System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting patient information; means for receiving and analyzing the patient information; means for recommending an optimal treatment center based on the analysis; means for generating a customized treatment plan for the patient; means for sharing treatment progress in real-time; and means for providing exercise demonstrations using the generated AI.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] Patients with lower back pain have difficulty finding the right treatment center, and lack consistent treatment plans and progress monitoring. They also lack support for performing exercises at home in the correct way. As a result, patients' quality of life declines. Furthermore, treatment centers struggle to attract new patients and lack the information they need to provide appropriate treatment. [Means for solving the problem]

[0005] The present invention provides the following means:

[0006] 1. Provide a means for patients to enter their symptoms and health information.

[0007] 2. Establish a means to analyze the received patient information and recommend the most suitable treatment center based on the analysis results.

[0008] 3. Provide a means to generate customized treatment plans for patients.

[0009] 4. Provide a means to share treatment progress in real time and modify treatment plans as needed.

[0010] 5. Provide a means to provide appropriate exercise demonstrations to patients using generative AI.

[0011] This allows patients with lower back pain to easily find the appropriate treatment center and treatment plan, and allows consistent management of their treatment progress. Furthermore, they can receive support in performing exercises in the correct way at home, which improves the effectiveness of treatment and improves the quality of life of patients. It also allows treatment centers to attract more new patients and provide more effective treatment.

[0012] The "patient information input means" is an interface for patients to input their own symptoms and health information.

[0013] The "analysis means" is a function that processes input patient information and derives symptom classification and appropriate treatment.

[0014] The "recommendation method" is a function that selects and presents the most suitable treatment center for the patient based on the analysis results.

[0015] A "customized treatment plan" is a treatment policy or action plan that is individually designed based on the patient's symptoms and health information.

[0016] The "progress sharing means" is a function for collecting treatment progress information in real time and sharing it with relevant personnel.

[0017] "Generative AI" is an engine that uses artificial intelligence technology to analyze data and generate results for specific tasks.

[0018] The "exercise demonstration means" is a function that presents appropriate exercise methods to patients in the form of videos or guides.

[0019] "Platform" is a general term for infrastructure software that integrates a variety of functions and provides consistent services.

[0020] "Clinic" is a general term for medical facilities and treatment facilities that provide specialized treatment for symptoms such as lower back pain. [Brief explanation of the drawings]

[0021] [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. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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, a 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), and an APU (Accelerated Processing Unit).

[0025] 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.

[0026] 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.

[0027] 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), Bluetooth (registered trademark), etc.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0033] 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.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0041] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0042] This invention is a platform that provides individualized treatment plans and advanced treatment management for patients with lower back pain. Specific program processing and its functions will be described in detail below.

[0043] 1. Entering and analyzing patient information

[0044] Server: Generates and makes accessible to patients a patient information entry form, including details of symptoms, pain level, existing treatments, and past medical history.

[0045] Terminal: Patients access the device through a browser or app, enter their symptoms and health information, and then press the send button to send the data to the server.

[0046] Server: Passes the received patient information to the analysis module, which uses an artificial intelligence (AI) algorithm to classify symptoms and create a list of suitable treatments and treatment centers.

[0047] 2. Recommendation of the best clinic and treatment plan

[0048] Server: Based on the analysis results, the server searches the database for information on the most suitable clinics and creates a list. It then makes recommendations based on the reputation, specialty, location, etc. of each clinic.

[0049] Server: Generates a personalized treatment plan for the patient, including details on visit frequency, home exercises, and treatment modalities.

[0050] Terminal: Displays recommended clinics and customized treatment plans to patients.

[0051] User: Check the information provided and select the most suitable clinic.

[0052] 3. Real-time sharing of treatment progress

[0053] Terminal: Provides patients with an interface for recording the progress of treatment. This interface allows them to input changes in pain after treatment, their impressions, and future plans.

[0054] User: Enters the progress of treatment and sends it to the server.

[0055] Server: Receives progress data sent by patients and shares it with their doctors and trainers. Comprehensively analyzes the progress data and modifies the treatment plan as needed.

[0056] 4. Generative AI exercises

[0057] Server: Using generative AI, it designs optimal exercises based on the patient's symptoms and treatment progress. This AI takes into account the patient's feedback and progress and generates exercise demonstration videos and guides.

[0058] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[0059] User: Exercise at home while watching exercise videos. After the exercise, provide feedback on the results and impressions.

[0060] Server: Analyzes the received feedback and incorporates it into your next exercise plan.

[0061] Specific examples

[0062] Patient information entry and analysis

[0063] The user enters the symptoms of lower back pain as "Location of pain: lower back, pain intensity: 7 / 10, duration: 2 months" and presses the send button.

[0064] The terminal sends the information to the server.

[0065] The server analyzes the received information, tags it as "lower back pain" and "chronic lower back pain," and recommends appropriate treatments (e.g., acupuncture, stretching).

[0066] Recommendation of the best clinic and treatment plan

[0067] The server searches the database for nearby acupuncture clinics and fitness clubs specializing in stretching, and presents the patient with a list of clinics suitable for the patient and a customized treatment plan.

[0068] The user selects the desired clinic from the list of recommended clinics and checks the specific treatment plan.

[0069] This allows the system to provide more effective and personalized treatment to patients with lower back pain, monitor their progress in real time, and utilizes generative AI to help patients perform the correct exercises at home.

[0070] The processing flow will be explained below.

[0071] Patient information entry and analysis

[0072] Step 1:

[0073] The server generates a patient information entry form and makes it accessible to the user.

[0074] Step 2:

[0075] The device displays a form in the user's browser or app and accepts input of symptoms and health information.

[0076] Step 3:

[0077] The user enters their lower back pain symptoms (e.g., location of pain, intensity of pain, duration of pain, etc.) and existing treatment information into the form and presses the "Submit" button.

[0078] Step 4:

[0079] The device sends the data sent by the user to the server in JSON format.

[0080] Step 5:

[0081] The server receives the input data and passes it to the analysis module.

[0082] Step 6:

[0083] The server's analysis module analyzes the data, classifies the symptoms, and lists suitable treatments and treatment centers.

[0084] Recommendation of the best clinic and treatment plan

[0085] Step 1:

[0086] Based on the analysis results, the server searches the database for appropriate clinic information.

[0087] Step 2:

[0088] The server collects information such as the ratings, specialties, and locations of each clinic, and creates a list of the most suitable clinics.

[0089] Step 3:

[0090] The server generates an optimized treatment plan for the patient, including the frequency of visits and specific treatment methods.

[0091] Step 4:

[0092] The terminal displays a list of recommended clinics and treatment plans to the patient.

[0093] Step 5:

[0094] The user checks the displayed information and selects the desired clinic.

[0095] Real-time sharing of treatment progress

[0096] Step 1:

[0097] The terminal provides the user with an interface for recording the progress of treatment.

[0098] Step 2:

[0099] The user enters and submits the progress of treatment (e.g., changes in pain, impressions, next appointment, etc.).

[0100] Step 3:

[0101] The terminal transmits the progress data transmitted by the user to the server.

[0102] Step 4:

[0103] The server receives the progress data and stores it in a database.

[0104] Step 5:

[0105] The server shares the saved progress data with the patient's doctor or trainer.

[0106] Step 6:

[0107] The server analyzes the progress data and modifies the treatment plan as needed.

[0108] Step 7:

[0109] The terminal notifies the user of the revised treatment plan.

[0110] Generative AI exercises

[0111] Step 1:

[0112] The server runs a generative AI engine and receives the user's health information and progress data as input.

[0113] Step 2:

[0114] The server uses generative AI to design exercises based on the patient's symptoms and condition.

[0115] Step 3:

[0116] The server generates a demonstration video and guide text for the designed exercise.

[0117] Step 4:

[0118] The device displays a link to the exercise video and guide text to the user.

[0119] Step 5:

[0120] Users watch exercise videos and follow the instructions to perform the exercises at home.

[0121] Step 6:

[0122] The user provides feedback on the results and impressions of the exercise.

[0123] Step 7:

[0124] The terminal transmits the feedback from the user to the server.

[0125] Step 8:

[0126] The server analyzes the feedback and reflects it in your next exercise plan.

[0127] In this way, the system performs specific operations at each processing step to help patients with lower back pain receive more effective treatment.

[0128] Example 1

[0129] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0130] In current systems for treating lower back pain, patient information entry, treatment plan generation, and treatment progress management are all performed separately, with no integrated management method available. This makes it difficult to recommend the most suitable treatment center for a patient or provide an individualized treatment plan, preventing improvements in treatment effectiveness. Real-time patient progress management is also difficult, limiting the provision of home exercises and the incorporation of feedback. A system that solves these issues is needed.

[0131] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0132] In this invention, the server includes a patient information input unit, a unit for receiving and analyzing the patient information, and a unit for analyzing the patient's feedback using a generative AI model and reflecting it in the next exercise plan, which enables integrated treatment management, not only realizing personalized treatment plans and recommendations of optimal treatment facilities, but also enabling real-time treatment progress management, providing exercises at home, and reflecting feedback.

[0133] "Patient information input means" refers to a means for providing an electronic form for patients to input their symptoms and health information.

[0134] The "analysis means" is a means of classifying the patient's symptoms using an artificial intelligence (AI) algorithm based on the received patient information and listing appropriate treatments and treatment facilities.

[0135] The "treatment facility recommendation means" is a means for searching for and recommending the most suitable treatment facility based on the analysis results.

[0136] The "treatment plan generating means" is a means for generating a customized treatment plan suitable for the patient based on the analysis results.

[0137] A "progress sharing tool" is a means for sharing a patient's treatment progress in real time with their doctor or trainer.

[0138] A "generative AI model means" is a means for using a generative AI model to design appropriate exercises and provide them to a patient.

[0139] The "feedback analysis means" is a means for analyzing the results and impressions of the exercise received from the patient and reflecting them in the next exercise plan.

[0140] The present invention is a platform that provides personalized treatment plans and advanced treatment management for patients with lower back pain. Detailed embodiments of the system are described below.

[0141] Entering patient information

[0142] The server generates an electronic form for entering patient information, including details of the patient's symptoms, pain level, existing treatments, past medical history, etc. For example, it uses an HTML form and is deployed as a web application.

[0143] The terminal displays this form to the patient via a browser or application.

[0144] The user (patient) enters symptoms and health information into the form and presses the send button to send the data to the server.

[0145] Patient information analysis

[0146] The server receives the patient information sent from the terminal and passes it to the analysis module.

[0147] This analysis module uses, for example, Python scripts and machine learning models (e.g., scikit-learn) to classify a patient's symptoms using AI algorithms and shortlist suitable treatments and treatment facilities.

[0148] Specifically, based on the information received, the analysis module tags the data with terms such as "lower back pain" or "chronic lower back pain" and recommends treatments such as acupuncture and stretching.

[0149] Recommendation of clinics and treatment plans

[0150] The server then searches for and lists the most suitable treatment facilities from a database based on the analysis results. The database includes information such as the facility's reputation, specialty, and location. For example, the server queries an SQL database to obtain information on treatment facilities that meet the criteria.

[0151] The server also uses the analysis to generate a customized treatment plan for the patient, including details on visit frequency, home exercises, and treatment methods.

[0152] The terminal displays recommended treatment facilities and customized treatment plans for the patient.

[0153] The user reviews the information presented and selects the desired treatment facility.

[0154] Recording and sharing treatment progress

[0155] The device provides an interface for recording treatment progress. This interface allows patients to enter changes in pain after treatment, their impressions, and the next treatment schedule. For example, the interface can be built using a web application using React.

[0156] The user inputs the treatment progress status and sends it to the server.

[0157] The server receives the progress data and shares it with the patient's doctor or trainer via email or in-app notifications.

[0158] Generative AI exercises

[0159] The server uses generative AI to design optimal exercises based on the patient's symptoms and treatment progress. For example, it sends prompts to a generative AI model (e.g., OpenAI GPT-3) to generate exercise content.

[0160] The device displays the generated exercise video and guide to the patient, possibly using a YouTube video link to play the video and review the exercise steps.

[0161] The user performs the exercises and provides feedback on their results and impressions.

[0162] The server analyzes the received feedback and incorporates it into the next exercise plan.

[0163] Examples of prompt statements

[0164] "Generate an appropriate exercise plan for a patient with lower back pain. Pain intensity is 7 / 10, duration is 2 months."

[0165] This system provides patients with personalized treatment plans and real-time progress monitoring for their treatment. It uses generative AI to enable correct exercises to be performed at home, and its unique feature is that feedback can be reflected in the next treatment.

[0166] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0167] Step 1: Enter patient information

[0168] The server generates an electronic form for entering patient information and deploys it as a web application, including details of symptoms, pain level, existing treatments, past medical history, etc. The server then sends the generated form to the terminal.

[0169] The terminal displays this form to the patient via a browser or application.

[0170] The user (patient) enters symptoms and health information into the form and presses the send button to send the data to the server.

