System

Abstract

An object of the system according to the embodiment is to provide a personalized treatment plan for a low back pain patient and share the progress of the treatment in real time.SOLUTION: A system includes a symptom information collection part, a treatment office recommendation part, a treatment plan generation part, a progress sharing part, and an exercise demonstration part. The symptom information collection unit collects symptoms and health information of the user. The hospital recommendation unit recommends an optimal hospital based on the information collected by the symptom information collection unit. The treatment plan generation unit generates a personalized treatment plan based on the information collected by the symptom information collection unit. The progress sharing unit shares the progress of the treatment based on the treatment plan generated by the treatment plan generation unit. The exercise demonstration unit demonstrates an exercise based on the treatment plan generated by the treatment plan generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Existing technologies do not adequately provide patients with individualized treatment plans and share treatment progress in real time, leaving room for improvement.

[0005] The system according to the embodiment aims to provide a personalized treatment plan for patients with lower back pain and share the progress of treatment in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a symptom information collection unit, a treatment clinic recommendation unit, a treatment plan generation unit, a progress sharing unit, and an exercise demonstration unit. The symptom information collection unit collects a user's symptoms and health information. The treatment clinic recommendation unit recommends the most suitable treatment clinic based on the information collected by the symptom information collection unit. The treatment plan generation unit generates an individualized treatment plan based on the information collected by the symptom information collection unit. The progress sharing unit shares the progress of treatment based on the treatment plan generated by the treatment plan generation unit. The exercise demonstration unit demonstrates exercises based on the treatment plan generated by the treatment plan generation unit. [Effects of the Invention]

[0007] The system of the embodiment provides a personalized treatment plan for patients with lower back pain and can share treatment progress in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The PainReliefAI system according to an embodiment of the present invention is a system in which AI recommends the most suitable treatment center and customized treatment plan based on the user's symptoms and health information, and the generation AI provides appropriate exercise demonstrations. As a result, the PainReliefAI system can provide individualized treatment plans and advanced treatment management for patients with lower back pain, supporting symptom improvement.

[0029] The PainReliefAI system according to the embodiment includes a symptom information collection unit, a treatment clinic recommendation unit, a treatment plan generation unit, a progress sharing unit, and an exercise demonstration unit. The symptom information collection unit collects a user's symptoms and health information. For example, when a user registers with PainReliefAI, the user first enters their symptoms and health information. The treatment clinic recommendation unit recommends the most suitable treatment clinic based on the information collected by the symptom information collection unit. For example, the treatment clinic recommendation unit selects the most suitable treatment clinic based on the user's area of ​​residence, treatment specialty, and past patient evaluations. The treatment plan generation unit generates an individualized treatment plan based on the information collected by the symptom information collection unit. For example, the treatment plan may suggest a combination of specific exercises, stretches, and treatment modalities. The progress sharing unit shares the progress of treatment based on the treatment plan generated by the treatment plan generation unit. For example, the progress sharing unit records the effects of treatment and changes in symptoms, and the AI ​​analyzes the data to provide real-time feedback. The exercise demonstration unit demonstrates exercises based on the treatment plan generated by the treatment plan generation unit. For example, an AI trainer using generative AI can use videos to show effective stretching and strength training methods for lower back pain, helping users perform exercises with the correct posture. This allows the PainReliefAI system according to the embodiment to provide individualized treatment plans and advanced treatment management for lower back pain patients, helping to improve their symptoms.

[0030] The symptom information collection unit collects real-time data from the wearable device, allowing the AI ​​to continuously monitor the user's physical condition. For example, the symptom information collection unit collects data such as heart rate, activity level, and sleep patterns in real time from the wearable device worn by the user, and the AI ​​analyzes the data to monitor the user's physical condition. The AI ​​also evaluates the user's health status based on the data collected from the wearable device and issues an alert if an abnormality is detected. For example, it detects sudden fluctuations in heart rate or abnormal activity patterns. Furthermore, the AI ​​continuously collects data from the wearable device and analyzes long-term health trends. For example, it monitors the progression and improvement of chronic symptoms and proposes appropriate treatment plans. This allows the user's physical condition to be continuously monitored.

[0031] The symptom information collection unit can track the user's lifestyle habits and daily activities and collect data to identify the cause of symptoms based on that. The symptom information collection unit, for example, provides an app that tracks the user's daily activities and collects data such as meals, exercise, and sleep. For example, it records the contents of meals and the frequency of exercise, and AI analyzes the data. It also tracks the user's lifestyle habits and analyzes the impact of specific behaviors on symptoms. For example, it evaluates the impact of sitting for long periods of time or irregular sleep on lower back pain. Furthermore, based on the tracking data, the AI ​​identifies the cause of symptoms and makes suggestions for improving the lifestyle habits. For example, it provides advice on stretching and improving posture. In this way, it is possible to track the user's lifestyle habits and daily activities and collect data to identify the cause of symptoms.

[0032] The symptom information collection unit collects the user's symptoms and health information through voice input, and the generation AI can analyze the voice data and convert it into text. The symptom information collection unit, for example, provides an interface that allows the user to input symptoms and health information through voice, and the generation AI analyzes the voice data and converts it into text. For example, voice recognition technology is used to automatically convert the user's utterances into text. Voice input can also be used to enable the user to provide information more naturally. For example, a format is adopted in which questions are asked using voice guidance and the user answers. Furthermore, the voice data is analyzed and converted into text taking into account the user's emotional state and tone. For example, emotion analysis is performed and positive expressions are preferentially converted into text. This allows information to be collected through voice input, and the generation AI to analyze the voice data and convert it into text.

[0033] The symptom information collection unit can link with other health apps and devices to centrally manage comprehensive health data. For example, the symptom information collection unit can link with other health apps and devices via API to build a system that centrally manages user health data. For example, it can collect data from fitness apps and smartwatches. Furthermore, AI can perform comprehensive health assessments based on the linked data. For example, it can integrate data from different devices to evaluate the user's overall health. Furthermore, it can provide personalized health advice based on data collected from other health apps and devices. For example, it can utilize data from food recording apps and sleep trackers. This allows it to link with other health apps and devices to centrally manage comprehensive health data.

