequency and method of data collection according to the patient's activity level, collecting data within a reasonable range. This allows for the collection of more relevant data by filtering data based on the patient's current lifestyle and activity level. 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 perform data collection using an AI model that takes the patient's lifestyle and activity level as input and outputs filtered data collection results.

[0083] The data collection unit can estimate the patient's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the patient is stressed, the data collection unit can prioritize collecting stress-related data (heart rate, blood pressure, etc.). If the patient is relaxed, the data collection unit can prioritize collecting data related to relaxation (respiratory rate, muscle tension, etc.). If the patient is in a hurry, the data collection unit can prioritize collecting data that can be collected in a short time (body temperature, blood pressure, etc.). This allows for the priority of collecting more important data by determining the priority of data to collect based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can determine the priority of data collection using an AI model that takes patient emotion data as input and outputs the priority of data to collect.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering the patient's geographical location during data collection. For example, if the patient is in a specific area, the data collection unit can collect environmental data (temperature, humidity, etc.) for that area and use it to aid in treatment. If the patient is on the move, the data collection unit can collect environmental data of the destination and incorporate it into the treatment plan. If the patient is in a specific facility (gym, hospital, etc.), the data collection unit can collect environmental data of that facility and use it to aid in treatment. In this way, by considering the patient's geographical location, highly relevant data can be prioritized for collection. 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 perform data collection using an AI model that takes the patient's geographical location as input and outputs highly relevant data.

[0085] The data collection unit can analyze patients' social media activity and collect relevant data during data collection. For example, the data collection unit can collect health information (exercise records, dietary information, etc.) shared by patients on social media and incorporate it into treatment plans. The data collection unit can analyze emotions (stress, joy, etc.) expressed by patients on social media and use this information to aid in treatment. The data collection unit can collect relevant information from health-related accounts that patients follow on social media. This allows for the collection of relevant data by analyzing patients' 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 perform data collection using an AI model that takes patients' social media activity as input and outputs relevant data.

[0086] The suggestion unit can estimate the patient's emotions and adjust the way the treatment plan is presented based on the estimated emotions. For example, if the patient is stressed, the suggestion unit can propose a simple and easy-to-understand treatment plan. If the patient is relaxed, the suggestion unit can propose a treatment plan that includes detailed explanations. If the patient is in a hurry, the suggestion unit can propose a concise treatment plan that gets straight to the point. This allows for the provision of more effective treatment plans by adjusting the way the treatment plan is presented based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can adjust the way the treatment plan is presented using an AI model that takes patient emotion data as input and outputs the way the treatment plan is presented.

[0087] The suggestion unit can adjust the level of detail in its treatment plan proposals based on the severity of the symptoms. For example, if the symptoms are mild, the suggestion unit may suggest simple exercises or lifestyle improvements. If the symptoms are moderate, the suggestion unit may suggest a detailed exercise plan or stretching method. If the symptoms are severe, the suggestion unit may suggest specialized treatment or a visit to a medical institution. By adjusting the level of detail in the suggestions based on the severity of the symptoms, a more appropriate treatment plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can adjust the level of detail in the treatment plan using an AI model that takes the severity of the patient's symptoms as input and outputs the level of detail in the suggestions.

[0088] The proposal unit can apply different treatment algorithms depending on the patient's lifestyle when proposing a treatment plan. For example, if the patient primarily works at a desk, the proposal unit can propose a treatment plan that emphasizes posture improvement and stretching. If the patient is sedentary, the proposal unit can propose a treatment plan that includes light exercise and physical activity. If the patient leads an active lifestyle, the proposal unit can propose a treatment plan that emphasizes muscle training and flexibility improvement. By applying different treatment algorithms according to the patient's lifestyle, a more effective treatment plan can be provided. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can propose a treatment plan using an AI model that takes patient lifestyle data as input and outputs a treatment algorithm.

[0089] The suggestion unit can estimate the patient's emotions and adjust the length of the treatment plan based on the estimated emotions. For example, if the patient is stressed, the suggestion unit can suggest a short and concise treatment plan. If the patient is relaxed, the suggestion unit can suggest a longer treatment plan that includes detailed explanations. If the patient is in a hurry, the suggestion unit can suggest a short, to-the-point treatment plan. By adjusting the length of the treatment plan based on the patient's emotions, a more effective treatment plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can adjust the length of the treatment plan using an AI model that takes patient emotion data as input and outputs the length of the treatment plan.