[0171] Input: Patient's symptoms, pain level, existing treatments, past medical history

[0172] Output: Patient information sent to the server

[0173] Step 2: Analyze patient information

[0174] The server passes the patient information received from the device to the analysis module, which uses an AI algorithm to classify the patient's symptoms and create a list of suitable treatments and treatment facilities, using Python scripts and machine learning models (e.g., scikit-learn).

[0175] Input: Patient Information

[0176] Output: Analysis results (e.g., tags such as "lower back pain" and "chronic lower back pain") and a list of recommended treatments (e.g., acupuncture, stretching)

[0177] Step 3: Recommend a treatment facility and treatment plan

[0178] Based on the analysis results, the server searches for and lists the most suitable treatment facilities from a database that includes information such as the reputation, specialty, and location of the treatment facilities. It sends a query to the SQL database to retrieve information on treatment facilities that meet the criteria.

[0179] The server also uses the analysis to generate a customized treatment plan for the patient, including details on visit frequency, home exercises, and treatment methods.

[0180] The terminal displays the list of treatment facilities and treatment plan generated by the server to the patient.

[0181] The user reviews the information presented and selects the desired treatment facility.

[0182] Input: Analysis results

[0183] Output: List of treatment facilities, customized treatment plan

[0184] Step 4: Record and share your treatment progress

[0185] The device provides an interface for recording treatment progress. This interface includes fields for recording pain changes, impressions, and next treatment schedules. The interface is built using a web application using React.

[0186] The user inputs the changes in pain after treatment, their impressions, and the next treatment schedule, and sends it to the server.

[0187] The server stores the received progress data and shares it with the patient's doctor or trainer via email or in-app notifications.

[0188] Input: Treatment progress information

[0189] Output: Saved progress data, sharing notifications with your doctor or trainer

[0190] Step 5: Generative AI provides exercises

[0191] The server uses a generative AI model to design optimal exercises based on the patient's symptoms and treatment progress. The exercise content is generated by sending prompts to the generative AI model (e.g., OpenAI GPT-3). For example, the server sends the prompt, "If the patient has pain in the lower back, please generate an exercise plan appropriate for the patient. The pain intensity is 7 / 10, and the duration is 2 months."

[0192] The device displays the generated exercise video and guide to the patient, using a YouTube video link to play the video and check the exercise steps.

[0193] The user performs the exercise and provides feedback on the results and impressions to the server.

[0194] The server analyzes the received feedback and incorporates it into your next exercise plan.

[0195] Input: Patient symptoms, treatment progress, feedback

[0196] Output: Optimal exercise plan, exercise video and guide, reflection on next exercise plan

[0197] (Application example 1)

[0198] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0199] Managing employee health, especially reducing chronic physical strain such as lower back pain, is an important issue in many industries. However, conventional systems have struggled to provide individually customized treatment plans, monitor health conditions in real time, and provide appropriate exercise plans. Furthermore, they lacked the functionality to monitor employees' movements and identify areas of strain, or to record and share exercise progress in real time. This resulted in employees not receiving optimal treatment, leading to reduced work efficiency and increased health risks.

[0200] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0201] In this invention, the server includes a patient information input means, a means for receiving and analyzing patient information, a means for recommending the most suitable treatment center based on the analysis results, a means for generating a customized treatment plan for the patient, a means for sharing treatment progress in real time, a means for providing exercise demonstrations using generative AI, a means for monitoring health status, a means for monitoring the employee's movements and identifying areas of strain, and a means for recording progress after performing the recommended exercise plan. This makes it possible to monitor the employee's health status in real time, provide individually customized treatment plans, demonstrate appropriate exercises, and record progress.

[0202] The "patient information input means" is an interface for patients to input their own symptoms and health information.

[0203] The "means for receiving and analyzing patient information" is a system for receiving input patient information and analyzing the data.

[0204] The "means for recommending the most suitable treatment center" is a system for recommending a treatment center suitable for the patient based on the analysis results.

[0205] The "means for generating a customized treatment plan" is a system that creates a treatment plan specifically for a patient based on the patient's symptoms and health condition.

[0206] "Means for sharing treatment progress in real time" is a system for sharing treatment progress and status with medical professionals in real time.

[0207] A "means for providing exercise demonstrations using generative AI" is a system for using a generative AI model to provide exercise demonstrations and guidance appropriate for a patient.

[0208] "Health monitoring means" refers to a system for constantly monitoring the health status of patients or employees and detecting abnormalities.

[0209] "Means for monitoring the employee's movements and identifying areas of stress" refers to a system for monitoring the employee's movements and identifying areas of the body that are under stress.

[0210] The "means for recording progress after carrying out a recommended exercise plan" is a system for recording the progress and effects of a provided exercise plan after carrying out the plan.

[0211] This invention is a system for managing the health of factory workers, and has the functions of providing and monitoring customized treatment plans to reduce physical strain, particularly lower back pain. This system is implemented using the following hardware and software configuration.

[0212] Hardware and software used

[0213] 1. Hardware:

[0214] Factory robots (e.g., industrial robots)

[0215] Health monitoring sensors (e.g., heart rate sensors, accelerometers)

[0216] Communication devices (e.g. smartphones, tablets)

[0217] 2. Software:

[0218] Python programming language

[0219] Robot System API

[0220] Health Management Applications

[0221] Program processing and data calculation

[0222] 1. Enter and analyze patient information:

[0223] The server generates a patient (employee) information entry form, and the employee enters their symptoms and health information, including the location of the pain, its intensity, and its duration.

[0224] The terminal transmits the input information to the server.

[0225] The server analyzes the received information and suggests appropriate treatment options, using an AI algorithm to analyze and recommend treatment options based on the employee's health status.

[0226] 2. Recommendation of the best clinic and treatment plan:

[0227] Based on the analysis results, the server searches the database for the most suitable clinic and recommends it to the employee.

[0228] The server generates a customized treatment plan for the employee, including visit frequency, home exercises, and detailed treatment regimens.

[0229] The terminal displays recommended clinics and treatment plans for the employee.

[0230] 3. Real-time sharing of treatment progress:

[0231] The terminal provides an interface for employees to record treatment progress.

[0232] The user inputs the progress of treatment through the application and sends it to the server.

[0233] The server analyzes the received progress data, shares it with the patient's doctor and trainer, and modifies the treatment plan as needed.

[0234] 4. Generative AI exercises:

[0235] The server uses generative AI to design optimal exercises based on the employee's symptoms and treatment progress. This AI generates exercise demonstration videos and guides taking into account the employee's feedback and progress.

[0236] The devices show employees exercise videos and guides.

[0237] Users watch exercise videos and perform exercises at home, then receive feedback on their results and impressions after the exercise.

[0238] The server analyzes the received feedback and incorporates it into the next exercise plan.

[0239] Specific examples

[0240] For example, if "employee_1" experiences pain in his lower back and enters the pain intensity as "7 / 10," the system will process it as follows:

[0241] The server analyzes the condition as "lower back pain" and "chronic pain" and suggests treatments including stretching.

[0242] The server recommends nearby fitness clubs specializing in stretching and generates a personalized treatment plan.

[0243] Employees can use this information to receive appropriate treatment. They can also do stretches at home while watching exercise videos, and record and share their progress using the app.

[0244] Prompt Sentence Examples

[0245] "Generate an exercise plan for an employee who is experiencing lower back pain. The pain is located in the lower back and the pain intensity is 7 / 10. Please suggest some exercises."

[0246] This allows for real-time monitoring of employee health, providing individually tailored treatment plans, demonstrating appropriate exercises and recording progress.

[0247] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0248] Step 1:

[0249] Enter and submit patient information

[0250] The terminal provides an interface for employees to input symptoms and health information through a patient information input form. For example, an employee may input "lower back pain, pain intensity 7 / 10, duration 2 months" and submit the information. The input data is sent to the server by the terminal. The input data includes details of the symptoms, pain level, existing treatments, and past medical history.

[0251] Step 2:

[0252] Receiving and analyzing patient information

[0253] The server receives patient information sent from the device. The received data is passed to the analysis module, which uses an AI algorithm to classify symptoms and create a list of suitable treatments and treatment centers. For example, the analysis might recommend "lower back pain → acupuncture and stretching." Here, the AI ​​algorithm refers to past data and existing medical knowledge to suggest the optimal treatment.

[0254] Step 3:

[0255] Generate optimal clinic and treatment plan

[0256] Based on the analysis results, the server searches the database for the most suitable clinic and recommends it to the employee. The recommendation includes each clinic's rating, specialty, location, etc. At the same time, a customized treatment plan is generated. The generated treatment plan includes details of the frequency of visits, home exercises, and treatment methods. For example, it recommends visiting the clinic twice a week and stretching daily.

[0257] Step 4:

[0258] View recommendations and treatment plans

[0259] The terminal displays the generated list of recommended clinics and the treatment plan to the employee. For example, the terminal's application screen may display a "list of nearby acupuncture clinics" and a "customized treatment plan." Employees can use this information to select the most suitable clinic and act according to the treatment plan.

[0260] Step 5:

[0261] Recording and sharing treatment progress

[0262] The device provides an interface for employees to record treatment progress. Employees enter information about changes in pain after treatment, their impressions, and future plans, and send the data to the server. The server analyzes the received progress data and shares it with the patient's doctor or trainer. It also modifies the treatment plan as needed. For example, if pain does not decrease, the treatment method is reevaluated.

[0263] Step 6:

[0264] Generative AI exercises

[0265] The server uses a generative AI model to design optimal exercises based on the employee's symptoms and treatment progress. This information includes exercise demonstration videos and procedures. For example, a "back stretch" video is generated. The generative AI model optimizes the exercises by taking into account the employee's feedback and progress. An example prompt is, "Please generate an exercise plan for an employee who is experiencing lower back pain. The pain is located in the lower back and the pain intensity is 7 / 10. Please recommend some exercises."

[0266] Step 7:

[0267] Exercise execution and feedback

[0268] The device displays the generated exercise video and guide to the employee. For example, a "back stretching" video is played on a smartphone or tablet. The employee performs the exercise and then records their results and thoughts afterwards. Post-exercise feedback is sent to the server and reflected in future exercise plans.

[0269] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0270] This invention is a platform that provides individualized treatment plans and advanced treatment management for patients with lower back pain, and also combines an emotion engine that recognizes the user's emotional state and optimizes treatment. The specific program processing and its functions are described in detail below.

[0271] 1. Entering and analyzing patient information

[0272] Server: Generates and makes accessible to users a patient information entry form, including details of symptoms, pain level, existing treatments, and past medical history.

[0273] Terminal: Patients access the device through a browser or app, enter their symptoms and health information, and then press the send button to send the data to the server.

[0274] User: Enter their back pain symptoms (e.g., location of pain, intensity of pain, duration of pain, etc.) and existing treatment information into the form and press the "Submit" button.

[0275] Terminal: Sends data sent by the user in JSON format to the server.

[0276] Server: Passes the received patient information to the analysis module, which uses an artificial intelligence (AI) algorithm to classify symptoms and create a list of suitable treatments and treatment centers.

[0277] 2. Recommendation of the best clinic and treatment plan

[0278] Server: Based on the analysis results, the server searches the database for information on the most suitable clinics and creates a list. It then makes recommendations based on the reputation, specialty, location, etc. of each clinic.

[0279] Server: Generates a personalized treatment plan for the patient, including details on visit frequency, home exercises, and treatment modalities.

[0280] Terminal: Displays recommended clinics and treatment plans to patients.

[0281] User: Check the information provided and select the most suitable clinic.

[0282] 3. Real-time sharing of treatment progress

[0283] Terminal: Provides patients with an interface for recording the progress of treatment. This interface allows them to input changes in pain after treatment, their impressions, and future plans.

[0284] User: Enters the progress of treatment and sends it to the server.

[0285] Terminal: Progress data sent by the user is sent to the server in JSON format.

[0286] Server: Receives progress data sent by patients and shares it with their doctors and trainers. Comprehensively analyzes the progress data and modifies the treatment plan as needed.

[0287] 4. Generative AI exercises

[0288] Server: Using generative AI, it designs optimal exercises based on the patient's symptoms and treatment progress. This AI takes into account the patient's feedback and progress and generates exercise demonstration videos and guides.

[0289] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[0290] User: Exercise at home while watching exercise videos. After the exercise, feedback on results and impressions is provided within the app.

[0291] Server: Analyzes the received feedback and incorporates it into your next exercise plan.

[0292] 5. Emotion recognition and reflection using the emotion engine

[0293] Server: Activates the emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using the comments and data entered by the user, as well as the user's behavioral data (e.g., input speed, input content, voice data, etc.).

[0294] Device: When the user inputs their thoughts on treatment progress and exercise, the emotion engine analyzes the input data in real time and evaluates the user's emotional state.

[0295] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend relaxing exercises.

[0296] Device: Presents users with customized treatment plans and exercises that reflect their emotional state.

[0297] Specific examples

[0298] Patient information entry and analysis

[0299] User: In the lower back pain condition input form, enter "Location of pain: lower back, pain intensity: 7 / 10, duration: 2 months" and press the submit button.