[0034] The clinic recommendation unit can analyze the user's past treatment history and feedback and recommend the most suitable clinic. For example, the clinic recommendation unit stores the user's past treatment history in a database, and the AI ​​analyzes that data to recommend the most suitable clinic. For example, it prioritizes recommendations of treatments and clinics that have been effective in the past. It also analyzes user feedback and evaluates clinics. For example, it evaluates the quality of clinics based on the ratings and comments provided by users after treatment. Furthermore, by integrating the past treatment history and feedback, the AI ​​selects the clinic that is most suitable for the user's symptoms. For example, it recommends clinics with a strong track record of treating specific symptoms. This makes it possible to analyze the user's past treatment history and feedback and recommend the most suitable clinic.

[0035] The clinic recommendation unit uses AI to evaluate the specialty and treatment track record of clinics and select the clinic that is most suitable for the user's symptoms. For example, the clinic recommendation unit registers the specialty and treatment track record of clinics in a database, and the AI ​​analyzes that data to select the clinic that is most suitable for the user's symptoms. For example, it recommends clinics that specialize in specific treatment methods. It also evaluates the treatment track record of clinics and preferentially recommends clinics with a high success rate for the user's symptoms. For example, it selects clinics with a strong track record in treating lower back pain. Furthermore, it selects the clinic that is most suitable for the user's symptoms based on the clinic's specialty. For example, it recommends clinics with expertise in specific symptoms. This makes it possible to evaluate the specialty and treatment track record of clinics and select the clinic that is most suitable for the user's symptoms.

[0036] The clinic recommendation unit can recommend the most accessible clinic based on the user's geographical location information. The clinic recommendation unit, for example, collects the user's geographical location information and builds a system that recommends the most accessible clinic. For example, it selects a clinic close to the user's home or workplace. It also recommends clinics that are easy for the user to get to based on the geographical location information. For example, it selects a clinic with good access by public transportation. It also analyzes the user's location information in real time and recommends the most convenient clinic. For example, it selects the clinic that is the shortest distance from the user's current location. This makes it possible to recommend the most accessible clinic based on the user's geographical location information.

[0037] The treatment plan generation unit allows the generation AI to generate an individualized treatment plan based on the user's symptoms and health information, and provide it to the user. The treatment plan generation unit, for example, collects the user's symptoms and health information, and the generation AI analyzes the data to generate an individualized treatment plan. For example, it suggests specific exercises and stretches. The generation AI also suggests the optimal combination of treatments based on the user's health information. For example, it provides a treatment plan that combines physical therapy and drug therapy. Furthermore, the generation AI customizes the treatment plan according to the user's symptoms. For example, it adjusts the treatment content according to the severity and progression of the symptoms. In this way, an individualized treatment plan can be generated and provided based on the user's symptoms and health information.

[0038] The treatment plan generation unit can create an optimal treatment plan by taking into account the user's lifestyle habits and daily activities. For example, the treatment plan generation unit tracks the user's lifestyle habits and daily activities, and the generation AI creates an optimal treatment plan based on that data. For example, it provides a treatment plan that takes into account exercise habits and dietary content. It also generates a treatment plan that matches the user's lifestyle rhythm. For example, it adjusts the timing of exercise and treatment to match work and home schedules. Furthermore, based on lifestyle data, the generation AI suggests the optimal treatment for the user. For example, it provides a comprehensive treatment plan that includes stress management and sleep improvement. This allows the generation of an optimal treatment plan by taking into account the user's lifestyle habits and daily activities.

[0039] The treatment plan generation unit customizes the treatment plan to suit the user's preferences, and the generation AI can propose it. The treatment plan generation unit, for example, collects the user's preferences and tastes, and the generation AI customizes the treatment plan based on that data. For example, the user's favorite exercises and relaxation methods are incorporated into the treatment plan. The generation AI also adjusts the treatment plan based on user feedback. For example, it prioritizes the user's preferred treatments and exercises. Furthermore, the generation AI personalizes the treatment plan according to the user's preferences. For example, it incorporates an environment or music that helps the user relax into the treatment plan. This allows the generation AI to customize the treatment plan to suit the user's preferences and propose it.

[0040] The treatment plan generation unit can link with other health apps and devices to provide a comprehensive health management plan. The treatment plan generation unit, for example, links with other health apps and devices via API and integrates the user's health data to provide a comprehensive health management plan. For example, it utilizes data from fitness apps and smartwatches. Furthermore, based on the linked data, the generation AI creates a comprehensive health management plan. For example, it integrates data on diet, exercise, sleep, etc. to provide an optimal treatment plan. Furthermore, based on data collected from other health apps and devices, the generation AI proposes an optimal health management plan for the user. For example, it utilizes data from a food recording app or sleep tracker. This allows it to link with other health apps and devices to provide a comprehensive health management plan.

[0041] The progress sharing unit monitors the user's treatment progress in real time, and the generating AI can provide feedback. The progress sharing unit, for example, builds a system that monitors the user's treatment progress in real time, and the generating AI analyzes the data and provides feedback. For example, it evaluates the effectiveness of treatment and changes in symptoms in real time. The generating AI also provides appropriate feedback based on the user's treatment progress data. For example, it suggests adjustments to exercises and treatment methods according to the treatment progress. Furthermore, the treatment progress is monitored in real time, and the generating AI provides encouraging messages and advice to the user. For example, it provides positive feedback if the treatment is showing effects. This allows the user's treatment progress to be monitored in real time, and the generating AI to provide feedback.

[0042] The progress sharing unit can quantitatively evaluate the effectiveness of treatment, and the AI ​​can analyze the data and provide feedback. For example, the progress sharing unit sets indicators for quantitatively evaluating the effectiveness of treatment, and the AI ​​can analyze the data and provide feedback. For example, it evaluates changes in pain intensity and range of motion. The AI ​​can also evaluate the effectiveness of treatment based on the quantitative data and provide specific feedback to the user. For example, it can suggest adjustments to exercises or treatment methods depending on the progress of treatment. Furthermore, a system can be built that quantitatively evaluates the effectiveness of treatment, and the AI ​​can analyze the data and provide appropriate feedback to the user. For example, it can provide positive feedback if the treatment is showing signs of effectiveness. This allows the effectiveness of treatment to be quantitatively evaluated, and the AI ​​can analyze the data and provide feedback.