[0090] The suggestion unit can determine the priority of treatment plans based on the timing of symptom onset. For example, if symptoms have recently appeared, the suggestion unit can suggest a plan that emphasizes early treatment. If symptoms have persisted chronically, the suggestion unit can suggest a long-term treatment plan. If symptoms occur during a specific time period, the suggestion unit can suggest a treatment plan tailored to that time period. By prioritizing suggestions based on the timing of symptom onset, a more effective treatment plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can determine the priority of treatment plans using an AI model that takes the timing of the patient's symptom onset as input and outputs the priority of suggestions.

[0091] The suggestion unit can adjust the order of treatment plan suggestions by referring to the patient's relevant medical data. For example, the suggestion unit can suggest the most effective treatment plan based on the patient's past diagnostic data. The suggestion unit can suggest treatments that should be prioritized, taking into account the patient's current health condition. The suggestion unit can refer to the patient's allergy information and suggest a treatment plan that avoids allergies. In this way, a more effective treatment plan can be provided by referring to the patient's relevant medical data. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can adjust the order of treatment plans using an AI model that takes the patient's medical data as input and outputs the order of treatment plans.

[0092] The instruction unit can estimate the patient's emotions and adjust the exercise instruction method based on the estimated emotions. For example, if the patient is feeling stressed, the instruction unit can suggest exercises that promote relaxation. If the patient is relaxed, the instruction unit can suggest exercises that improve concentration. If the patient is in a hurry, the instruction unit can suggest short, effective exercises. In this way, by adjusting the exercise instruction method based on the patient's emotions, more effective exercise instruction can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can adjust the exercise instruction method using an AI model that takes patient emotion data as input and outputs exercise instruction methods.

[0093] The instruction system can adjust the intensity of exercises based on the patient's fitness level during exercise instruction. For example, if the patient is a beginner, the instruction system can suggest a plan that starts with light exercises. If the patient is an intermediate level, the instruction system can suggest exercises of moderate intensity. If the patient is an advanced level, the instruction system can suggest high-intensity exercises. By adjusting the exercise intensity based on the patient's fitness level, more appropriate exercise instruction can be provided. Some or all of the above processing in the instruction system may be performed using AI, for example, or without AI. For example, the instruction system can adjust the exercise intensity using an AI model that takes the patient's fitness level as input and outputs the exercise intensity.

[0094] The instruction unit can suggest exercise timings according to the patient's lifestyle rhythm during exercise instruction. For example, if the patient is a morning person, the instruction unit can suggest morning exercises. If the patient is a night owl, the instruction unit can suggest evening exercises. If the patient has an irregular lifestyle rhythm, the instruction unit can suggest exercises at flexible times. This allows for more effective exercise instruction by suggesting exercise timings according to the patient's lifestyle rhythm. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can suggest exercise timings using an AI model that takes the patient's lifestyle rhythm data as input and outputs exercise timings.

[0095] The instruction system can estimate the patient's emotions and adjust the order of exercises based on those emotions. For example, if the patient is stressed, the instruction system might suggest relaxing exercises first. If the patient is relaxed, the instruction system might suggest exercises that improve concentration first. If the patient is in a hurry, the instruction system might suggest short, effective exercises first. By adjusting the order of exercises based on the patient's emotions, more effective exercise instruction can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the instruction system may be performed using AI, or not using AI. For example, the instruction system can adjust the order of exercises using an AI model that takes patient emotion data as input and outputs the order of exercises.

[0096] The instruction department can suggest the optimal exercise location when providing exercise instruction, taking into account the patient's geographical location. For example, if the patient is at home, the instruction department can suggest exercises that can be done at home. If the patient is at a gym, the instruction department can suggest exercises that utilize the gym's equipment. If the patient is in a park, the instruction department can suggest exercises that can be done in the park. In this way, the optimal exercise location can be suggested by considering the patient's geographical location. Some or all of the above processing in the instruction department may be performed using AI, for example, or without AI. For example, the instruction department can suggest an exercise location using an AI model that takes the patient's geographical location as input and outputs the optimal exercise location.