[0300] Terminal: Sends input information to the server.

[0301] Server: Analyzes the received information, tags it as "lower back pain" and "chronic lower back pain," and recommends appropriate treatments (e.g., acupuncture, stretching).

[0302] Recommendation of the best clinic and treatment plan

[0303] Server: Searches the database for nearby acupuncture clinics and fitness clubs specializing in stretching, and presents the patient with a list of clinics suitable for the patient and a customized treatment plan.

[0304] User: Select the desired clinic from the list of clinics presented.

[0305] Emotion engine recognizes and reflects emotions

[0306] User: Enters a comment about treatment progress, saying, "Today the pain has eased a little, but I'm still a little anxious," and submits it.

[0307] Terminal: Sends comments to the server.

[0308] Server: The emotion engine analyzes the comments and identifies the emotion "anxiety."

[0309] Server: Based on the analysis results, a new treatment plan is generated that includes relaxing exercises.

[0310] Terminal: Displays the new treatment plan to the user.

[0311] In this way, by combining the emotion engine, it is possible to provide customized treatment that takes into consideration the patient's emotions, thereby improving the effectiveness of treatment.

[0312] The processing flow will be explained below.

[0313] Patient information entry and analysis

[0314] Step 1:

[0315] The server generates a patient information entry form and makes it accessible to the user.

[0316] Step 2:

[0317] The device displays a form in the user's browser or app and accepts input of symptoms and health information.

[0318] Step 3:

[0319] The user enters their lower back pain symptoms (e.g., location of pain, intensity of pain, duration of pain, etc.) and existing treatment information into the form and presses the "Submit" button.

[0320] Step 4:

[0321] The device sends the data sent by the user to the server in JSON format.

[0322] Step 5:

[0323] The server receives the input data and passes it to the analysis module.

[0324] Step 6:

[0325] The server's analysis module analyzes the data, classifies the symptoms, and lists suitable treatments and treatment centers.

[0326] Recommendation of the best clinic and treatment plan

[0327] Step 1:

[0328] Based on the analysis results, the server searches the database for appropriate clinic information.

[0329] Step 2:

[0330] The server collects information such as the ratings, specialties, and locations of each clinic, and creates a list of the most suitable clinics.

[0331] Step 3:

[0332] The server generates an optimized treatment plan for the patient, including the frequency of visits and specific treatment methods.

[0333] Step 4:

[0334] The terminal displays a list of recommended clinics and treatment plans to the patient.

[0335] Step 5:

[0336] The user checks the displayed information and selects the desired clinic.

[0337] Real-time sharing of treatment progress

[0338] Step 1:

[0339] The terminal provides the user with an interface for recording the progress of treatment.

[0340] Step 2:

[0341] The user enters and submits the progress of treatment (e.g., changes in pain, impressions, next appointment, etc.).

[0342] Step 3:

[0343] The device sends the progress data sent by the user to the server in JSON format.

[0344] Step 4:

[0345] The server receives the progress data and stores it in a database.

[0346] Step 5:

[0347] The server shares the saved progress data with the patient's doctor or trainer.

[0348] Step 6:

[0349] The server analyzes the progress data and modifies the treatment plan as needed.

[0350] Step 7:

[0351] The terminal notifies the user of the revised treatment plan.

[0352] Generative AI exercises

[0353] Step 1:

[0354] The server runs a generative AI engine and receives the user's health information and progress data as input.

[0355] Step 2:

[0356] The server uses generative AI to design exercises based on the patient's symptoms and condition.

[0357] Step 3:

[0358] The server generates a demonstration video and guide text for the designed exercise.

[0359] Step 4:

[0360] The device displays a link to the exercise video and guide text to the user.

[0361] Step 5:

[0362] Users watch exercise videos and follow the instructions to perform the exercises at home.

[0363] Step 6:

[0364] The user provides feedback on the results and impressions of the exercise.

[0365] Step 7:

[0366] The terminal transmits the feedback from the user to the server.

[0367] Step 8:

[0368] The server analyzes the feedback and reflects it in your next exercise plan.

[0369] Emotion engine recognizes and reflects emotions

[0370] Step 1:

[0371] The server starts an emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using the comments and data entered by the user and the user's behavioral data (e.g., input speed, input content, voice data, etc.).

[0372] Step 2:

[0373] When the device inputs the user's treatment progress and impressions of the exercises, the emotion engine analyzes this in real time and evaluates the user's emotional state.

[0374] Step 3:

[0375] The server receives the analysis results from the emotion engine and reflects them in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend exercises that will have a relaxing effect.

[0376] Step 4:

[0377] The device displays customized treatment plans and exercises to the user that reflect their emotional state.

[0378] Specific examples

[0379] Patient information entry and analysis

[0380] Step 1:

[0381] The user inputs the symptoms of lower back pain (e.g., location of pain, intensity of pain, duration of pain) and presses the submit button.

[0382] Step 2:

[0383] The terminal sends the input information to the server.

[0384] Step 3:

[0385] The server analyzes the information it receives, tags it with "lower back pain" and "chronic lower back pain," and recommends appropriate treatments and clinics.

[0386] Recommendation of the best clinic and treatment plan

[0387] Step 1:

[0388] Based on the analysis results, the server searches the database for appropriate treatment centers and creates a list of treatment centers and a treatment plan suitable for the patient.

[0389] Step 2:

[0390] The user selects the desired clinic from the displayed list of clinics and checks the specific treatment plan.

[0391] Emotion engine recognizes and reflects emotions

[0392] Step 1:

[0393] The user enters a comment about the progress of treatment, such as "Today the pain has eased a little, but I'm still a little worried," and submits it.

[0394] Step 2:

[0395] The device sends the comment to the server.

[0396] Step 3:

[0397] The server analyzes the comments using an emotion engine and identifies the emotion of "anxiety."

[0398] Step 4:

[0399] Based on the analysis results, the server generates a new treatment plan that includes relaxing exercises.

[0400] Step 5:

[0401] The device displays the new treatment plan to the user.

[0402] By combining emotion engines in this way, it is possible to provide customized treatment that takes into account the patient's emotional state, improving the effectiveness of treatment.

[0403] Example 2

[0404] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0405] Conventional lower back pain treatment systems are lacking in providing appropriate treatment plans for individual patients' symptoms and emotional states, leaving the need for improved treatment effectiveness. Furthermore, there are issues with the lack of real-time sharing of treatment progress and the provision of generative AI-based exercises, resulting in a suboptimal treatment experience.

[0406] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0407] In this invention, the server includes a patient information input means, a means for receiving and analyzing the patient information, a means for recommending an optimal treatment facility based on the analysis results, a means for generating a treatment plan customized for the patient, a means for sharing treatment progress in real time, a means for providing exercise demonstrations using a generative AI, and a means for recognizing the patient's emotional state using an emotion analysis means and optimizing the treatment plan based thereon. This makes it possible to provide an optimal treatment plan based on the patient's individual symptoms and emotional state, thereby improving treatment effectiveness and patient satisfaction.

[0408] The "patient information input means" is a means for providing an interface for patients to input their own symptoms and health information.

[0409] The "means for receiving and analyzing the patient information" refers to a means for receiving information entered by the patient on a server and using an AI algorithm or the like to classify symptoms and recommend treatment methods based on that information.

[0410] The "means for recommending the most suitable treatment facility" is a means for searching a database for the most suitable treatment facility for the patient based on the analysis results, and listing it based on information such as rating, specialty, and location.

[0411] The "means for generating a customized treatment plan" refers to a means for generating an individually optimized treatment plan based on the patient's symptoms and analysis results.

[0412] "Means for sharing treatment progress in real time" refers to a means for transmitting a patient's treatment progress data to a server and sharing it with the attending physician or trainer in real time.

[0413] "Means for providing exercise demonstrations using generative AI" refers to means for using generative AI to create an optimal exercise plan based on the patient's symptoms and progress of treatment, and providing the plan to the patient as a video or guide.

[0414] "Means for recognizing a patient's emotional state using emotion analysis means and optimizing a treatment plan based on that" refers to means for analyzing the patient's input data and behavioral data, recognizing the patient's emotional state, and dynamically optimizing a treatment plan based on that.

[0415] This invention is a platform that provides individualized treatment plans and advanced treatment management for patients with lower back pain, and also combines an emotion engine that recognizes the user's emotional state and optimizes treatment. The specific program processing and its functions are described in detail below.

[0416] Patient information entry and analysis

[0417] Server: Generates a patient information input form that users can access. This form includes details of symptoms, pain level, existing treatments, past medical history, etc. Specifically, the form is generated using HTML and CSS, and real-time data validation and submission functionality is implemented using JavaScript.

[0418] Device: Patients access the device through a browser or app and enter their symptoms and health information. Specifically, they can use a smartphone or PC browser. They then press the send button to send the data to the server.

[0419] Server: Passes the received patient information to the analysis module. This analysis module is implemented in Python and uses artificial intelligence (AI) algorithms with libraries such as TensorFlow and PyTorch to classify symptoms. It then lists candidates for appropriate treatments and treatment facilities.

[0420] Example: A user enters "Pain location is lower back, pain intensity is 7 / 10, duration is 2 months" into a form to input their lower back pain condition, and presses the submit button. The device converts the input information into JSON format and sends it to the server. The server analyzes the received information, tags it as "lower back pain" and "chronic lower back pain," and recommends appropriate treatments (e.g., acupuncture, stretching).

[0421] Recommendation of the best treatment facility and treatment plan

[0422] Server: Based on the analysis results, the server searches the database for information on the most suitable treatment facilities and creates a list. Specifically, it uses a relational database such as MySQL or PostgreSQL. It makes recommendations based on the reputation, specialty, location, etc. of each treatment facility.

[0423] Server: Generates an optimized treatment plan for the patient. This treatment plan includes details on the frequency of visits, home exercises, and treatment modalities. It is automatically generated using a Python script based on the analysis results from the AI ​​module.

[0424] Terminal: Display recommended treatment facilities and treatment plans to patients. Specifically, the data is rendered using a front-end framework (e.g., React or Vue.js).

[0425] Example: The server searches the database for nearby acupuncture clinics and fitness clubs specializing in stretching, and presents a list of treatment facilities suitable for the patient and a customized treatment plan. The user selects the desired facility from the presented list of treatment facilities.

[0426] Real-time sharing of treatment progress

[0427] Terminal: Provides an interface for recording the progress of treatment. This interface allows users to input changes in pain after treatment, their impressions, and future plans. Specifically, data entry is facilitated using a mobile application.

[0428] User: Enters the progress of treatment and sends it to the server. For example, enters information such as "The pain has eased a little."

[0429] Server: Shares the received progress data with the patient's doctor and trainer. The data is shared using cloud storage (e.g., Amazon S3). The progress data is comprehensively analyzed and the treatment plan is revised as necessary. This analysis is performed using machine learning algorithms.

[0430] Generative AI exercises

[0431] Server: Using generative AI, the server designs optimal exercises based on the patient's symptoms and treatment progress. The AI ​​takes into account the patient's feedback and progress and generates exercise demonstration videos and guides. Specifically, it uses a video generation engine (e.g., OpenAI's DALL-E or GPT-3).

[0432] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[0433] User: Exercise at home while watching exercise videos. After the exercise, feedback on results and impressions is provided within the app.

[0434] Server: Analyzes the received feedback and incorporates it into your next exercise plan.

[0435] Emotion engine recognizes and reflects emotions

[0436] Server: Launches the emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using comments and data entered by the user and their behavioral data (e.g., input speed, input content, voice data, etc.). It uses a natural language processing (NLP) library (e.g., SpaCy, NLTK).

[0437] Device: When the user inputs their thoughts on treatment progress and exercise, the emotion engine analyzes the input data in real time and evaluates the user's emotional state.

[0438] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend relaxing exercises.

[0439] Example: A user inputs and sends a comment about their treatment progress, such as "Today the pain has eased a little, but I'm still a little anxious." The device sends the comment to the server, which uses an emotion engine to analyze the comment and identify the emotion of "anxiety." Based on the analysis results, a new treatment plan including relaxation exercises is generated and the new treatment plan is displayed to the user on the device.

[0440] In this way, by combining the emotion engine, it is possible to provide customized treatment that takes into consideration the patient's emotions, thereby improving the effectiveness of treatment.

[0441] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0442] Step 1:

[0443] Server: Generates a patient information input form and makes it accessible to users. The input is a web form using HTML and CSS, and items include details of symptoms, pain level, existing treatments, and past medical history. The output is an interface that accepts user input. Specifically, it places form elements in an HTML file and implements real-time validation functionality using JavaScript.

[0444] Step 2:

[0445] Device: The patient accesses the input form through a browser or app and enters symptoms and health information. Input involves entering information such as symptoms into each field. Output involves saving the entered data on the device. Specifically, the user enters information into text boxes and drop-down menus on the form and presses the submit button.