[0043] The progress sharing unit can visualize the treatment progress, allowing the user to intuitively understand it. The progress sharing unit, for example, provides an interface for visualizing the treatment progress, allowing the user to intuitively understand it. For example, the progress of treatment can be displayed using graphs or charts. Furthermore, the progress of treatment can be displayed using colors or icons, allowing the user to intuitively understand the effects of treatment based on the visualized treatment progress data. Furthermore, by visualizing the treatment progress, the user can intuitively understand the effects of treatment. For example, the progress of treatment can be displayed using animations or interactive graphs. In this way, the treatment progress can be visualized, allowing the user to intuitively understand it.

[0044] The exercise demonstration unit uses the generating AI to analyze the user's physical condition and suggest optimal exercises. For example, the exercise demonstration unit uses the generating AI to analyze the user's physical condition in real time and suggest optimal exercises based on that data. For example, it provides exercises that match the user's muscle strength and flexibility. The generating AI also creates an individualized exercise plan based on the user's physical condition. For example, it suggests exercises that target specific muscle groups. Furthermore, the generating AI continuously monitors the user's physical condition and dynamically adjusts the exercise plan. For example, it changes the intensity and type of exercise according to the user's progress. This allows the generating AI to analyze the user's physical condition and suggest optimal exercises.

[0045] The exercise demonstration unit allows the generation AI to provide a personalized exercise plan based on the user's exercise history. For example, the exercise demonstration unit stores the user's exercise history in a database, and the generation AI analyzes that data to provide a personalized exercise plan. For example, a new plan is created based on the effects of past exercises. The generation AI also suggests optimal exercises for the user based on the exercise history. For example, if a particular exercise is found to be effective, it may be continuously suggested. Furthermore, the generation AI analyzes the user's exercise history and dynamically adjusts the exercise plan. For example, it may change the intensity or type of exercise according to the user's progress. This allows the generation AI to provide a personalized exercise plan based on the user's exercise history.

[0046] The exercise demonstration unit allows the generating AI to provide real-time feedback on the exercise demonstration, and to instruct the user on the correct poses. For example, the exercise demonstration unit allows the generating AI to provide real-time feedback on the exercise demonstration, and to instruct the user on how to perform the correct poses. For example, it uses a camera to analyze the user's movements and show them the correct poses. It also monitors the user's exercise in real time, and the generating AI provides appropriate feedback. For example, it points out areas for correction or improvement in the pose in real time. Furthermore, the generating AI analyzes the exercise demonstration in real time and provides specific guidance to the user. For example, it provides advice and tips on how to perform the correct poses. This allows the generating AI to provide real-time feedback on the exercise demonstration, and to instruct the user on the correct poses.

[0047] The exercise demonstration unit can share exercise demonstrations with other users and promote feedback within the community. The exercise demonstration unit, for example, provides a platform on which users can share exercise demonstrations with other users and promote feedback within the community. For example, exercise videos can be shared and comments and ratings can be received from other users. A function for promoting feedback within the community is also added. For example, a function for commenting on and rating exercise demonstrations is provided. Furthermore, sharing exercise demonstrations with other users can increase motivation. For example, sharing exercise progress can receive encouragement and advice from other users. This allows exercise demonstrations to be shared with other users and promote feedback within the community.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The PainReliefAI system can also track a user's lifestyle habits and daily activities, and collect data to identify the cause of symptoms based on that. For example, an app can be provided to track a user's daily activities, collecting data on diet, exercise, sleep, etc. For example, the contents of meals and frequency of exercise can be recorded, and the AI ​​can analyze the data. The system can also track a user's lifestyle habits and analyze the impact of specific behaviors on symptoms. For example, it can evaluate the impact of long periods of sitting or irregular sleep on lower back pain. Furthermore, based on the tracking data, the AI ​​can identify the cause of symptoms and suggest lifestyle improvements. For example, it can provide advice on stretching and improving posture. This allows the system to track a user's lifestyle habits and collect data to identify the cause of symptoms.

[0050] The PainReliefAI system can also analyze a user's past treatment history and feedback to recommend the most suitable clinic. For example, the user's past treatment history is stored in a database, and the AI ​​analyzes that data to recommend the most suitable clinic. For example, it may prioritize recommendations of treatments and clinics that have been effective in the past. It can also analyze user feedback to evaluate clinics. For example, it can evaluate the quality of clinics based on the ratings and comments provided by users after treatment. Furthermore, by integrating past treatment history and feedback, the AI ​​can select the clinic that is most suitable for the user's symptoms. For example, it can recommend clinics with a strong track record of treating specific symptoms. This allows the system to analyze a user's past treatment history and feedback to recommend the most suitable clinic.

[0051] The PainReliefAI system has further enhanced its treatment plan generation function, enabling it to create optimal treatment plans that take into account the user's lifestyle and daily activities. For example, it tracks the user's lifestyle and daily activities, and the generation AI creates optimal treatment plans based on that data. For example, it provides treatment plans that take into account exercise habits and dietary content. It also generates treatment plans that fit the user's lifestyle rhythm. For example, it adjusts the timing of exercise and treatment to fit work and home schedules. Furthermore, based on lifestyle data, the generation AI suggests optimal treatment methods for the user. For example, it provides a comprehensive treatment plan that includes stress management and sleep improvement. This allows it to create optimal treatment plans that take into account the user's lifestyle and daily activities.

[0052] The PainReliefAI system has further strengthened its progress sharing function, allowing it to quantitatively evaluate the effectiveness of treatment, with the AI ​​analyzing the data and providing feedback. For example, indicators can be set to quantitatively evaluate the effectiveness of treatment, and the AI ​​can analyze the data and provide feedback. For example, changes in pain intensity and range of motion can be evaluated. The AI ​​can also evaluate the effectiveness of treatment based on quantitative data and provide specific feedback to the user. For example, it can suggest adjustments to exercises or treatment methods depending on the progress of treatment. Furthermore, a system can be built to quantitatively evaluate the effectiveness of treatment, with the AI ​​analyzing the data and providing appropriate feedback to the user. For example, positive feedback can be provided if the treatment is showing signs of effectiveness. This allows the effectiveness of treatment to be quantitatively evaluated, with the AI ​​analyzing the data and providing feedback.