[0097] The instruction department can customize exercise instruction by referring to the patient's past exercise history. For example, the instruction department can analyze the effectiveness of exercises the patient has performed in the past and suggest effective exercises. The instruction department can avoid exercises the patient has previously struggled with and focus on exercises the patient excels at. Based on the patient's past exercise history, the instruction department can suggest exercises that are appropriate to the patient's progress. In this way, by referring to the patient's past exercise history, more effective exercise instruction can be provided. Some or all of the above processes in the instruction department may be performed using AI, for example, or not. For example, the instruction department can customize exercise instruction using an AI model that takes the patient's past exercise history as input and outputs instruction content.

[0098] The clinic recommendation department can estimate the patient's emotions and adjust its clinic recommendation method based on the estimated emotions. For example, if the patient is stressed, the clinic recommendation department can suggest a relaxing clinic. If the patient is relaxed, the clinic recommendation department can suggest a clinic that provides professional treatment. If the patient is in a hurry, the clinic recommendation department can suggest a clinic that can respond quickly. In this way, by adjusting the clinic recommendation method based on the patient's emotions, it is possible to suggest a more appropriate clinic. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the clinic recommendation department may be performed using AI, for example, or not using AI. For example, the clinic recommendation department can adjust its clinic recommendation method using an AI model that takes patient emotion data as input and outputs clinic recommendation methods.

[0099] The clinic recommendation department can select the most suitable clinic by referring to the patient's past treatment history when recommending clinics. For example, the clinic recommendation department can analyze the effectiveness of treatments the patient has received in the past and recommend clinics that offer similar treatments. If a particular treatment was effective based on the patient's past treatment history, the clinic recommendation department can recommend clinics that offer that treatment. Based on the patient's past treatment history, the clinic recommendation department can recommend clinics where treatment is likely to be effective. In this way, the most suitable clinic can be selected by referring to the patient's past treatment history. Some or all of the above processes in the clinic recommendation department may be performed using AI, for example, or without AI. For example, the clinic recommendation department can select a clinic using an AI model that takes the patient's past treatment history as input and outputs the most suitable clinic.

[0100] The clinic recommendation department can prioritize suggesting clinics located within the patient's living area. For example, it can prioritize suggesting clinics close to the patient's home, clinics close to the patient's workplace, or clinics close to places the patient frequently visits (such as gyms or schools). By prioritizing clinics within the patient's living area, it can suggest more convenient clinics. Some or all of the above processing in the clinic recommendation department may be performed using AI, or not. For example, the clinic recommendation department can suggest clinics using an AI model that takes information about the patient's living area as input and outputs the most suitable clinic.

[0101] The clinic recommendation system can estimate a patient's emotions and prioritize clinics based on those emotions. For example, if a patient is stressed, the system will prioritize clinics that offer relaxation. If a patient is relaxed, the system can prioritize clinics that provide specialized treatment. If a patient is in a hurry, the system can prioritize clinics that can respond quickly. By prioritizing clinics based on the patient's emotions, the system can recommend more appropriate clinics. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the clinic recommendation system may be performed using AI or not. For example, the clinic recommendation system can determine the priority of clinics using an AI model that takes patient emotion data as input and outputs clinic priority.

[0102] The clinic recommendation department can suggest the most suitable clinic by considering the patient's geographical location information. For example, the clinic recommendation department can prioritize suggesting clinics close to the patient's home, clinics close to the patient's workplace, or clinics close to places the patient frequently visits (such as gyms or schools). In this way, the clinic recommendation department can suggest the most suitable clinic by considering the patient's geographical location information. Some or all of the above processing in the clinic recommendation department may be performed using AI, for example, or without AI. For example, the clinic recommendation department can suggest clinics using an AI model that takes the patient's geographical location information as input and outputs the most suitable clinic.

[0103] The clinic recommendation department can analyze a patient's social media activity and suggest relevant clinics when making recommendations. For example, the clinic recommendation department can suggest clinics that the patient follows on social media. The clinic recommendation department can suggest highly-rated clinics based on reviews of clinics shared by the patient on social media. The clinic recommendation department can analyze the emotions (stress, joy, etc.) expressed by the patient on social media and suggest relevant clinics. In this way, relevant clinics can be suggested by analyzing the patient's social media activity. Some or all of the above processes in the clinic recommendation department may be performed using AI, for example, or not. For example, the clinic recommendation department can suggest clinics using an AI model that takes a patient's social media activity as input and outputs relevant clinics.