[0446] Step 3:

[0447] Terminal: Data entered by the user is sent to the server in JSON format. Input is the information entered in each field. Output is the data converted to JSON format and sent to the server. Specifically, form data is serialized into JSON using a JavaScript function and sent to the server via an HTTP POST request.

[0448] Step 4:

[0449] Server: Passes the received patient information to the analysis module. The input is the JSON data sent to the server. The output is the symptom classification results and a list of recommended treatments and treatment facilities. Specifically, it runs a Python script and performs analysis using AI libraries such as TensorFlow and PyTorch.

[0450] Step 5:

[0451] Server: Based on the analysis results, it searches the database for information on the most suitable treatment facilities and creates a list. The input is the analysis results by AI. The output is a list of treatment facilities that are best suited to the user. Specifically, it executes an SQL query to retrieve treatment facility information from the database and filters it based on the analysis results.

[0452] Step 6:

[0453] Server: Generates a treatment plan optimized for the patient. The input is the analysis results and treatment facility information obtained from the database. The output is a customized treatment plan. Specifically, the treatment plan is automatically generated using a Python script based on the results of the AI ​​module.

[0454] Step 7:

[0455] Terminal: Displays recommended treatment facilities and treatment plans to patients. The input is the list of treatment facilities and customized treatment plans sent from the server. The output is an interface that displays them to the user. Specific operations include rendering the data using front-end frameworks such as React and Vue.js.

[0456] Step 8:

[0457] User: Check the presented information and select the most suitable treatment facility. The input is the list of treatment facilities displayed on the terminal. The output is the selected treatment facility information. Specifically, the user selects the desired treatment facility from the list and presses the decision button.

[0458] Step 9:

[0459] Terminal: Provides an interface for recording the progress of treatment. Input includes changes in pain after treatment, impressions, and future plans. Output is the progress data entered by the user. Specific operations include displaying a form with text fields and check boxes.

[0460] Step 10:

[0461] User: Enters the progress of treatment and sends it to the server. The input is the progress information of treatment. The output is the progress data sent to the server. The specific operation is to enter the progress and press the send button.

[0462] Step 11:

[0463] Terminal: The progress data sent by the user is sent to the server in JSON format. The input is the entered progress information. The output is the JSON data sent to the server. Specifically, the input content is converted to JSON and sent to the server via an HTTP POST request.

[0464] Step 12:

[0465] Server: Shares the received progress data with the doctor or trainer. The input is the progress data sent by the user. The output is the shared progress data. Specifically, it stores the progress data in a database and shares it using a relational database management system (RDBMS).

[0466] Step 13:

[0467] Server: Uses generative AI to design optimal exercises based on the patient's symptoms and treatment progress. Input is patient feedback and progress data. Output is exercise demonstration videos and guides. Specific operations include running the generative AI algorithm to generate individual exercise plans and create videos and guidelines.

[0468] Step 14:

[0469] Device: Displays exercise videos and guides to patients. The input is the generated exercise video and guide. The output is the exercise video and guide displayed in a playable format on the display screen. The specific operation is to display the generated video and guide in a playable format on a web page or within the app.

[0470] Step 15:

[0471] User: Exercises at home while watching an exercise video, and provides feedback on the results and impressions. The input is the results and impressions of the exercise. The output is feedback data sent within the app. Specifically, the user exercises while watching the video, and after finishing, they enter and send their impressions and results.

[0472] Step 16:

[0473] Server: Analyzes the received feedback and reflects it in the next exercise plan. The input is the feedback data sent by the user. The output is the next exercise plan. Specifically, the feedback data is passed to the AI ​​module for analysis and automatically reflected in the next plan.

[0474] Step 17:

[0475] Server: Launches the emotion engine and analyzes user input data and interactions. Inputs are comments and behavioral data entered by users. The output is analyzed emotion data. Specific operations include running the emotion analysis algorithm and extracting emotions from text and behavioral data.

[0476] Step 18:

[0477] Terminal: When the user inputs their thoughts on treatment progress or exercise, the emotion engine analyzes the input data in real time and evaluates their emotional state. The input is the user's thoughts and behavioral data. The output is the evaluation result of their emotional state. Specific operations include instantly analyzing the input data and interactions and determining their emotional state.

[0478] Step 19:

[0479] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. The input is the emotional data from the emotion analysis engine. The output is an optimized treatment plan. Specifically, the emotion data is passed to the treatment plan generation module, which generates a new treatment plan.

[0480] Step 20:

[0481] Terminal: Displays to the user a customized treatment plan or exercises that reflect the user's emotional state. The input is a new treatment plan sent from the server. The output is rendering the customized treatment plan or exercises on the display screen. The specific operation is to render the new treatment plan or exercises on the display screen.

[0482] (Application example 2)

[0483] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0484] Current low back pain treatments are still lacking in providing treatment plans that take into account each patient's individual symptoms and emotional state. Even for the same symptoms, the optimal treatment and treatment center may differ depending on the patient's emotions and daily life. There is also a lack of a system for sharing treatment progress in real time and providing individually customized meal plans. Therefore, there is a need for a system that can provide optimal treatment for each patient and maximize its effectiveness.

[0485] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a patient information input means, a means for receiving and analyzing patient information, a means for recommending an optimal treatment center based on the analysis results, a means for generating a treatment plan customized for the patient, a means for sharing treatment progress in real time, a means for providing exercise demonstrations using a generation AI, a means for analyzing the user's emotional state using an emotion engine and reflecting the analysis in the treatment plan, and a means for generating an optimal meal plan based on the user's emotional state and providing the meal in cooperation with a delivery service. This makes it possible to provide an optimal treatment plan that takes into account the symptoms and emotional state of each patient, share treatment progress in real time, and provide individually customized meal plans.

[0486] The "patient information input means" is an interface for patients to input their own symptoms and health information.

[0487] The "means for receiving and analyzing" is a module that receives input patient information and analyzes it using AI or other methods.

[0488] "Means for recommending the most suitable treatment center" is a function that suggests a treatment center that is suitable for the patient based on the analysis results.

[0489] A "means for generating a customized treatment plan" is a system that creates a treatment plan tailored to the symptoms and conditions of each individual patient.

[0490] "Means for sharing treatment progress in real time" is a function that allows patients and healthcare providers to check the progress of treatment in real time.

[0491] The "means for providing exercise demonstrations using generative AI" is a system that uses AI to provide patients with optimal exercise procedures and videos.

[0492] "Means of analyzing the user's emotional state using an emotion engine and reflecting it in the treatment plan" is a function that analyzes emotions based on the user's input data and behavior, and takes this into consideration and reflects it in the treatment plan.

[0493] "Means for generating optimal meal plans and providing meals in cooperation with delivery services" refers to a system that creates meal menus tailored to the user's health and emotional state and provides those meals in cooperation with delivery services.

[0494] This invention is a system including a patient information input means, a receiving and analyzing means, a recommending optimal treatment center, a generating a customized treatment plan, a sharing means for sharing treatment progress in real time, a providing exercise demonstrations using a generative AI, a means for analyzing a user's emotional state using an emotion engine and reflecting it in a treatment plan, and a generating an optimal meal plan and providing meals in cooperation with a delivery service.

[0495] Patient information entry and analysis

[0496] Server: Generates and makes accessible to users a patient information entry form, including details of symptoms, pain level, existing treatments, and past medical history.

[0497] Terminal: Patients access the terminal through a browser or app, enter their health information, and then press the send button to send the data to the server.

[0498] Server: Passes the received patient information to the analysis module, which uses an artificial intelligence (AI) algorithm to classify symptoms and create a list of suitable treatments and treatment centers.

[0499] Recommendation of the best clinic and treatment plan

[0500] Server: Based on the analysis results, the server searches the database for information on the most suitable clinics and creates a list. It then makes recommendations based on the reputation, specialty, location, etc. of each clinic.

[0501] Server: Generates a personalized treatment plan for the patient, including details on visit frequency, home exercises, and treatment modalities.

[0502] Terminal: Displays recommended clinics and treatment plans to patients.

[0503] Real-time sharing of treatment progress

[0504] Terminal: Provides patients with an interface for recording the progress of treatment. This interface allows them to input changes in pain after treatment, their impressions, and future plans.

[0505] User: Enters the progress of treatment and sends it to the server.

[0506] Server: Receives progress data sent by patients and shares it with their doctors and trainers. Comprehensively analyzes the progress data and modifies the treatment plan as needed.

[0507] Generative AI exercises

[0508] Server: Using generative AI, it designs optimal exercises based on the patient's symptoms and treatment progress. This AI takes into account the patient's feedback and progress and generates exercise videos and guides.

[0509] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[0510] User: Exercise at home while watching exercise videos. After the exercise, feedback on results and impressions is provided within the app.

[0511] Emotion engine recognizes and reflects emotions

[0512] Server: Activates the emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using the comments and data entered by the user, as well as the user's behavioral data (e.g., input speed, input content, voice data, etc.).

[0513] Device: When the user inputs their thoughts on treatment progress and exercise, the emotion engine analyzes the input data in real time and evaluates the user's emotional state.

[0514] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend relaxing exercises.

[0515] Meal plan generation and delivery service

[0516] Server: Using an emotion engine and generative AI, it generates an optimal meal plan based on the user's emotional state and symptoms. This plan takes into account nutritional balance and relaxation effects.

[0517] Device: Provides an interface where users can view meal plans within the app and order delivery with one tap.

[0518] Users: After receiving their meal, they provide feedback in the app to be reflected in their next meal plan.

[0519] Specific examples

[0520] Example of input

[0521] User: "I'm feeling a bit tired today and would like a relaxing meal."

[0522] Server: Analyzes the input information, and the emotion engine identifies "feeling tired" and "desire to relax."

[0523] Generative AI model prompt: "The user is tired and wants to relax. Please suggest recipes using ingredients that have a relaxing effect."

[0524] Results: The AI ​​suggests relaxing meals such as "chicken soup with lemongrass" and "lavender tea."

[0525] In this way, the entire system works together to provide personalized treatments and meal plans based on the user's health and emotional state.

[0526] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0527] Step 1:

[0528] The user opens the smartphone app and enters symptoms and health information into the patient information input form.

[0529] The data entered includes the location of the pain, the intensity of the pain, and the duration of the pain.

[0530] The terminal sends this input data to the server in JSON format.

[0531] Step 2:

[0532] The server passes the received JSON data to the analysis module.

[0533] This module uses AI algorithms such as TensorFlow to analyze and classify patient symptoms.

[0534] As a result of the analysis, tags such as "lower back pain" and "chronic lower back pain" are assigned.

[0535] Step 3:

[0536] The server searches the database based on the tagged analysis results and generates a list of suitable clinics.

[0537] The list includes information such as each clinic's rating, specialty, treatment features, and location.

[0538] This information is transmitted to the user terminal.

[0539] Step 4:

[0540] The terminal displays the customized treatment plan to the user along with a list of recommended clinics.

[0541] The treatment plan includes details of how often to visit, exercises to do at home, and treatment options.

[0542] The user selects the desired clinic from this list.

[0543] Step 5:

[0544] The app records the user's progress as they visit a clinic or do exercises at home and sends the information to a server.

[0545] The information entered includes changes in pain after treatment, impressions, and future plans.

[0546] The device sends this progress data to the server in JSON format.

[0547] Step 6:

[0548] The server receives the progress data and shares it with the patient's doctor or trainer.

[0549] Comprehensive analysis of progress data will determine whether the treatment plan is working or if adjustments are needed.

[0550] Modify the treatment plan as needed.

[0551] Step 7:

[0552] The server uses generative AI to design optimal exercises based on progress data and treatment status.

[0553] Exercise demonstration videos and guides are generated and sent to the user's device.

[0554] Step 8:

[0555] Users can exercise at home by watching exercise videos and guides.

[0556] After the exercise, feedback on results and impressions is provided within the app, and the data is sent to the server.

[0557] Step 9:

[0558] The server runs the emotion engine and analyzes the user's input data and interactions.

[0559] The system recognizes the user's emotional state (e.g., "stressed" or "relaxed") from comments, input data, voice data, etc.

[0560] Emotional data is also reflected in optimizing treatment plans.

[0561] Step 10:

[0562] The server and generating AI generate an optimal meal plan based on the user's emotional state.

[0563] For example, if you feel tired and want to relax, we will suggest a meal plan using ingredients that have a relaxing effect.

[0564] Step 11:

[0565] The device displays a customized meal plan to the user and allows them to order delivery with just one tap.

[0566] After receiving their meal, users provide feedback within the app and send the data to be reflected in their next plan.

[0567] By following these steps, the entire system works together to provide optimal treatment and meal plans that take into account each user's individual symptoms and emotional state.

[0568] 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.

[0569] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0570] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0571] [Second embodiment]

[0572] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0573] 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.

[0574] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0575] 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.

[0576] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0577] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0578] 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.

[0579] 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.

[0580] 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 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.

[0581] 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.

[0582] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0583] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0584] This invention is a platform that provides individualized treatment plans and advanced treatment management for patients with lower back pain. Specific program processing and its functions will be described in detail below.