[0053] The PainReliefAI system further enhances its exercise demonstration function, allowing the generation AI to analyze the user's physical condition and suggest optimal exercises. For example, the generation AI can analyze the user's physical condition in real time and suggest optimal exercises based on that data. For example, it can provide exercises tailored to the user's muscle strength and flexibility. The generation AI can also create personalized exercise plans based on the user's physical condition. For example, it can suggest exercises that target specific muscle groups. Furthermore, the generation AI can continuously monitor the user's physical condition and dynamically adjust the exercise plan. For example, it can change the intensity and type of exercise according to the user's progress. This allows the generation AI to analyze the user's physical condition and suggest optimal exercises.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The symptom information collection unit collects the user's symptoms and health information. For example, when a user registers with PainReliefAI, they first enter their symptoms and health information. Step 2: The treatment center recommendation unit recommends the most suitable treatment center based on the information collected by the symptom information collection unit. For example, it selects the most suitable treatment center by taking into consideration the user's area of ​​residence, treatment specialty, past patient evaluations, etc. Step 3: The treatment plan generator generates a personalized treatment plan based on the information collected by the symptom information collector, for example, by suggesting a combination of specific exercises, stretches, and therapies. Step 4: The progress sharing unit shares the progress of treatment based on the treatment plan generated by the treatment plan generation unit. For example, it records the effects of treatment and changes in symptoms, and the AI ​​analyzes the data to provide real-time feedback. Step 5: The exercise demonstration unit demonstrates exercises based on the treatment plan generated by the treatment plan generation unit. For example, an AI trainer using the generation AI can use videos to show effective stretching and strength training methods for lower back pain, helping the user perform the exercises in the correct poses.

[0056] (Example 2) The PainReliefAI system according to an embodiment of the present invention is a system in which AI recommends the most suitable treatment center and customized treatment plan based on the user's symptoms and health information, and the generation AI provides appropriate exercise demonstrations. As a result, the PainReliefAI system can provide individualized treatment plans and advanced treatment management for patients with lower back pain, supporting symptom improvement.

[0057] The PainReliefAI system according to the embodiment includes a symptom information collection unit, a treatment clinic recommendation unit, a treatment plan generation unit, a progress sharing unit, and an exercise demonstration unit. The symptom information collection unit collects a user's symptoms and health information. For example, when a user registers with PainReliefAI, the user first enters their symptoms and health information. The treatment clinic recommendation unit recommends the most suitable treatment clinic based on the information collected by the symptom information collection unit. For example, the treatment clinic recommendation unit selects the most suitable treatment clinic based on the user's area of ​​residence, treatment specialty, and past patient evaluations. The treatment plan generation unit generates an individualized treatment plan based on the information collected by the symptom information collection unit. For example, the treatment plan may suggest a combination of specific exercises, stretches, and treatment modalities. The progress sharing unit shares the progress of treatment based on the treatment plan generated by the treatment plan generation unit. For example, the progress sharing unit records the effects of treatment and changes in symptoms, and the AI ​​analyzes the data to provide real-time feedback. The exercise demonstration unit demonstrates exercises based on the treatment plan generated by the treatment plan generation unit. For example, an AI trainer using generative AI can use videos to show effective stretching and strength training methods for lower back pain, helping users perform exercises with the correct posture. This allows the PainReliefAI system according to the embodiment to provide individualized treatment plans and advanced treatment management for lower back pain patients, helping to improve their symptoms.

[0058] The symptom information collection unit uses generative AI to perform emotion analysis when the user enters their symptoms and health information, allowing for information collection that takes the user's emotional state into account. For example, when the user enters their symptoms or health information, the generative AI performs real-time emotion analysis to understand the user's emotional state. For example, it analyzes facial expressions and tone of voice during input and provides advice to reduce stress and anxiety. It also uses emotion analysis to consider the emotions the user has when entering information and provides an interface that elicits positive emotions. For example, it presents encouraging messages and success stories. Furthermore, it automatically adjusts the input content based on the user's emotional state to collect more accurate information. For example, if negative emotions are strong, it changes the order or content of questions. This makes it possible to collect information that takes the user's emotional state into account.

[0059] The symptom information collection unit collects real-time data from the wearable device, allowing the AI ​​to continuously monitor the user's physical condition. For example, the symptom information collection unit collects data such as heart rate, activity level, and sleep patterns in real time from the wearable device worn by the user, and the AI ​​analyzes the data to monitor the user's physical condition. The AI ​​also evaluates the user's health status based on the data collected from the wearable device and issues an alert if an abnormality is detected. For example, it detects sudden fluctuations in heart rate or abnormal activity patterns. Furthermore, the AI ​​continuously collects data from the wearable device and analyzes long-term health trends. For example, it monitors the progression and improvement of chronic symptoms and proposes appropriate treatment plans. This allows the user's physical condition to be continuously monitored.

[0060] The symptom information collection unit can track the user's lifestyle habits and daily activities and collect data to identify the cause of symptoms based on that. The symptom information collection unit, for example, provides an app that tracks the user's daily activities and collects data such as meals, exercise, and sleep. For example, it records the contents of meals and the frequency of exercise, and AI analyzes the data. It also tracks the user's lifestyle habits and analyzes the impact of specific behaviors on symptoms. For example, it evaluates the impact of sitting for long periods of time or irregular sleep on lower back pain. Furthermore, based on the tracking data, the AI ​​identifies the cause of symptoms and makes suggestions for improving the lifestyle habits. For example, it provides advice on stretching and improving posture. In this way, it is possible to track the user's lifestyle habits and daily activities and collect data to identify the cause of symptoms.

[0061] The symptom information collection unit collects the user's symptoms and health information through voice input, and the generation AI can analyze the voice data and convert it into text. The symptom information collection unit, for example, provides an interface that allows the user to input symptoms and health information through voice, and the generation AI analyzes the voice data and converts it into text. For example, voice recognition technology is used to automatically convert the user's utterances into text. Voice input can also be used to enable the user to provide information more naturally. For example, a format is adopted in which questions are asked using voice guidance and the user answers. Furthermore, the voice data is analyzed and converted into text taking into account the user's emotional state and tone. For example, emotion analysis is performed and positive expressions are preferentially converted into text. This allows information to be collected through voice input, and the generation AI to analyze the voice data and convert it into text.