[0104] The tracking unit can estimate the patient's emotions and adjust the display method of treatment progress based on the estimated emotions. For example, if the patient is stressed, the tracking unit can provide a simple and easy-to-understand display method. If the patient is relaxed, the tracking unit can provide a display method that includes detailed information. If the patient is in a hurry, the tracking unit can provide a display method that gets straight to the point. By adjusting the display method of treatment progress based on the patient's emotions, a more effective display of treatment progress can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can adjust the display method using an AI model that takes patient emotion data as input and outputs a display method for treatment progress.

[0105] The tracking unit can predict the progress of treatment by referring to past treatment data when tracking the progress of treatment. For example, the tracking unit can predict the progress of treatment and suggest the next step based on the patient's past treatment data. The tracking unit can predict the timing when treatment is most likely to be effective based on the patient's past treatment data. The tracking unit can analyze the patient's past treatment data and display the progress of treatment in real time. This allows the tracking unit to predict the progress of treatment and suggest the next step by referring to past treatment data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can take the patient's past treatment data as input and predict the progress of treatment using an AI model that predicts the progress of treatment.

[0106] The tracking unit can apply different tracking algorithms depending on the patient's lifestyle when tracking the progress of treatment. For example, if the patient primarily does desk work, the tracking unit can apply a tracking algorithm that emphasizes posture improvement and stretching. If the patient is sedentary, the tracking unit can apply a tracking algorithm that includes light exercise and physical activity. If the patient leads an active lifestyle, the tracking unit can apply a tracking algorithm that emphasizes muscle training and flexibility improvement. By applying different tracking algorithms according to the patient's lifestyle, it is possible to provide more effective tracking of the progress of treatment. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can track the progress of treatment using an AI model that takes patient lifestyle data as input and outputs a tracking algorithm.

[0107] The tracking unit can estimate the patient's emotions and prioritize treatment progress based on the estimated emotions. For example, if the patient is stressed, the tracking unit can prioritize displaying treatment progress related to stress reduction. If the patient is relaxed, the tracking unit can display the overall treatment progress in detail. If the patient is in a hurry, the tracking unit can display important treatment progress in a concise manner. This allows for a more effective display of treatment progress by prioritizing treatment progress based on the patient's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can determine priorities using an AI model that takes patient emotion data as input and outputs a priority order for treatment progress.

[0108] The tracking unit can display the progress of treatment while considering the patient's geographical location. For example, if the patient is at home, the tracking unit can prioritize displaying the progress of treatment that can be performed at home. If the patient is at a treatment center, the tracking unit can prioritize displaying the progress of treatment at the treatment center. If the patient is on the move, the tracking unit can prioritize displaying the progress of treatment that can be performed at the destination. This allows for a more appropriate display of treatment progress by considering the patient's geographical location. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can display the progress using an AI model that takes the patient's geographical location as input and outputs the progress of treatment.

[0109] The tracking unit can evaluate the progress of treatment by referring to the patient's relevant medical data when tracking the progress of treatment. For example, the tracking unit can evaluate the progress of treatment based on the patient's past diagnostic data. The tracking unit can evaluate the progress of treatment by considering the patient's current health status. The tracking unit can refer to the patient's allergy information and evaluate the progress of treatment to avoid allergies. This allows for a more accurate evaluation of the progress of treatment by referring to the patient's relevant medical data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can use an AI model that evaluates the progress of treatment, taking the patient's medical data as input.

[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 back pain treatment system can further estimate the patient's emotions and adjust the way it proposes a treatment plan based on those emotions. For example, if the patient is stressed, it can propose a simple and easy-to-understand treatment plan. If the patient is relaxed, it can propose a treatment plan that includes detailed explanations. If the patient is in a hurry, it can propose a short, to-the-point treatment plan. This allows for the provision of a more effective treatment plan by adjusting how it is presented based on the patient's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can adjust how it presents the treatment plan using an AI model that takes patient emotion data as input and outputs a treatment plan presentation method.

[0112] The back pain treatment system can further analyze the patient's past treatment history and select the optimal data collection method. For example, it can analyze the types and frequency of treatments the patient has received in the past and prioritize the collection of data necessary when receiving similar treatments. If a particular treatment was effective based on the patient's past treatment history, data related to that treatment can be collected with emphasis. Based on the patient's past treatment history, data can be collected at times when treatment is likely to be effective. In this way, the optimal data collection method can be selected by analyzing the patient's past treatment history. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can select a data collection method using an AI model that takes the patient's past treatment history as input and outputs the optimal data collection method.