[0585] 1. Entering and analyzing patient information

[0586] Server: Generates and makes accessible to patients a patient information entry form, including details of symptoms, pain level, existing treatments, and past medical history.

[0587] Terminal: Patients access the device through a browser or app, enter their symptoms and health information, and then press the send button to send the data to the server.

[0588] Server: Passes the received patient information to the analysis module, which uses an artificial intelligence (AI) algorithm to classify symptoms and create a list of suitable treatments and treatment centers.

[0589] 2. Recommendation of the best clinic and treatment plan

[0590] Server: Based on the analysis results, the server searches the database for information on the most suitable clinics and creates a list. It then makes recommendations based on the reputation, specialty, location, etc. of each clinic.

[0591] Server: Generates a personalized treatment plan for the patient, including details on visit frequency, home exercises, and treatment modalities.

[0592] Terminal: Displays recommended clinics and customized treatment plans to patients.

[0593] User: Check the information provided and select the most suitable clinic.

[0594] 3. Real-time sharing of treatment progress

[0595] Terminal: Provides patients with an interface for recording the progress of treatment. This interface allows them to input changes in pain after treatment, their impressions, and future plans.

[0596] User: Enters the progress of treatment and sends it to the server.

[0597] Server: Receives progress data sent by patients and shares it with their doctors and trainers. Comprehensively analyzes the progress data and modifies the treatment plan as needed.

[0598] 4. Generative AI exercises

[0599] Server: Using generative AI, it designs optimal exercises based on the patient's symptoms and treatment progress. This AI takes into account the patient's feedback and progress and generates exercise demonstration videos and guides.

[0600] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[0601] User: Exercise at home while watching exercise videos. After the exercise, provide feedback on the results and impressions.

[0602] Server: Analyzes the received feedback and incorporates it into your next exercise plan.

[0603] Specific examples

[0604] Patient information entry and analysis

[0605] The user enters the symptoms of lower back pain as "Location of pain: lower back, pain intensity: 7 / 10, duration: 2 months" and presses the send button.

[0606] The terminal sends the information to the server.

[0607] The server analyzes the received information, tags it as "lower back pain" and "chronic lower back pain," and recommends appropriate treatments (e.g., acupuncture, stretching).

[0608] Recommendation of the best clinic and treatment plan

[0609] The server searches the database for nearby acupuncture clinics and fitness clubs specializing in stretching, and presents the patient with a list of clinics suitable for the patient and a customized treatment plan.

[0610] The user selects the desired clinic from the list of recommended clinics and checks the specific treatment plan.

[0611] This allows the system to provide more effective and personalized treatment to patients with lower back pain, monitor their progress in real time, and utilizes generative AI to help patients perform the correct exercises at home.

[0612] The processing flow will be explained below.

[0613] Patient information entry and analysis

[0614] Step 1:

[0615] The server generates a patient information entry form and makes it accessible to the user.

[0616] Step 2:

[0617] The device displays a form in the user's browser or app and accepts input of symptoms and health information.

[0618] Step 3:

[0619] The user enters their lower back pain symptoms (e.g., location of pain, intensity of pain, duration of pain, etc.) and existing treatment information into the form and presses the "Submit" button.

[0620] Step 4:

[0621] The device sends the data sent by the user to the server in JSON format.

[0622] Step 5:

[0623] The server receives the input data and passes it to the analysis module.

[0624] Step 6:

[0625] The server's analysis module analyzes the data, classifies the symptoms, and lists suitable treatments and treatment centers.

[0626] Recommendation of the best clinic and treatment plan

[0627] Step 1:

[0628] Based on the analysis results, the server searches the database for appropriate clinic information.

[0629] Step 2:

[0630] The server collects information such as the ratings, specialties, and locations of each clinic, and creates a list of the most suitable clinics.

[0631] Step 3:

[0632] The server generates an optimized treatment plan for the patient, including the frequency of visits and specific treatment methods.

[0633] Step 4:

[0634] The terminal displays a list of recommended clinics and treatment plans to the patient.

[0635] Step 5:

[0636] The user checks the displayed information and selects the desired clinic.

[0637] Real-time sharing of treatment progress

[0638] Step 1:

[0639] The terminal provides the user with an interface for recording the progress of treatment.

[0640] Step 2:

[0641] The user enters and submits the progress of treatment (e.g., changes in pain, impressions, next appointment, etc.).

[0642] Step 3:

[0643] The terminal transmits the progress data transmitted by the user to the server.

[0644] Step 4:

[0645] The server receives the progress data and stores it in a database.

[0646] Step 5:

[0647] The server shares the saved progress data with the patient's doctor or trainer.

[0648] Step 6:

[0649] The server analyzes the progress data and modifies the treatment plan as needed.

[0650] Step 7:

[0651] The terminal notifies the user of the revised treatment plan.

[0652] Generative AI exercises

[0653] Step 1:

[0654] The server runs a generative AI engine and receives the user's health information and progress data as input.

[0655] Step 2:

[0656] The server uses generative AI to design exercises based on the patient's symptoms and condition.

[0657] Step 3:

[0658] The server generates a demonstration video and guide text for the designed exercise.

[0659] Step 4:

[0660] The device displays a link to the exercise video and guide text to the user.

[0661] Step 5:

[0662] Users watch exercise videos and follow the instructions to perform the exercises at home.

[0663] Step 6:

[0664] The user provides feedback on the results and impressions of the exercise.

[0665] Step 7:

[0666] The terminal transmits the feedback from the user to the server.

[0667] Step 8:

[0668] The server analyzes the feedback and reflects it in your next exercise plan.

[0669] In this way, the system performs specific operations at each processing step to help patients with lower back pain receive more effective treatment.

[0670] Example 1

[0671] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0672] In current systems for treating lower back pain, patient information entry, treatment plan generation, and treatment progress management are all performed separately, with no integrated management method available. This makes it difficult to recommend the most suitable treatment center for a patient or provide an individualized treatment plan, preventing improvements in treatment effectiveness. Real-time patient progress management is also difficult, limiting the provision of home exercises and the incorporation of feedback. A system that solves these issues is needed.

[0673] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0674] In this invention, the server includes a patient information input unit, a unit for receiving and analyzing the patient information, and a unit for analyzing the patient's feedback using a generative AI model and reflecting it in the next exercise plan, which enables integrated treatment management, not only realizing personalized treatment plans and recommendations of optimal treatment facilities, but also enabling real-time treatment progress management, providing exercises at home, and reflecting feedback.

[0675] "Patient information input means" refers to a means for providing an electronic form for patients to input their symptoms and health information.

[0676] The "analysis means" is a means of classifying the patient's symptoms using an artificial intelligence (AI) algorithm based on the received patient information and listing appropriate treatments and treatment facilities.

[0677] The "treatment facility recommendation means" is a means for searching for and recommending the most suitable treatment facility based on the analysis results.

[0678] The "treatment plan generating means" is a means for generating a customized treatment plan suitable for the patient based on the analysis results.

[0679] A "progress sharing tool" is a means for sharing a patient's treatment progress in real time with their doctor or trainer.

[0680] A "generative AI model means" is a means for using a generative AI model to design appropriate exercises and provide them to a patient.

[0681] The "feedback analysis means" is a means for analyzing the results and impressions of the exercise received from the patient and reflecting them in the next exercise plan.

[0682] The present invention is a platform that provides personalized treatment plans and advanced treatment management for patients with lower back pain. Detailed embodiments of the system are described below.

[0683] Entering patient information

[0684] The server generates an electronic form for entering patient information, including details of the patient's symptoms, pain level, existing treatments, past medical history, etc. For example, it uses an HTML form and is deployed as a web application.

[0685] The terminal displays this form to the patient via a browser or application.

[0686] The user (patient) enters symptoms and health information into the form and presses the send button to send the data to the server.

[0687] Patient information analysis

[0688] The server receives the patient information sent from the terminal and passes it to the analysis module.

[0689] This analysis module uses, for example, Python scripts and machine learning models (e.g., scikit-learn) to classify a patient's symptoms using AI algorithms and shortlist suitable treatments and treatment facilities.

[0690] Specifically, based on the information received, the analysis module tags the data with terms such as "lower back pain" or "chronic lower back pain" and recommends treatments such as acupuncture and stretching.

[0691] Recommendation of clinics and treatment plans

[0692] The server then searches for and lists the most suitable treatment facilities from a database based on the analysis results. The database includes information such as the facility's reputation, specialty, and location. For example, the server queries an SQL database to obtain information on treatment facilities that meet the criteria.

[0693] The server also uses the analysis to generate a customized treatment plan for the patient, including details on visit frequency, home exercises, and treatment methods.

[0694] The terminal displays recommended treatment facilities and customized treatment plans for the patient.

[0695] The user reviews the information presented and selects the desired treatment facility.

[0696] Recording and sharing treatment progress

[0697] The device provides an interface for recording treatment progress. This interface allows patients to enter changes in pain after treatment, their impressions, and the next treatment schedule. For example, the interface can be built using a web application using React.

[0698] The user inputs the treatment progress status and sends it to the server.

[0699] The server receives the progress data and shares it with the patient's doctor or trainer via email or in-app notifications.

[0700] Generative AI exercises

[0701] The server uses generative AI to design optimal exercises based on the patient's symptoms and treatment progress. For example, it sends prompts to a generative AI model (e.g., OpenAI GPT-3) to generate exercise content.

[0702] The device displays the generated exercise video and guide to the patient, possibly using a YouTube video link to play the video and review the exercise steps.

[0703] The user performs the exercises and provides feedback on their results and impressions.

[0704] The server analyzes the received feedback and incorporates it into the next exercise plan.

[0705] Examples of prompt statements

[0706] "Generate an appropriate exercise plan for a patient with lower back pain. Pain intensity is 7 / 10, duration is 2 months."

[0707] This system provides patients with personalized treatment plans and real-time progress monitoring for their treatment. It uses generative AI to enable correct exercises to be performed at home, and its unique feature is that feedback can be reflected in the next treatment.

[0708] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0709] Step 1: Enter patient information

[0710] The server generates an electronic form for entering patient information and deploys it as a web application, including details of symptoms, pain level, existing treatments, past medical history, etc. The server then sends the generated form to the terminal.

[0711] The terminal displays this form to the patient via a browser or application.

[0712] The user (patient) enters symptoms and health information into the form and presses the send button to send the data to the server.

[0713] Input: Patient's symptoms, pain level, existing treatments, past medical history

[0714] Output: Patient information sent to the server

[0715] Step 2: Analyze patient information

[0716] The server passes the patient information received from the device to the analysis module, which uses an AI algorithm to classify the patient's symptoms and create a list of suitable treatments and treatment facilities, using Python scripts and machine learning models (e.g., scikit-learn).

[0717] Input: Patient Information

[0718] Output: Analysis results (e.g., tags such as "lower back pain" and "chronic lower back pain") and a list of recommended treatments (e.g., acupuncture, stretching)

[0719] Step 3: Recommend a treatment facility and treatment plan

[0720] Based on the analysis results, the server searches for and lists the most suitable treatment facilities from a database that includes information such as the reputation, specialty, and location of the treatment facilities. It sends a query to the SQL database to retrieve information on treatment facilities that meet the criteria.

[0721] The server also uses the analysis to generate a customized treatment plan for the patient, including details on visit frequency, home exercises, and treatment methods.

[0722] The terminal displays the list of treatment facilities and treatment plan generated by the server to the patient.

[0723] The user reviews the information presented and selects the desired treatment facility.

[0724] Input: Analysis results

[0725] Output: List of treatment facilities, customized treatment plan

[0726] Step 4: Record and share your treatment progress

[0727] The device provides an interface for recording treatment progress. This interface includes fields for recording pain changes, impressions, and next treatment schedules. The interface is built using a web application using React.

[0728] The user inputs the changes in pain after treatment, their impressions, and the next treatment schedule, and sends it to the server.

[0729] The server stores the received progress data and shares it with the patient's doctor or trainer via email or in-app notifications.

[0730] Input: Treatment progress information

[0731] Output: Saved progress data, sharing notifications with your doctor or trainer

[0732] Step 5: Generative AI provides exercises

[0733] The server uses a generative AI model to design optimal exercises based on the patient's symptoms and treatment progress. The exercise content is generated by sending prompts to the generative AI model (e.g., OpenAI GPT-3). For example, the server sends the prompt, "If the patient has pain in the lower back, please generate an exercise plan appropriate for the patient. The pain intensity is 7 / 10, and the duration is 2 months."

[0734] The device displays the generated exercise video and guide to the patient, using a YouTube video link to play the video and check the exercise steps.

[0735] The user performs the exercise and provides feedback on the results and impressions to the server.

[0736] The server analyzes the received feedback and incorporates it into your next exercise plan.

[0737] Input: Patient symptoms, treatment progress, feedback

[0738] Output: Optimal exercise plan, exercise video and guide, reflection on next exercise plan

[0739] (Application example 1)

[0740] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0741] Managing employee health, especially reducing chronic physical strain such as lower back pain, is an important issue in many industries. However, conventional systems have struggled to provide individually customized treatment plans, monitor health conditions in real time, and provide appropriate exercise plans. Furthermore, they lacked the functionality to monitor employees' movements and identify areas of strain, or to record and share exercise progress in real time. This resulted in employees not receiving optimal treatment, leading to reduced work efficiency and increased health risks.