[0062] The symptom information collection unit can link with other health apps and devices to centrally manage comprehensive health data. For example, the symptom information collection unit can link with other health apps and devices via API to build a system that centrally manages user health data. For example, it can collect data from fitness apps and smartwatches. Furthermore, AI can perform comprehensive health assessments based on the linked data. For example, it can integrate data from different devices to evaluate the user's overall health. Furthermore, it can provide personalized health advice based on data collected from other health apps and devices. For example, it can utilize data from food recording apps and sleep trackers. This allows it to link with other health apps and devices to centrally manage comprehensive health data.

[0063] The symptom information collection unit uses the emotion estimation function to analyze the user's emotions in real time when they are inputting information, and can provide advice to reduce stress and anxiety. The symptom information collection unit uses the emotion estimation function to analyze emotions in real time when the user is inputting symptoms or health information, for example. For example, it analyzes the user's facial expressions and voice tone using a camera or microphone. It also provides advice to reduce the user's stress and anxiety based on the emotion estimation data. For example, it suggests breathing techniques or stretches to help them relax. It also monitors the user's emotional state in real time, and displays positive messages or words of encouragement if the user's negative emotions are strong. This makes it possible to analyze the user's emotions in real time and provide advice to reduce stress and anxiety.

[0064] The clinic recommendation unit can analyze the user's past treatment history and feedback and recommend the most suitable clinic. For example, the clinic recommendation unit stores the user's past treatment history in a database, and the AI ​​analyzes that data to recommend the most suitable clinic. For example, it prioritizes recommendations of treatments and clinics that have been effective in the past. It also analyzes user feedback and evaluates clinics. For example, it evaluates the quality of clinics based on the ratings and comments provided by users after treatment. Furthermore, by integrating the past treatment history and feedback, the AI ​​selects the clinic that is most suitable for the user's symptoms. For example, it recommends clinics with a strong track record of treating specific symptoms. This makes it possible to analyze the user's past treatment history and feedback and recommend the most suitable clinic.

[0065] The clinic recommendation unit uses AI to evaluate the specialty and treatment track record of clinics and select the clinic that is most suitable for the user's symptoms. For example, the clinic recommendation unit registers the specialty and treatment track record of clinics in a database, and the AI ​​analyzes that data to select the clinic that is most suitable for the user's symptoms. For example, it recommends clinics that specialize in specific treatment methods. It also evaluates the treatment track record of clinics and preferentially recommends clinics with a high success rate for the user's symptoms. For example, it selects clinics with a strong track record in treating lower back pain. Furthermore, it selects the clinic that is most suitable for the user's symptoms based on the clinic's specialty. For example, it recommends clinics with expertise in specific symptoms. This makes it possible to evaluate the specialty and treatment track record of clinics and select the clinic that is most suitable for the user's symptoms.

[0066] The treatment clinic recommendation unit can take into account the emotional state of the user and use the emotion estimation function to recommend a treatment clinic that the user can visit with peace of mind. The treatment clinic recommendation unit, for example, analyzes the emotional state of the user in real time and recommends a treatment clinic that the user can visit with peace of mind. For example, it selects a treatment clinic that provides a relaxing environment for the user. Furthermore, it uses the emotion estimation function to analyze the emotions the user has when selecting a treatment clinic and recommends a treatment clinic that elicits positive emotions. For example, it selects a treatment clinic that will reduce the user's anxiety. Furthermore, it takes into account the emotional state of the user and evaluates the atmosphere of the treatment clinic and the response of the staff. For example, it recommends a treatment clinic that the user can visit with peace of mind. In this way, it is possible to recommend a treatment clinic that the user can visit with peace of mind, taking into account the emotional state of the user.

[0067] The clinic recommendation unit can recommend the most accessible clinic based on the user's geographical location information. The clinic recommendation unit, for example, collects the user's geographical location information and builds a system that recommends the most accessible clinic. For example, it selects a clinic close to the user's home or workplace. It also recommends clinics that are easy for the user to get to based on the geographical location information. For example, it selects a clinic with good access by public transportation. It also analyzes the user's location information in real time and recommends the most convenient clinic. For example, it selects the clinic that is the shortest distance from the user's current location. This makes it possible to recommend the most accessible clinic based on the user's geographical location information.

[0068] The clinic recommendation unit uses the emotion estimation function to analyze the emotions of the user when selecting a clinic and can recommend clinics that elicit positive emotions. The clinic recommendation unit, for example, uses the emotion estimation function to analyze the emotions of the user when selecting a clinic in real time. For example, it analyzes the user's facial expressions and tone of voice and calculates an emotion score. Furthermore, it recommends clinics that elicit positive emotions based on the user's emotion data. For example, it selects clinics with a relaxing environment and friendly staff. Furthermore, it builds a system that recommends clinics that the user can visit with peace of mind based on the emotion estimation data. For example, it selects clinics that will reduce the user's anxiety. In this way, it is possible to analyze the user's emotions and recommend clinics that elicit positive emotions.

[0069] The treatment plan generation unit allows the generation AI to generate an individualized treatment plan based on the user's symptoms and health information, and provide it to the user. The treatment plan generation unit, for example, collects the user's symptoms and health information, and the generation AI analyzes the data to generate an individualized treatment plan. For example, it suggests specific exercises and stretches. The generation AI also suggests the optimal combination of treatments based on the user's health information. For example, it provides a treatment plan that combines physical therapy and drug therapy. Furthermore, the generation AI customizes the treatment plan according to the user's symptoms. For example, it adjusts the treatment content according to the severity and progression of the symptoms. In this way, an individualized treatment plan can be generated and provided based on the user's symptoms and health information.

[0070] The treatment plan generation unit can create an optimal treatment plan by taking into account the user's lifestyle habits and daily activities. For example, the treatment plan generation unit tracks the user's lifestyle habits and daily activities, and the generation AI creates an optimal treatment plan based on that data. For example, it provides a treatment plan that takes into account exercise habits and dietary content. It also generates a treatment plan that matches the user's lifestyle rhythm. For example, it adjusts the timing of exercise and treatment to match work and home schedules. Furthermore, based on lifestyle data, the generation AI suggests the optimal treatment for the user. For example, it provides a comprehensive treatment plan that includes stress management and sleep improvement. This allows the generation of an optimal treatment plan by taking into account the user's lifestyle habits and daily activities.