[0113] The back pain treatment system can further estimate the patient's emotions and adjust the exercise instruction method based on the estimated emotions. For example, if the patient is stressed, it can suggest exercises that have a relaxing effect. If the patient is relaxed, it can suggest exercises that improve concentration. If the patient is in a hurry, it can suggest short, effective exercises. In this way, by adjusting the exercise instruction method based on the patient's emotions, more effective exercise instruction can be provided. Emotion estimation is achieved using, 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 instruction unit may be performed using, for example, AI, or not using AI. For example, the instruction unit can adjust the exercise instruction method using an AI model that takes patient emotion data as input and outputs exercise instruction methods.

[0114] The back pain treatment system can further prioritize the collection of highly relevant data by considering the patient's geographical location. For example, if the patient is in a specific area, environmental data for that area (temperature, humidity, etc.) can be collected and used for treatment. If the patient is on the move, environmental data for the destination can be collected and reflected in the treatment plan. If the patient is in a specific facility (gym, hospital, etc.), environmental data for that facility can be collected and used for treatment. In this way, by considering the patient's geographical location, highly relevant data can be prioritized for collection. 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 perform data collection using an AI model that takes the patient's geographical location as input and outputs highly relevant data.

[0115] The back pain treatment system can further estimate the patient's emotions and adjust its clinic recommendation method based on those emotions. For example, if the patient is stressed, it can suggest a relaxing clinic. If the patient is relaxed, it can suggest a clinic that provides professional treatment. If the patient is in a hurry, it can suggest a clinic that can respond quickly. By adjusting the clinic recommendation method based on the patient's emotions, it can suggest a more appropriate clinic. Emotion estimation is achieved using, 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 clinic recommendation unit may be performed using, for example, AI, or not using AI. For example, the clinic recommendation unit can adjust its clinic recommendation method using an AI model that takes patient emotion data as input and outputs clinic recommendation methods.

[0116] The back pain treatment system can further analyze the patient's social media activity and collect relevant data. For example, it can collect health information (exercise records, diet, etc.) shared by the patient on social media and incorporate it into the treatment plan. It can analyze the emotions (stress, joy, etc.) expressed by the patient on social media and use this information to aid in treatment. It can collect relevant information from health-related accounts that the patient follows on social media. In this way, relevant data can be collected by analyzing the patient's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can collect data using an AI model that takes the patient's social media activity as input and outputs relevant data.

[0117] The back pain treatment system can further estimate the patient's emotions and adjust the display method of treatment progress based on the estimated emotions. For example, if the patient is stressed, a simple and easy-to-understand display method can be provided. If the patient is relaxed, a display method including detailed information can be provided. If the patient is in a hurry, a display method that gets straight to the point can be provided. By adjusting the display method of treatment progress based on the patient's emotions, a more effective display of treatment progress can be provided. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can adjust the display method using an AI model that takes patient emotion data as input and outputs a display method for treatment progress.

[0118] The back pain treatment system can further suggest exercise timings according to the patient's lifestyle. For example, if the patient is a morning person, morning exercises can be suggested. If the patient is a night owl, evening exercises can be suggested. If the patient has an irregular lifestyle, exercises can be suggested at flexible times. This allows for more effective exercise guidance by suggesting exercise timings according to the patient's lifestyle. Some or all of the above processing in the instruction unit may be performed using AI, for example, or without AI. For example, the instruction unit can suggest exercise timings using an AI model that takes the patient's lifestyle data as input and outputs exercise timings.

[0119] The back pain treatment system can further estimate the patient's emotions and determine the priority of data to collect based on those emotions. For example, if the patient is stressed, stress-related data (heart rate, blood pressure, etc.) can be prioritized for collection. If the patient is relaxed, data related to relaxation (respiratory rate, muscle tension, etc.) can be prioritized for collection. If the patient is in a hurry, data that can be collected quickly (body temperature, blood pressure, etc.) can be prioritized for collection. This allows for the collection of more important data by prioritizing data collection based on the patient's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can determine the priority of data collection using an AI model that takes the patient's emotional data as input and outputs the priority of the data to be collected.