[0742] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0743] In this invention, the server includes a patient information input means, a means for receiving and analyzing patient information, a means for recommending the most suitable treatment center based on the analysis results, a means for generating a customized treatment plan for the patient, a means for sharing treatment progress in real time, a means for providing exercise demonstrations using generative AI, a means for monitoring health status, a means for monitoring the employee's movements and identifying areas of strain, and a means for recording progress after performing the recommended exercise plan. This makes it possible to monitor the employee's health status in real time, provide individually customized treatment plans, demonstrate appropriate exercises, and record progress.

[0744] The "patient information input means" is an interface for patients to input their own symptoms and health information.

[0745] The "means for receiving and analyzing patient information" is a system for receiving input patient information and analyzing the data.

[0746] The "means for recommending the most suitable treatment center" is a system for recommending a treatment center suitable for the patient based on the analysis results.

[0747] The "means for generating a customized treatment plan" is a system that creates a treatment plan specifically for a patient based on the patient's symptoms and health condition.

[0748] "Means for sharing treatment progress in real time" is a system for sharing treatment progress and status with medical professionals in real time.

[0749] A "means for providing exercise demonstrations using generative AI" is a system for using a generative AI model to provide exercise demonstrations and guidance appropriate for a patient.

[0750] "Health monitoring means" refers to a system for constantly monitoring the health status of patients or employees and detecting abnormalities.

[0751] "Means for monitoring the employee's movements and identifying areas of stress" refers to a system for monitoring the employee's movements and identifying areas of the body that are under stress.

[0752] The "means for recording progress after carrying out a recommended exercise plan" is a system for recording the progress and effects of a provided exercise plan after carrying out the plan.

[0753] This invention is a system for managing the health of factory workers, and has the functions of providing and monitoring customized treatment plans to reduce physical strain, particularly lower back pain. This system is implemented using the following hardware and software configuration.

[0754] Hardware and software used

[0755] 1. Hardware:

[0756] Factory robots (e.g., industrial robots)

[0757] Health monitoring sensors (e.g., heart rate sensors, accelerometers)

[0758] Communication devices (e.g. smartphones, tablets)

[0759] 2. Software:

[0760] Python programming language

[0761] Robot System API

[0762] Health Management Applications

[0763] Program processing and data calculation

[0764] 1. Enter and analyze patient information:

[0765] The server generates a patient (employee) information entry form, and the employee enters their symptoms and health information, including the location of the pain, its intensity, and its duration.

[0766] The terminal transmits the input information to the server.

[0767] The server analyzes the received information and suggests appropriate treatment options, using an AI algorithm to analyze and recommend treatment options based on the employee's health status.

[0768] 2. Recommendation of the best clinic and treatment plan:

[0769] Based on the analysis results, the server searches the database for the most suitable clinic and recommends it to the employee.

[0770] The server generates a customized treatment plan for the employee, including visit frequency, home exercises, and detailed treatment regimens.

[0771] The terminal displays recommended clinics and treatment plans for the employee.

[0772] 3. Real-time sharing of treatment progress:

[0773] The terminal provides an interface for employees to record treatment progress.

[0774] The user inputs the progress of treatment through the application and sends it to the server.

[0775] The server analyzes the received progress data, shares it with the patient's doctor and trainer, and modifies the treatment plan as needed.

[0776] 4. Generative AI exercises:

[0777] The server uses generative AI to design optimal exercises based on the employee's symptoms and treatment progress. This AI generates exercise demonstration videos and guides taking into account the employee's feedback and progress.

[0778] The devices show employees exercise videos and guides.

[0779] Users watch exercise videos and perform exercises at home, then receive feedback on their results and impressions after the exercise.

[0780] The server analyzes the received feedback and incorporates it into the next exercise plan.

[0781] Specific examples

[0782] For example, if "employee_1" experiences pain in his lower back and enters the pain intensity as "7 / 10," the system will process it as follows:

[0783] The server analyzes the condition as "lower back pain" and "chronic pain" and suggests treatments including stretching.

[0784] The server recommends nearby fitness clubs specializing in stretching and generates a personalized treatment plan.

[0785] Employees can use this information to receive appropriate treatment. They can also do stretches at home while watching exercise videos, and record and share their progress using the app.

[0786] Prompt Sentence Examples

[0787] "Generate an exercise plan for an employee who is experiencing lower back pain. The pain is located in the lower back and the pain intensity is 7 / 10. Please suggest some exercises."

[0788] This allows for real-time monitoring of employee health, providing individually tailored treatment plans, demonstrating appropriate exercises and recording progress.

[0789] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0790] Step 1:

[0791] Enter and submit patient information

[0792] The terminal provides an interface for employees to input symptoms and health information through a patient information input form. For example, an employee may input "lower back pain, pain intensity 7 / 10, duration 2 months" and submit the information. The input data is sent to the server by the terminal. The input data includes details of the symptoms, pain level, existing treatments, and past medical history.

[0793] Step 2:

[0794] Receiving and analyzing patient information

[0795] The server receives patient information sent from the device. The received data is passed to the analysis module, which uses an AI algorithm to classify symptoms and create a list of suitable treatments and treatment centers. For example, the analysis might recommend "lower back pain → acupuncture and stretching." Here, the AI ​​algorithm refers to past data and existing medical knowledge to suggest the optimal treatment.

[0796] Step 3:

[0797] Generate optimal clinic and treatment plan

[0798] Based on the analysis results, the server searches the database for the most suitable clinic and recommends it to the employee. The recommendation includes each clinic's rating, specialty, location, etc. At the same time, a customized treatment plan is generated. The generated treatment plan includes details of the frequency of visits, home exercises, and treatment methods. For example, it recommends visiting the clinic twice a week and stretching daily.

[0799] Step 4:

[0800] View recommendations and treatment plans

[0801] The terminal displays the generated list of recommended clinics and the treatment plan to the employee. For example, the terminal's application screen may display a "list of nearby acupuncture clinics" and a "customized treatment plan." Employees can use this information to select the most suitable clinic and act according to the treatment plan.

[0802] Step 5:

[0803] Recording and sharing treatment progress

[0804] The device provides an interface for employees to record treatment progress. Employees enter information about changes in pain after treatment, their impressions, and future plans, and send the data to the server. The server analyzes the received progress data and shares it with the patient's doctor or trainer. It also modifies the treatment plan as needed. For example, if pain does not decrease, the treatment method is reevaluated.

[0805] Step 6:

[0806] Generative AI exercises

[0807] The server uses a generative AI model to design optimal exercises based on the employee's symptoms and treatment progress. This information includes exercise demonstration videos and procedures. For example, a "back stretch" video is generated. The generative AI model optimizes the exercises by taking into account the employee's feedback and progress. An example prompt is, "Please generate an exercise plan for an employee who is experiencing lower back pain. The pain is located in the lower back and the pain intensity is 7 / 10. Please recommend some exercises."

[0808] Step 7:

[0809] Exercise execution and feedback

[0810] The device displays the generated exercise video and guide to the employee. For example, a "back stretching" video is played on a smartphone or tablet. The employee performs the exercise and then records their results and thoughts afterwards. Post-exercise feedback is sent to the server and reflected in future exercise plans.

[0811] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0812] This invention is a platform that provides individualized treatment plans and advanced treatment management for patients with lower back pain, and also combines an emotion engine that recognizes the user's emotional state and optimizes treatment. The specific program processing and its functions are described in detail below.

[0813] 1. Entering and analyzing patient information

[0814] Server: Generates and makes accessible to users a patient information entry form, including details of symptoms, pain level, existing treatments, and past medical history.

[0815] Terminal: Patients access the device through a browser or app, enter their symptoms and health information, and then press the send button to send the data to the server.

[0816] User: Enter their back pain symptoms (e.g., location of pain, intensity of pain, duration of pain, etc.) and existing treatment information into the form and press the "Submit" button.

[0817] Terminal: Sends data sent by the user in JSON format to the server.

[0818] Server: Passes the received patient information to the analysis module, which uses an artificial intelligence (AI) algorithm to classify symptoms and create a list of suitable treatments and treatment centers.

[0819] 2. Recommendation of the best clinic and treatment plan

[0820] Server: Based on the analysis results, the server searches the database for information on the most suitable clinics and creates a list. It then makes recommendations based on the reputation, specialty, location, etc. of each clinic.

[0821] Server: Generates a personalized treatment plan for the patient, including details on visit frequency, home exercises, and treatment modalities.

[0822] Terminal: Displays recommended clinics and treatment plans to patients.

[0823] User: Check the information provided and select the most suitable clinic.

[0824] 3. Real-time sharing of treatment progress

[0825] Terminal: Provides patients with an interface for recording the progress of treatment. This interface allows them to input changes in pain after treatment, their impressions, and future plans.

[0826] User: Enters the progress of treatment and sends it to the server.

[0827] Terminal: Progress data sent by the user is sent to the server in JSON format.

[0828] Server: Receives progress data sent by patients and shares it with their doctors and trainers. Comprehensively analyzes the progress data and modifies the treatment plan as needed.

[0829] 4. Generative AI exercises

[0830] Server: Using generative AI, it designs optimal exercises based on the patient's symptoms and treatment progress. This AI takes into account the patient's feedback and progress and generates exercise demonstration videos and guides.

[0831] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[0832] User: Exercise at home while watching exercise videos. After the exercise, feedback on results and impressions is provided within the app.

[0833] Server: Analyzes the received feedback and incorporates it into your next exercise plan.

[0834] 5. Emotion recognition and reflection using the emotion engine

[0835] Server: Activates the emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using the comments and data entered by the user, as well as the user's behavioral data (e.g., input speed, input content, voice data, etc.).

[0836] Device: When the user inputs their thoughts on treatment progress and exercise, the emotion engine analyzes the input data in real time and evaluates the user's emotional state.

[0837] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend relaxing exercises.

[0838] Device: Presents users with customized treatment plans and exercises that reflect their emotional state.

[0839] Specific examples

[0840] Patient information entry and analysis

[0841] User: In the lower back pain condition input form, enter "Location of pain: lower back, pain intensity: 7 / 10, duration: 2 months" and press the submit button.

[0842] Terminal: Sends input information to the server.

[0843] Server: Analyzes the received information, tags it as "lower back pain" and "chronic lower back pain," and recommends appropriate treatments (e.g., acupuncture, stretching).

[0844] Recommendation of the best clinic and treatment plan

[0845] Server: Searches the database for nearby acupuncture clinics and fitness clubs specializing in stretching, and presents the patient with a list of clinics suitable for the patient and a customized treatment plan.

[0846] User: Select the desired clinic from the list of clinics presented.

[0847] Emotion engine recognizes and reflects emotions

[0848] User: Enters a comment about treatment progress, saying, "Today the pain has eased a little, but I'm still a little anxious," and submits it.

[0849] Terminal: Sends comments to the server.

[0850] Server: The emotion engine analyzes the comments and identifies the emotion "anxiety."

[0851] Server: Based on the analysis results, a new treatment plan is generated that includes relaxing exercises.

[0852] Terminal: Displays the new treatment plan to the user.

[0853] In this way, by combining the emotion engine, it is possible to provide customized treatment that takes into consideration the patient's emotions, thereby improving the effectiveness of treatment.

[0854] The processing flow will be explained below.

[0855] Patient information entry and analysis

[0856] Step 1:

[0857] The server generates a patient information entry form and makes it accessible to the user.

[0858] Step 2:

[0859] The device displays a form in the user's browser or app and accepts input of symptoms and health information.

[0860] Step 3:

[0861] The user enters their lower back pain symptoms (e.g., location of pain, intensity of pain, duration of pain, etc.) and existing treatment information into the form and presses the "Submit" button.

[0862] Step 4:

[0863] The device sends the data sent by the user to the server in JSON format.

[0864] Step 5:

[0865] The server receives the input data and passes it to the analysis module.

[0866] Step 6:

[0867] The server's analysis module analyzes the data, classifies the symptoms, and lists suitable treatments and treatment centers.

[0868] Recommendation of the best clinic and treatment plan

[0869] Step 1:

[0870] Based on the analysis results, the server searches the database for appropriate clinic information.

[0871] Step 2:

[0872] The server collects information such as the ratings, specialties, and locations of each clinic, and creates a list of the most suitable clinics.

[0873] Step 3:

[0874] The server generates an optimized treatment plan for the patient, including the frequency of visits and specific treatment methods.

[0875] Step 4:

[0876] The terminal displays a list of recommended clinics and treatment plans to the patient.

[0877] Step 5:

[0878] The user checks the displayed information and selects the desired clinic.

[0879] Real-time sharing of treatment progress

[0880] Step 1:

[0881] The terminal provides the user with an interface for recording the progress of treatment.