[0071] The treatment plan generation unit uses the emotion estimation function to create a treatment plan that takes into account the user's emotional state, thereby increasing motivation for treatment. The treatment plan generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and customize the treatment plan based on that data. For example, it may suggest exercises that elicit positive emotions. It may also create a treatment plan that takes into account the user's emotional state to increase motivation. For example, it may incorporate encouraging messages and success stories into the treatment plan. It may also adjust the treatment plan based on the emotion estimation data to help the user feel positive about treatment. For example, it may suggest exercises and relaxation methods that match the user's preferences. This makes it possible to create a treatment plan that takes into account the user's emotional state and increases motivation for treatment.

[0072] The treatment plan generation unit customizes the treatment plan to suit the user's preferences, and the generation AI can propose it. The treatment plan generation unit, for example, collects the user's preferences and tastes, and the generation AI customizes the treatment plan based on that data. For example, the user's favorite exercises and relaxation methods are incorporated into the treatment plan. The generation AI also adjusts the treatment plan based on user feedback. For example, it prioritizes the user's preferred treatments and exercises. Furthermore, the generation AI personalizes the treatment plan according to the user's preferences. For example, it incorporates an environment or music that helps the user relax into the treatment plan. This allows the generation AI to customize the treatment plan to suit the user's preferences and propose it.

[0073] The treatment plan generation unit can link with other health apps and devices to provide a comprehensive health management plan. The treatment plan generation unit, for example, links with other health apps and devices via API and integrates the user's health data to provide a comprehensive health management plan. For example, it utilizes data from fitness apps and smartwatches. Furthermore, based on the linked data, the generation AI creates a comprehensive health management plan. For example, it integrates data on diet, exercise, sleep, etc. to provide an optimal treatment plan. Furthermore, based on data collected from other health apps and devices, the generation AI proposes an optimal health management plan for the user. For example, it utilizes data from a food recording app or sleep tracker. This allows it to link with other health apps and devices to provide a comprehensive health management plan.

[0074] The treatment plan generation unit can use the emotion estimation function to analyze the emotions the user has toward the treatment plan and provide a plan that elicits positive emotions. The treatment plan generation unit, for example, uses the emotion estimation function to analyze the emotions the user has toward the treatment plan in real time. For example, it analyzes the user's facial expressions and voice tone to calculate an emotion score. Furthermore, it provides a treatment plan that elicits positive emotions based on the user's emotion data. For example, it suggests relaxing exercises and relaxation methods. Furthermore, it adjusts the treatment plan based on the emotion estimation data so that the user can have positive emotions toward the treatment plan. For example, it suggests exercises and relaxation methods that suit the user's preferences. In this way, it is possible to provide a plan that will allow the user to have positive emotions toward the treatment plan.

[0075] The progress sharing unit monitors the user's treatment progress in real time, and the generating AI can provide feedback. The progress sharing unit, for example, builds a system that monitors the user's treatment progress in real time, and the generating AI analyzes the data and provides feedback. For example, it evaluates the effectiveness of treatment and changes in symptoms in real time. The generating AI also provides appropriate feedback based on the user's treatment progress data. For example, it suggests adjustments to exercises and treatment methods according to the treatment progress. Furthermore, the treatment progress is monitored in real time, and the generating AI provides encouraging messages and advice to the user. For example, it provides positive feedback if the treatment is showing effects. This allows the user's treatment progress to be monitored in real time, and the generating AI to provide feedback.

[0076] The progress sharing unit can quantitatively evaluate the effectiveness of treatment, and the AI ​​can analyze the data and provide feedback. For example, the progress sharing unit sets indicators for quantitatively evaluating the effectiveness of treatment, and the AI ​​can analyze the data and provide feedback. For example, it evaluates changes in pain intensity and range of motion. The AI ​​can also evaluate the effectiveness of treatment based on the quantitative data and provide specific feedback to the user. For example, it can suggest adjustments to exercises or treatment methods depending on the progress of treatment. Furthermore, a system can be built that quantitatively evaluates the effectiveness of treatment, and the AI ​​can analyze the data and provide appropriate feedback to the user. For example, it can provide positive feedback if the treatment is showing signs of effectiveness. This allows the effectiveness of treatment to be quantitatively evaluated, and the AI ​​can analyze the data and provide feedback.

[0077] The progress sharing unit uses the emotion estimation function to provide feedback that takes into account the user's emotional state, thereby maintaining motivation for treatment. The progress sharing unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide feedback based on that data. For example, it provides encouraging messages to elicit positive emotions. It also provides feedback to maintain motivation by taking the user's emotional state into account. For example, it provides positive feedback according to the progress of treatment. It also adjusts the feedback based on the emotion estimation data so that the user can have positive emotions toward treatment. For example, it provides encouraging messages or advice tailored to the user's preferences. In this way, it is possible to provide feedback that takes into account the user's emotional state and maintain motivation for treatment.

[0078] The progress sharing unit can visualize the treatment progress, allowing the user to intuitively understand it. The progress sharing unit, for example, provides an interface for visualizing the treatment progress, allowing the user to intuitively understand it. For example, the progress of treatment can be displayed using graphs or charts. Furthermore, the progress of treatment can be displayed using colors or icons, allowing the user to intuitively understand the effects of treatment based on the visualized treatment progress data. Furthermore, by visualizing the treatment progress, the user can intuitively understand the effects of treatment. For example, the progress of treatment can be displayed using animations or interactive graphs. In this way, the treatment progress can be visualized, allowing the user to intuitively understand it.

[0079] The progress sharing unit can use the emotion estimation function to analyze the emotions the user has regarding treatment progress and provide feedback that elicits positive emotions. The progress sharing unit, for example, uses the emotion estimation function to analyze the emotions the user has regarding treatment progress in real time. For example, it analyzes the user's facial expressions and voice tone to calculate an emotion score. It also provides feedback that elicits positive emotions based on the user's emotion data. For example, it suggests relaxing exercises or relaxation methods. It also adjusts the feedback based on the emotion estimation data so that the user can have positive emotions regarding treatment progress. For example, it provides encouraging messages or advice tailored to the user's preferences. In this way, it is possible to provide feedback that elicits positive emotions regarding treatment progress.

[0080] The exercise demonstration unit uses the generating AI to analyze the user's physical condition and suggest optimal exercises. For example, the exercise demonstration unit uses the generating AI to analyze the user's physical condition in real time and suggest optimal exercises based on that data. For example, it provides exercises that match the user's muscle strength and flexibility. The generating AI also creates an individualized exercise plan based on the user's physical condition. For example, it suggests exercises that target specific muscle groups. Furthermore, the generating AI continuously monitors the user's physical condition and dynamically adjusts the exercise plan. For example, it changes the intensity and type of exercise according to the user's progress. This allows the generating AI to analyze the user's physical condition and suggest optimal exercises.