[0120] The back pain treatment system can further customize the instruction content by referring to the patient's past exercise history. For example, it can analyze the effectiveness of exercises the patient has performed in the past and suggest effective exercises. It can avoid exercises the patient has previously struggled with and focus on exercises they are good at. Based on the patient's past exercise history, it can suggest exercises that are appropriate to their progress. In this way, by referring to the patient's past exercise history, more effective exercise instruction can be provided. Some or all of the above processing in the instruction unit may be performed using AI, for example, or not using AI. For example, the instruction unit can customize exercise instruction using an AI model that takes the patient's past exercise history as input and outputs instruction content.

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

[0122] Step 1: The data collection unit collects data on the patient's symptoms and lifestyle. For example, it collects data on what movements cause the patient pain and what postures the patient often maintains. The data collection unit can collect detailed data to understand the patient's symptoms and lifestyle. Step 2: The proposal unit analyzes the data collected by the data collection unit and proposes an individualized treatment plan. For example, it may suggest specific exercises, stretches, or lifestyle improvements. The proposal unit can analyze the collected data to provide the patient with the most suitable treatment plan. Step 3: The instruction team provides guidance on the correct exercises based on the treatment plan proposed by the proposal team. Generative AI is used to instruct patients on the correct way to perform the exercises. For example, the generative AI can demonstrate the exercise procedure through a video, allowing the patient to perform the exercises accordingly. The instruction team can then guide patients on the correct exercises so that they can receive effective treatment at home. Step 4: The clinic recommendation department proposes the most suitable clinic based on the treatment plan proposed by the department. For example, they may propose a clinic specializing in the patient's symptoms or a clinic located within the patient's living area. The clinic recommendation department can propose the most suitable clinic so that the patient can find one that is right for them. Step 5: The tracking unit tracks the effectiveness and progress of the treatment in real time based on the treatment plan proposed by the suggestion unit. For example, it tracks changes in pain after the patient performs exercises and the effectiveness of treatment at the clinic. The tracking unit can track the effectiveness and progress of the treatment in real time to understand the progress of the patient's treatment.