[0882] Step 2:

[0883] The user enters and submits the progress of treatment (e.g., changes in pain, impressions, next appointment, etc.).

[0884] Step 3:

[0885] The device sends the progress data sent by the user to the server in JSON format.

[0886] Step 4:

[0887] The server receives the progress data and stores it in a database.

[0888] Step 5:

[0889] The server shares the saved progress data with the patient's doctor or trainer.

[0890] Step 6:

[0891] The server analyzes the progress data and modifies the treatment plan as needed.

[0892] Step 7:

[0893] The terminal notifies the user of the revised treatment plan.

[0894] Generative AI exercises

[0895] Step 1:

[0896] The server runs a generative AI engine and receives the user's health information and progress data as input.

[0897] Step 2:

[0898] The server uses generative AI to design exercises based on the patient's symptoms and condition.

[0899] Step 3:

[0900] The server generates a demonstration video and guide text for the designed exercise.

[0901] Step 4:

[0902] The device displays a link to the exercise video and guide text to the user.

[0903] Step 5:

[0904] Users watch exercise videos and follow the instructions to perform the exercises at home.

[0905] Step 6:

[0906] The user provides feedback on the results and impressions of the exercise.

[0907] Step 7:

[0908] The terminal transmits the feedback from the user to the server.

[0909] Step 8:

[0910] The server analyzes the feedback and reflects it in your next exercise plan.

[0911] Emotion engine recognizes and reflects emotions

[0912] Step 1:

[0913] The server starts an emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using the comments and data entered by the user and the user's behavioral data (e.g., input speed, input content, voice data, etc.).

[0914] Step 2:

[0915] When the device inputs the user's treatment progress and impressions of the exercises, the emotion engine analyzes this in real time and evaluates the user's emotional state.

[0916] Step 3:

[0917] The server receives the analysis results from the emotion engine and reflects them in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend exercises that will have a relaxing effect.

[0918] Step 4:

[0919] The device displays customized treatment plans and exercises to the user that reflect their emotional state.

[0920] Specific examples

[0921] Patient information entry and analysis

[0922] Step 1:

[0923] The user inputs the symptoms of lower back pain (e.g., location of pain, intensity of pain, duration of pain) and presses the submit button.

[0924] Step 2:

[0925] The terminal sends the input information to the server.

[0926] Step 3:

[0927] The server analyzes the information it receives, tags it with "lower back pain" and "chronic lower back pain," and recommends appropriate treatments and clinics.

[0928] Recommendation of the best clinic and treatment plan

[0929] Step 1:

[0930] Based on the analysis results, the server searches the database for appropriate treatment centers and creates a list of treatment centers and a treatment plan suitable for the patient.

[0931] Step 2:

[0932] The user selects the desired clinic from the displayed list of clinics and checks the specific treatment plan.

[0933] Emotion engine recognizes and reflects emotions

[0934] Step 1:

[0935] The user enters a comment about the progress of treatment, such as "Today the pain has eased a little, but I'm still a little worried," and submits it.

[0936] Step 2:

[0937] The device sends the comment to the server.

[0938] Step 3:

[0939] The server analyzes the comments using an emotion engine and identifies the emotion of "anxiety."

[0940] Step 4:

[0941] Based on the analysis results, the server generates a new treatment plan that includes relaxing exercises.

[0942] Step 5:

[0943] The device displays the new treatment plan to the user.

[0944] By combining emotion engines in this way, it is possible to provide customized treatment that takes into account the patient's emotional state, improving the effectiveness of treatment.

[0945] Example 2

[0946] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0947] Conventional lower back pain treatment systems are lacking in providing appropriate treatment plans for individual patients' symptoms and emotional states, leaving the need for improved treatment effectiveness. Furthermore, there are issues with the lack of real-time sharing of treatment progress and the provision of generative AI-based exercises, resulting in a suboptimal treatment experience.

[0948] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0949] In this invention, the server includes a patient information input means, a means for receiving and analyzing the patient information, a means for recommending an optimal treatment facility based on the analysis results, a means for generating a treatment plan customized for the patient, a means for sharing treatment progress in real time, a means for providing exercise demonstrations using a generative AI, and a means for recognizing the patient's emotional state using an emotion analysis means and optimizing the treatment plan based thereon. This makes it possible to provide an optimal treatment plan based on the patient's individual symptoms and emotional state, thereby improving treatment effectiveness and patient satisfaction.

[0950] The "patient information input means" is a means for providing an interface for patients to input their own symptoms and health information.

[0951] The "means for receiving and analyzing the patient information" refers to a means for receiving information entered by the patient on a server and using an AI algorithm or the like to classify symptoms and recommend treatment methods based on that information.

[0952] The "means for recommending the most suitable treatment facility" is a means for searching a database for the most suitable treatment facility for the patient based on the analysis results, and listing it based on information such as rating, specialty, and location.

[0953] The "means for generating a customized treatment plan" refers to a means for generating an individually optimized treatment plan based on the patient's symptoms and analysis results.

[0954] "Means for sharing treatment progress in real time" refers to a means for transmitting a patient's treatment progress data to a server and sharing it with the attending physician or trainer in real time.

[0955] "Means for providing exercise demonstrations using generative AI" refers to means for using generative AI to create an optimal exercise plan based on the patient's symptoms and progress of treatment, and providing the plan to the patient as a video or guide.

[0956] "Means for recognizing a patient's emotional state using emotion analysis means and optimizing a treatment plan based on that" refers to means for analyzing the patient's input data and behavioral data, recognizing the patient's emotional state, and dynamically optimizing a treatment plan based on that.

[0957] This invention is a platform that provides individualized treatment plans and advanced treatment management for patients with lower back pain, and also combines an emotion engine that recognizes the user's emotional state and optimizes treatment. The specific program processing and its functions are described in detail below.

[0958] Patient information entry and analysis

[0959] Server: Generates a patient information input form that users can access. This form includes details of symptoms, pain level, existing treatments, past medical history, etc. Specifically, the form is generated using HTML and CSS, and real-time data validation and submission functionality is implemented using JavaScript.

[0960] Device: Patients access the device through a browser or app and enter their symptoms and health information. Specifically, they can use a smartphone or PC browser. They then press the send button to send the data to the server.

[0961] Server: Passes the received patient information to the analysis module. This analysis module is implemented in Python and uses artificial intelligence (AI) algorithms with libraries such as TensorFlow and PyTorch to classify symptoms. It then lists candidates for appropriate treatments and treatment facilities.

[0962] Example: A user enters "Pain location is lower back, pain intensity is 7 / 10, duration is 2 months" into a form to input their lower back pain condition, and presses the submit button. The device converts the input information into JSON format and sends it to the server. The server analyzes the received information, tags it as "lower back pain" and "chronic lower back pain," and recommends appropriate treatments (e.g., acupuncture, stretching).

[0963] Recommendation of the best treatment facility and treatment plan

[0964] Server: Based on the analysis results, the server searches the database for information on the most suitable treatment facilities and creates a list. Specifically, it uses a relational database such as MySQL or PostgreSQL. It makes recommendations based on the reputation, specialty, location, etc. of each treatment facility.

[0965] Server: Generates an optimized treatment plan for the patient. This treatment plan includes details on the frequency of visits, home exercises, and treatment modalities. It is automatically generated using a Python script based on the analysis results from the AI ​​module.

[0966] Terminal: Display recommended treatment facilities and treatment plans to patients. Specifically, the data is rendered using a front-end framework (e.g., React or Vue.js).

[0967] Example: The server searches the database for nearby acupuncture clinics and fitness clubs specializing in stretching, and presents a list of treatment facilities suitable for the patient and a customized treatment plan. The user selects the desired facility from the presented list of treatment facilities.

[0968] Real-time sharing of treatment progress

[0969] Terminal: Provides an interface for recording the progress of treatment. This interface allows users to input changes in pain after treatment, their impressions, and future plans. Specifically, data entry is facilitated using a mobile application.

[0970] User: Enters the progress of treatment and sends it to the server. For example, enters information such as "The pain has eased a little."

[0971] Server: Shares the received progress data with the patient's doctor and trainer. The data is shared using cloud storage (e.g., Amazon S3). The progress data is comprehensively analyzed and the treatment plan is revised as necessary. This analysis is performed using machine learning algorithms.

[0972] Generative AI exercises

[0973] Server: Using generative AI, the server designs optimal exercises based on the patient's symptoms and treatment progress. The AI ​​takes into account the patient's feedback and progress and generates exercise demonstration videos and guides. Specifically, it uses a video generation engine (e.g., OpenAI's DALL-E or GPT-3).

[0974] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[0975] User: Exercise at home while watching exercise videos. After the exercise, feedback on results and impressions is provided within the app.

[0976] Server: Analyzes the received feedback and incorporates it into your next exercise plan.

[0977] Emotion engine recognizes and reflects emotions

[0978] Server: Launches the emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using comments and data entered by the user and their behavioral data (e.g., input speed, input content, voice data, etc.). It uses a natural language processing (NLP) library (e.g., SpaCy, NLTK).

[0979] Device: When the user inputs their thoughts on treatment progress and exercise, the emotion engine analyzes the input data in real time and evaluates the user's emotional state.

[0980] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend relaxing exercises.

[0981] Example: A user inputs and sends a comment about their treatment progress, such as "Today the pain has eased a little, but I'm still a little anxious." The device sends the comment to the server, which uses an emotion engine to analyze the comment and identify the emotion of "anxiety." Based on the analysis results, a new treatment plan including relaxation exercises is generated and the new treatment plan is displayed to the user on the device.

[0982] In this way, by combining the emotion engine, it is possible to provide customized treatment that takes into consideration the patient's emotions, thereby improving the effectiveness of treatment.

[0983] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0984] Step 1:

[0985] Server: Generates a patient information input form and makes it accessible to users. The input is a web form using HTML and CSS, and items include details of symptoms, pain level, existing treatments, and past medical history. The output is an interface that accepts user input. Specifically, it places form elements in an HTML file and implements real-time validation functionality using JavaScript.

[0986] Step 2:

[0987] Device: The patient accesses the input form through a browser or app and enters symptoms and health information. Input involves entering information such as symptoms into each field. Output involves saving the entered data on the device. Specifically, the user enters information into text boxes and drop-down menus on the form and presses the submit button.

[0988] Step 3:

[0989] Terminal: Data entered by the user is sent to the server in JSON format. Input is the information entered in each field. Output is the data converted to JSON format and sent to the server. Specifically, form data is serialized into JSON using a JavaScript function and sent to the server via an HTTP POST request.

[0990] Step 4:

[0991] Server: Passes the received patient information to the analysis module. The input is the JSON data sent to the server. The output is the symptom classification results and a list of recommended treatments and treatment facilities. Specifically, it runs a Python script and performs analysis using AI libraries such as TensorFlow and PyTorch.

[0992] Step 5:

[0993] Server: Based on the analysis results, it searches the database for information on the most suitable treatment facilities and creates a list. The input is the analysis results by AI. The output is a list of treatment facilities that are best suited to the user. Specifically, it executes an SQL query to retrieve treatment facility information from the database and filters it based on the analysis results.

[0994] Step 6:

[0995] Server: Generates a treatment plan optimized for the patient. The input is the analysis results and treatment facility information obtained from the database. The output is a customized treatment plan. Specifically, the treatment plan is automatically generated using a Python script based on the results of the AI ​​module.

[0996] Step 7:

[0997] Terminal: Displays recommended treatment facilities and treatment plans to patients. The input is the list of treatment facilities and customized treatment plans sent from the server. The output is an interface that displays them to the user. Specific operations include rendering the data using front-end frameworks such as React and Vue.js.

[0998] Step 8:

[0999] User: Check the presented information and select the most suitable treatment facility. The input is the list of treatment facilities displayed on the terminal. The output is the selected treatment facility information. Specifically, the user selects the desired treatment facility from the list and presses the decision button.

[1000] Step 9:

[1001] Terminal: Provides an interface for recording the progress of treatment. Input includes changes in pain after treatment, impressions, and future plans. Output is the progress data entered by the user. Specific operations include displaying a form with text fields and check boxes.

[1002] Step 10:

[1003] User: Enters the progress of treatment and sends it to the server. The input is the progress information of treatment. The output is the progress data sent to the server. The specific operation is to enter the progress and press the send button.

[1004] Step 11:

[1005] Terminal: The progress data sent by the user is sent to the server in JSON format. The input is the entered progress information. The output is the JSON data sent to the server. Specifically, the input content is converted to JSON and sent to the server via an HTTP POST request.

[1006] Step 12:

[1007] Server: Shares the received progress data with the doctor or trainer. The input is the progress data sent by the user. The output is the shared progress data. Specifically, it stores the progress data in a database and shares it using a relational database management system (RDBMS).

[1008] Step 13:

[1009] Server: Uses generative AI to design optimal exercises based on the patient's symptoms and treatment progress. Input is patient feedback and progress data. Output is exercise demonstration videos and guides. Specific operations include running the generative AI algorithm to generate individual exercise plans and create videos and guidelines.