[0081] The exercise demonstration unit allows the generation AI to provide a personalized exercise plan based on the user's exercise history. For example, the exercise demonstration unit stores the user's exercise history in a database, and the generation AI analyzes that data to provide a personalized exercise plan. For example, a new plan is created based on the effects of past exercises. The generation AI also suggests optimal exercises for the user based on the exercise history. For example, if a particular exercise is found to be effective, it may be continuously suggested. Furthermore, the generation AI analyzes the user's exercise history and dynamically adjusts the exercise plan. For example, it may change the intensity or type of exercise according to the user's progress. This allows the generation AI to provide a personalized exercise plan based on the user's exercise history.

[0082] The exercise demonstration unit can use the emotion estimation function to suggest exercises that take the user's emotional state into consideration, thereby increasing motivation. The exercise demonstration unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and suggest exercises based on that data. For example, it provides exercises that elicit positive emotions. It also suggests exercises that take the user's emotional state into consideration and increase motivation. For example, it incorporates encouraging messages and success stories into the exercise plan. Furthermore, it adjusts the exercise plan based on the emotion estimation data so that the user feels positive about the exercise. For example, it suggests exercises and relaxation methods that suit the user's preferences. This makes it possible to suggest exercises that take the user's emotional state into consideration and increase motivation.

[0083] The exercise demonstration unit allows the generating AI to provide real-time feedback on the exercise demonstration, and to instruct the user on the correct poses. For example, the exercise demonstration unit allows the generating AI to provide real-time feedback on the exercise demonstration, and to instruct the user on how to perform the correct poses. For example, it uses a camera to analyze the user's movements and show them the correct poses. It also monitors the user's exercise in real time, and the generating AI provides appropriate feedback. For example, it points out areas for correction or improvement in the pose in real time. Furthermore, the generating AI analyzes the exercise demonstration in real time and provides specific guidance to the user. For example, it provides advice and tips on how to perform the correct poses. This allows the generating AI to provide real-time feedback on the exercise demonstration, and to instruct the user on the correct poses.

[0084] The exercise demonstration unit can share exercise demonstrations with other users and promote feedback within the community. The exercise demonstration unit, for example, provides a platform on which users can share exercise demonstrations with other users and promote feedback within the community. For example, exercise videos can be shared and comments and ratings can be received from other users. A function for promoting feedback within the community is also added. For example, a function for commenting on and rating exercise demonstrations is provided. Furthermore, sharing exercise demonstrations with other users can increase motivation. For example, sharing exercise progress can receive encouragement and advice from other users. This allows exercise demonstrations to be shared with other users and promote feedback within the community.

[0085] The exercise demonstration unit can use the emotion estimation function to analyze the emotions the user has toward exercise and suggest exercises that will elicit positive emotions. The exercise demonstration unit, for example, uses the emotion estimation function to analyze the emotions the user has toward exercise in real time. For example, it analyzes the user's facial expressions and voice tone to calculate an emotion score. It also suggests exercises that will elicit positive emotions based on the user's emotion data. For example, it suggests exercises or relaxation methods that will help the user relax. It also adjusts the exercise plan based on the emotion estimation data so that the user will have positive emotions toward exercise. For example, it suggests exercises or relaxation methods that suit the user's preferences. This makes it possible to suggest exercises that will elicit positive emotions toward exercise.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The PainReliefAI system can also select a clinic taking into account the user's emotional state. For example, to reduce the anxiety and stress users may feel when choosing a clinic, the system uses its emotion estimation function to analyze the user's emotions in real time and recommend clinics that offer a relaxing environment. It can also select clinics that elicit positive emotions based on the user's emotional data. For example, it can prioritize recommendations of clinics with friendly staff and comfortable facilities. Furthermore, it can monitor the user's emotional state, evaluate the impact of the clinic's atmosphere and service on the user, and select the most suitable clinic. This allows the system to provide an environment where users can receive treatment with peace of mind.

[0088] The PainReliefAI system can also track a user's lifestyle habits and daily activities, and collect data to identify the cause of symptoms based on that. For example, an app can be provided to track a user's daily activities, collecting data on diet, exercise, sleep, etc. For example, the contents of meals and frequency of exercise can be recorded, and the AI ​​can analyze the data. The system can also track a user's lifestyle habits and analyze the impact of specific behaviors on symptoms. For example, it can evaluate the impact of long periods of sitting or irregular sleep on lower back pain. Furthermore, based on the tracking data, the AI ​​can identify the cause of symptoms and suggest lifestyle improvements. For example, it can provide advice on stretching and improving posture. This allows the system to track a user's lifestyle habits and collect data to identify the cause of symptoms.

[0089] The PainReliefAI system can also analyze a user's past treatment history and feedback to recommend the most suitable clinic. For example, the user's past treatment history is stored in a database, and the AI ​​analyzes that data to recommend the most suitable clinic. For example, it may prioritize recommendations of treatments and clinics that have been effective in the past. It can also analyze user feedback to evaluate clinics. For example, it can evaluate the quality of clinics based on the ratings and comments provided by users after treatment. Furthermore, by integrating past treatment history and feedback, the AI ​​can select the clinic that is most suitable for the user's symptoms. For example, it can recommend clinics with a strong track record of treating specific symptoms. This allows the system to analyze a user's past treatment history and feedback to recommend the most suitable clinic.

[0090] The PainReliefAI system further takes into account the user's emotional state and can use its emotion estimation function to recommend clinics where the user can feel safe. For example, it can analyze the user's emotional state in real time and recommend clinics where the user can feel safe. For example, it can select clinics that provide a relaxing environment for the user. It can also use its emotion estimation function to analyze the emotions the user has when choosing a clinic and recommend clinics that elicit positive emotions. For example, it can select clinics that will reduce the user's anxiety. It can also take into account the user's emotional state and evaluate the clinic's atmosphere and the staff's response. For example, it can recommend clinics where the user can feel safe. This makes it possible to recommend clinics where the user can feel safe taking into account the user's emotional state.