[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, proposal unit, instruction unit, treatment center proposal unit, and tracking 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 data on the patient's symptoms and lifestyle using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to propose an individualized treatment plan. The instruction unit is implemented in the specific processing unit 46A of the smart device 14 and uses generated AI to instruct on the correct way to exercise. The treatment center proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the most suitable treatment center for the patient. The tracking unit is implemented in the specific processing unit 290 of the data processing unit 12 and tracks the effectiveness and progress of treatment in real time. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[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, proposal unit, instruction unit, treatment center proposal unit, and tracking unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data on the patient's symptoms and lifestyle using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 via the control unit 46A. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and proposes an individualized treatment plan. The instruction unit is implemented, for example, by the control unit 46A of the smart glasses 214, which uses generated AI to instruct on the correct way to exercise. The treatment center proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the most suitable treatment center for the patient. The tracking unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which tracks the effectiveness and progress of treatment in real time. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[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, proposal unit, instruction unit, treatment center proposal unit, and tracking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data on the patient's symptoms and lifestyle using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data to propose an individualized treatment plan. The instruction unit is implemented in the specific processing unit 46A of the headset terminal 314 and uses generated AI to instruct on the correct way to exercise. The treatment center proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the most suitable treatment center for the patient. The tracking unit is implemented in the specific processing unit 290 of the data processing unit 12 and tracks the effectiveness and progress of treatment in real time. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[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, proposal unit, instruction unit, treatment center proposal unit, and tracking unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect data on the patient's symptoms and lifestyle, and transmits it to the data processing unit 12 via the control unit 46A. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data and proposes an individualized treatment plan. The instruction unit is implemented, for example, by the control unit 46A of the robot 414, which uses generated AI to instruct on the correct way to exercise. The treatment center proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which proposes the most suitable treatment center for the patient. The tracking unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which tracks the effectiveness and progress of treatment in real time. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[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) A data collection unit that collects data on patients' symptoms and lifestyle habits, The data collected by the aforementioned collection unit is analyzed, and a proposal unit proposes an individualized treatment plan. The instruction department provides guidance on the correct exercises based on the treatment plan proposed by the aforementioned proposal department, The treatment center proposal department proposes the most suitable treatment center based on the treatment plan proposed by the aforementioned proposal department, The system includes a tracking unit that tracks the effectiveness and progress of treatment in real time based on the treatment plan proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned leadership, Use generative AI to instruct on the correct way to exercise. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned treatment center proposal department, We will suggest the most suitable treatment center based on the patient's symptoms and treatment plan. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned tracking unit is Monitor the progress of the patient's treatment and evaluate the effectiveness of the treatment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned tracking unit is Track changes in pain levels after patients perform exercises and the effectiveness of treatment at the clinic. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned tracking unit is Provide appropriate feedback according to the patient's treatment progress. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the 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 treatment 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 data, filtering is performed based on the patient's current living situation and activity level. 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 data to collect 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 data, the system prioritizes the collection of highly relevant data, 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 During data collection, analyze patients' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, We estimate the patient's emotions and adjust the way the treatment plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When proposing a treatment plan, adjust the level of detail based on the severity of the symptoms. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When proposing a treatment plan, different treatment algorithms are applied according to the patient's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, The system estimates the patient's emotions and adjusts the length of the treatment plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When proposing a treatment plan, prioritize the suggestions based on when the symptoms started. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When proposing a treatment plan, we adjust the order of suggestions by referring to the patient's relevant medical data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned leadership, The system estimates the patient's emotions and adjusts the exercise instruction method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned leadership, During exercise instruction, adjust the intensity of the exercises based on the patient's fitness level. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned leadership, When providing exercise instruction, we suggest exercise timings that are appropriate for the patient's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned leadership, The system estimates the patient's emotions and adjusts the order of exercises based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned leadership, When providing exercise instruction, we suggest the optimal exercise location considering the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned leadership, When providing exercise instruction, the content of the instruction is customized by referring to the patient's past exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned treatment center proposal department, The system estimates the patient's emotions and adjusts the treatment plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned treatment center proposal department, When recommending treatment centers, we select the most suitable center by referring to the patient's past treatment history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned treatment center proposal department, When suggesting treatment centers, we prioritize suggesting centers located within the patient's living area. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned treatment center proposal department, The system estimates the patient's emotions and determines the priority of treatment centers based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned treatment center proposal department, When suggesting a treatment center, we take the patient's geographical location into consideration to propose the most suitable center. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned treatment center proposal department, When recommending treatment centers, we analyze the patient's social media activity and suggest relevant centers. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned tracking unit is The system estimates the patient's emotions and adjusts how treatment progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned tracking unit is When tracking the progress of treatment, past treatment data is used to predict the progression. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned tracking unit is When tracking the progress of treatment, different tracking algorithms are applied depending on the patient's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned tracking unit is The system estimates the patient's emotions and prioritizes the treatment progression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned tracking unit is When tracking the progress of treatment, the progress is displayed taking into account the patient's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned tracking unit is When tracking the progress of treatment, the patient's relevant medical data is used to assess the progress. 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. A data collection unit that collects data on patients' symptoms and lifestyle habits, The data collected by the aforementioned collection unit is analyzed, and a proposal unit proposes an individualized treatment plan. The instruction department provides guidance on the correct exercises based on the treatment plan proposed by the aforementioned proposal department, The treatment center proposal department proposes the most suitable treatment center based on the treatment plan proposed by the aforementioned proposal department, The system includes a tracking unit that tracks the effectiveness and progress of treatment in real time based on the treatment plan proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned leadership, Using generative AI to instruct on the correct way to exercise. The system according to feature 1.

3. The aforementioned treatment center proposal department, We will suggest the most suitable treatment center based on the patient's symptoms and treatment plan. The system according to feature 1.

4. The aforementioned tracking unit is Monitor the progress of the patient's treatment and evaluate the effectiveness of the treatment. The system according to feature 1.

5. The aforementioned tracking unit is Track changes in pain levels after patients perform exercises and the effectiveness of treatment at the clinic. The system according to feature 1.

6. The aforementioned tracking unit is Provide appropriate feedback according to the patient's treatment progress. The system according to feature 1.

7. The aforementioned collection unit is We estimate the patient's emotions and adjust the timing of data collection based on the estimated emotions. The system according to feature 1.

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

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