[0091] The PainReliefAI system has further enhanced its treatment plan generation function, enabling it to create optimal treatment plans that take into account the user's lifestyle and daily activities. For example, it tracks the user's lifestyle and daily activities, and the generation AI creates optimal treatment plans based on that data. For example, it provides treatment plans that take into account exercise habits and dietary content. It also generates treatment plans that fit the user's lifestyle rhythm. For example, it adjusts the timing of exercise and treatment to fit work and home schedules. Furthermore, based on lifestyle data, the generation AI suggests optimal treatment methods for the user. For example, it provides a comprehensive treatment plan that includes stress management and sleep improvement. This allows it to create optimal treatment plans that take into account the user's lifestyle and daily activities.

[0092] The PainReliefAI system can also use its emotion estimation function to create a treatment plan that takes the user's emotional state into account, increasing their motivation for treatment. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and customize the treatment plan based on that data. For example, exercises that elicit positive emotions can be suggested. The system can also create a treatment plan that takes the user's emotional state into account and increases their motivation. For example, encouraging messages and success stories can be incorporated into the treatment plan. Furthermore, the system can adjust the treatment plan based on the emotion estimation data to help the user feel positive about treatment. For example, it can suggest exercises and relaxation methods that suit the user's preferences. This makes it possible to create a treatment plan that takes the user's emotional state into account and increases their motivation for treatment.

[0093] The PainReliefAI system has further strengthened its progress sharing function, allowing it to quantitatively evaluate the effectiveness of treatment, with the AI ​​analyzing the data and providing feedback. For example, indicators can be set to quantitatively evaluate the effectiveness of treatment, and the AI ​​can analyze the data and provide feedback. For example, changes in pain intensity and range of motion can be evaluated. The AI ​​can also evaluate the effectiveness of treatment based on quantitative data and provide specific feedback to the user. For example, it can suggest adjustments to exercises or treatment methods depending on the progress of treatment. Furthermore, a system can be built to quantitatively evaluate the effectiveness of treatment, with the AI ​​analyzing the data and providing appropriate feedback to the user. For example, positive feedback can be provided if the treatment is showing signs of effectiveness. This allows the effectiveness of treatment to be quantitatively evaluated, with the AI ​​analyzing the data and providing feedback.

[0094] The PainReliefAI system can also use its emotion estimation function to provide feedback that takes into account the user's emotional state, thereby maintaining motivation for treatment. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and provide feedback based on that data. For example, encouraging messages can be provided to elicit positive emotions. The system can also provide feedback to maintain motivation by taking the user's emotional state into account. For example, positive feedback can be provided according to the progress of treatment. Furthermore, based on the emotion estimation data, feedback can be adjusted to help the user feel positive about treatment. For example, encouraging messages or advice tailored to the user's preferences can be provided. This allows the system to provide feedback that takes into account the user's emotional state and maintain motivation for treatment.

[0095] The PainReliefAI system further enhances its exercise demonstration function, allowing the generation AI to analyze the user's physical condition and suggest optimal exercises. For example, the generation AI can analyze the user's physical condition in real time and suggest optimal exercises based on that data. For example, it can provide exercises tailored to the user's muscle strength and flexibility. The generation AI can also create personalized exercise plans based on the user's physical condition. For example, it can suggest exercises that target specific muscle groups. Furthermore, the generation AI can continuously monitor the user's physical condition and dynamically adjust the exercise plan. For example, it can change the intensity and type of exercise according to the user's progress. This allows the generation AI to analyze the user's physical condition and suggest optimal exercises.

[0096] The PainReliefAI system can also use its emotion estimation function to suggest exercises that take the user's emotional state into account, thereby increasing motivation. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and suggest exercises based on that data. For example, it can provide exercises that elicit positive emotions. It can also suggest exercises that take the user's emotional state into account and increase motivation. For example, it can incorporate encouraging messages and success stories into the exercise plan. Furthermore, it can adjust the exercise plan based on the emotion estimation data to help the user feel positive about the exercise. For example, it can suggest exercises and relaxation methods that suit the user's preferences. This makes it possible to suggest exercises that take the user's emotional state into account and increase motivation.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The symptom information collection unit collects the user's symptoms and health information. For example, when a user registers with PainReliefAI, they first enter their symptoms and health information. Step 2: The treatment center recommendation unit recommends the most suitable treatment center based on the information collected by the symptom information collection unit. For example, it selects the most suitable treatment center by taking into consideration the user's area of ​​residence, treatment specialty, past patient evaluations, etc. Step 3: The treatment plan generator generates a personalized treatment plan based on the information collected by the symptom information collector, for example, by suggesting a combination of specific exercises, stretches, and therapies. Step 4: The progress sharing unit shares the progress of treatment based on the treatment plan generated by the treatment plan generation unit. For example, it records the effects of treatment and changes in symptoms, and the AI ​​analyzes the data to provide real-time feedback. Step 5: The exercise demonstration unit demonstrates exercises based on the treatment plan generated by the treatment plan generation unit. For example, an AI trainer using the generation AI can use videos to show effective stretching and strength training methods for lower back pain, helping the user perform the exercises in the correct poses.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0135] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0136] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0138] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0140] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0143] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0147] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a symptom information collection unit that collects symptom and health information of a user; a treatment center recommendation unit that recommends an optimal treatment center based on the information collected by the symptom information collection unit; a treatment plan generation unit that generates an individualized treatment plan based on the information collected by the symptom information collection unit; a progress sharing unit that shares the progress of treatment based on the treatment plan generated by the treatment plan generating unit; an exercise demonstrator that demonstrates exercises based on the treatment plan generated by the treatment plan generator. A system characterized by:

2. The symptom information collecting unit When users input their symptoms and health information, generative AI is used to perform emotion analysis and collect information that takes into account the user's emotional state.

2. The system of claim 1.

3. The symptom information collecting unit AI collects real-time data from wearable devices and continuously monitors the user's physical condition.

2. The system of claim 1.

4. The symptom information collecting unit Tracking users' lifestyles and daily activities to gather data that can be used to identify the cause of symptoms 2. The system of claim 1.

5. The symptom information collecting unit Users' symptoms and health information are collected through voice input, and the generative AI analyzes the voice data and converts it into text.

2. The system of claim 1.

6. The symptom information collecting unit Connect with other health apps and devices to centralize your comprehensive health data 2. The system of claim 1.

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