[1010] Step 14:

[1011] Device: Displays exercise videos and guides to patients. The input is the generated exercise video and guide. The output is the exercise video and guide displayed in a playable format on the display screen. The specific operation is to display the generated video and guide in a playable format on a web page or within the app.

[1012] Step 15:

[1013] User: Exercises at home while watching an exercise video, and provides feedback on the results and impressions. The input is the results and impressions of the exercise. The output is feedback data sent within the app. Specifically, the user exercises while watching the video, and after finishing, they enter and send their impressions and results.

[1014] Step 16:

[1015] Server: Analyzes the received feedback and reflects it in the next exercise plan. The input is the feedback data sent by the user. The output is the next exercise plan. Specifically, the feedback data is passed to the AI ​​module for analysis and automatically reflected in the next plan.

[1016] Step 17:

[1017] Server: Launches the emotion engine and analyzes user input data and interactions. Inputs are comments and behavioral data entered by users. The output is analyzed emotion data. Specific operations include running the emotion analysis algorithm and extracting emotions from text and behavioral data.

[1018] Step 18:

[1019] Terminal: When the user inputs their thoughts on treatment progress or exercise, the emotion engine analyzes the input data in real time and evaluates their emotional state. The input is the user's thoughts and behavioral data. The output is the evaluation result of their emotional state. Specific operations include instantly analyzing the input data and interactions and determining their emotional state.

[1020] Step 19:

[1021] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. The input is the emotional data from the emotion analysis engine. The output is an optimized treatment plan. Specifically, the emotion data is passed to the treatment plan generation module, which generates a new treatment plan.

[1022] Step 20:

[1023] Terminal: Displays to the user a customized treatment plan or exercises that reflect the user's emotional state. The input is a new treatment plan sent from the server. The output is rendering the customized treatment plan or exercises on the display screen. The specific operation is to render the new treatment plan or exercises on the display screen.

[1024] (Application example 2)

[1025] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1026] Current low back pain treatments are still lacking in providing treatment plans that take into account each patient's individual symptoms and emotional state. Even for the same symptoms, the optimal treatment and treatment center may differ depending on the patient's emotions and daily life. There is also a lack of a system for sharing treatment progress in real time and providing individually customized meal plans. Therefore, there is a need for a system that can provide optimal treatment for each patient and maximize its effectiveness.

[1027] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a patient information input means, a means for receiving and analyzing patient information, a means for recommending an optimal treatment center based on the analysis results, a means for generating a treatment plan customized for the patient, a means for sharing treatment progress in real time, a means for providing exercise demonstrations using a generation AI, a means for analyzing the user's emotional state using an emotion engine and reflecting the analysis in the treatment plan, and a means for generating an optimal meal plan based on the user's emotional state and providing the meal in cooperation with a delivery service. This makes it possible to provide an optimal treatment plan that takes into account the symptoms and emotional state of each patient, share treatment progress in real time, and provide individually customized meal plans.

[1028] The "patient information input means" is an interface for patients to input their own symptoms and health information.

[1029] The "means for receiving and analyzing" is a module that receives input patient information and analyzes it using AI or other methods.

[1030] "Means for recommending the most suitable treatment center" is a function that suggests a treatment center that is suitable for the patient based on the analysis results.

[1031] A "means for generating a customized treatment plan" is a system that creates a treatment plan tailored to the symptoms and conditions of each individual patient.

[1032] "Means for sharing treatment progress in real time" is a function that allows patients and healthcare providers to check the progress of treatment in real time.

[1033] The "means for providing exercise demonstrations using generative AI" is a system that uses AI to provide patients with optimal exercise procedures and videos.

[1034] "Means of analyzing the user's emotional state using an emotion engine and reflecting it in the treatment plan" is a function that analyzes emotions based on the user's input data and behavior, and takes this into consideration and reflects it in the treatment plan.

[1035] "Means for generating optimal meal plans and providing meals in cooperation with delivery services" refers to a system that creates meal menus tailored to the user's health and emotional state and provides those meals in cooperation with delivery services.

[1036] This invention is a system including a patient information input means, a receiving and analyzing means, a recommending optimal treatment center, a generating a customized treatment plan, a sharing means for sharing treatment progress in real time, a providing exercise demonstrations using a generative AI, a means for analyzing a user's emotional state using an emotion engine and reflecting it in a treatment plan, and a generating an optimal meal plan and providing meals in cooperation with a delivery service.

[1037] Patient information entry and analysis

[1038] Server: Generates and makes accessible to users a patient information entry form, including details of symptoms, pain level, existing treatments, and past medical history.

[1039] Terminal: Patients access the terminal through a browser or app, enter their health information, and then press the send button to send the data to the server.

[1040] Server: Passes the received patient information to the analysis module, which uses an artificial intelligence (AI) algorithm to classify symptoms and create a list of suitable treatments and treatment centers.

[1041] Recommendation of the best clinic and treatment plan

[1042] Server: Based on the analysis results, the server searches the database for information on the most suitable clinics and creates a list. It then makes recommendations based on the reputation, specialty, location, etc. of each clinic.

[1043] Server: Generates a personalized treatment plan for the patient, including details on visit frequency, home exercises, and treatment modalities.

[1044] Terminal: Displays recommended clinics and treatment plans to patients.

[1045] Real-time sharing of treatment progress

[1046] Terminal: Provides patients with an interface for recording the progress of treatment. This interface allows them to input changes in pain after treatment, their impressions, and future plans.

[1047] User: Enters the progress of treatment and sends it to the server.

[1048] Server: Receives progress data sent by patients and shares it with their doctors and trainers. Comprehensively analyzes the progress data and modifies the treatment plan as needed.

[1049] Generative AI exercises

[1050] Server: Using generative AI, it designs optimal exercises based on the patient's symptoms and treatment progress. This AI takes into account the patient's feedback and progress and generates exercise videos and guides.

[1051] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[1052] User: Exercise at home while watching exercise videos. After the exercise, feedback on results and impressions is provided within the app.

[1053] Emotion engine recognizes and reflects emotions

[1054] Server: Activates the emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using the comments and data entered by the user, as well as the user's behavioral data (e.g., input speed, input content, voice data, etc.).

[1055] Device: When the user inputs their thoughts on treatment progress and exercise, the emotion engine analyzes the input data in real time and evaluates the user's emotional state.

[1056] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend relaxing exercises.

[1057] Meal plan generation and delivery service

[1058] Server: Using an emotion engine and generative AI, it generates an optimal meal plan based on the user's emotional state and symptoms. This plan takes into account nutritional balance and relaxation effects.

[1059] Device: Provides an interface where users can view meal plans within the app and order delivery with one tap.

[1060] Users: After receiving their meal, they provide feedback in the app to be reflected in their next meal plan.

[1061] Specific examples

[1062] Example of input

[1063] User: "I'm feeling a bit tired today and would like a relaxing meal."

[1064] Server: Analyzes the input information, and the emotion engine identifies "feeling tired" and "desire to relax."

[1065] Generative AI model prompt: "The user is tired and wants to relax. Please suggest recipes using ingredients that have a relaxing effect."

[1066] Results: The AI ​​suggests relaxing meals such as "chicken soup with lemongrass" and "lavender tea."

[1067] In this way, the entire system works together to provide personalized treatments and meal plans based on the user's health and emotional state.

[1068] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1069] Step 1:

[1070] The user opens the smartphone app and enters symptoms and health information into the patient information input form.

[1071] The data entered includes the location of the pain, the intensity of the pain, and the duration of the pain.

[1072] The terminal sends this input data to the server in JSON format.

[1073] Step 2:

[1074] The server passes the received JSON data to the analysis module.

[1075] This module uses AI algorithms such as TensorFlow to analyze and classify patient symptoms.

[1076] As a result of the analysis, tags such as "lower back pain" and "chronic lower back pain" are assigned.

[1077] Step 3:

[1078] The server searches the database based on the tagged analysis results and generates a list of suitable clinics.

[1079] The list includes information such as each clinic's rating, specialty, treatment features, and location.

[1080] This information is transmitted to the user terminal.

[1081] Step 4:

[1082] The terminal displays the customized treatment plan to the user along with a list of recommended clinics.

[1083] The treatment plan includes details of how often to visit, exercises to do at home, and treatment options.

[1084] The user selects the desired clinic from this list.

[1085] Step 5:

[1086] The app records the user's progress as they visit a clinic or do exercises at home and sends the information to a server.

[1087] The information entered includes changes in pain after treatment, impressions, and future plans.

[1088] The device sends this progress data to the server in JSON format.

[1089] Step 6:

[1090] The server receives the progress data and shares it with the patient's doctor or trainer.

[1091] Comprehensive analysis of progress data will determine whether the treatment plan is working or if adjustments are needed.

[1092] Modify the treatment plan as needed.

[1093] Step 7:

[1094] The server uses generative AI to design optimal exercises based on progress data and treatment status.

[1095] Exercise demonstration videos and guides are generated and sent to the user's device.

[1096] Step 8:

[1097] Users can exercise at home by watching exercise videos and guides.

[1098] After the exercise, feedback on results and impressions is provided within the app, and the data is sent to the server.

[1099] Step 9:

[1100] The server runs the emotion engine and analyzes the user's input data and interactions.

[1101] The system recognizes the user's emotional state (e.g., "stressed" or "relaxed") from comments, input data, voice data, etc.

[1102] Emotional data is also reflected in optimizing treatment plans.

[1103] Step 10:

[1104] The server and generating AI generate an optimal meal plan based on the user's emotional state.

[1105] For example, if you feel tired and want to relax, we will suggest a meal plan using ingredients that have a relaxing effect.

[1106] Step 11:

[1107] The device displays a customized meal plan to the user and allows them to order delivery with just one tap.

[1108] After receiving their meal, users provide feedback within the app and send the data to be reflected in their next plan.

[1109] By following these steps, the entire system works together to provide optimal treatment and meal plans that take into account each user's individual symptoms and emotional state.

[1110] 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.

[1111] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1112] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1113] [Third embodiment]

[1114] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1117] 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.

[1118] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1119] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1120] 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.

[1121] 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.

[1122] 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 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.

[1123] 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.

[1124] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1125] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1126] This invention is a platform that provides individualized treatment plans and advanced treatment management for patients with lower back pain. Specific program processing and its functions will be described in detail below.

[1127] 1. Entering and analyzing patient information

[1128] Server: Generates and makes accessible to patients a patient information entry form, including details of symptoms, pain level, existing treatments, and past medical history.

[1129] Terminal: Patients access the device through a browser or app, enter their symptoms and health information, and then press the send button to send the data to the server.

[1130] Server: Passes the received patient information to the analysis module, which uses an artificial intelligence (AI) algorithm to classify symptoms and create a list of suitable treatments and treatment centers.

[1131] 2. Recommendation of the best clinic and treatment plan

[1132] Server: Based on the analysis results, the server searches the database for information on the most suitable clinics and creates a list. It then makes recommendations based on the reputation, specialty, location, etc. of each clinic.

[1133] Server: Generates a personalized treatment plan for the patient, including details on visit frequency, home exercises, and treatment modalities.

[1134] Terminal: Displays recommended clinics and customized treatment plans to patients.

[1135] User: Check the information provided and select the most suitable clinic.

[1136] 3. Real-time sharing of treatment progress

[1137] Terminal: Provides patients with an interface for recording the progress of treatment. This interface allows them to input changes in pain after treatment, their impressions, and future plans.

[1138] User: Enters the progress of treatment and sends it to the server.

[1139] Server: Receives progress data sent by patients and shares it with their doctors and trainers. Comprehensively analyzes the progress data and modifies the treatment plan as needed.

[1140] 4. Generative AI exercises

[1141] Server: Using generative AI, it designs optimal exercises based on the patient's symptoms and treatment progress. This AI takes into account the patient's feedback and progress and generates exercise demonstration videos and guides.

[1142] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[1143] User: Exercise at home while watching exercise videos. After the exercise, provide feedback on the results and impressions.

[1144] Server: Analyzes the received feedback and incorporates it into your next exercise plan.

[1145] Specific examples

[1146] Patient information entry and analysis

[1147] The user enters the symptoms of lower back pain as "Location of pain: lower back, pain intensity: 7 / 10, duration: 2 months" and presses the send button.

[1148] The terminal sends the information to the server.

[1149] The server analyzes the received information, tags it as "lower back pain" and "chronic lower back pain," and recommends appropriate treatments (e.g., acupuncture, stretching).

[1150] Recommendation of the best clinic and treatment plan

[1151] The server searches the database for nearby acupuncture clinics and fitness clubs specializing in stretching, and presents the patient with a list of clinics suitable for the patient and a customized treatment plan.

[1152] The user selects the desired clinic from the list of recommended clinics and checks the specific treatment plan.

[1153] This allows the system to provide more effective and personalized treatment to patients with lower back pain, monitor their progress in real time, and utilizes generative AI to help patients perform the correct exercises at home.

[1154] The processing flow will be explained below.

[1155] Patient information entry and analysis

[1156] Step 1:

[1157] The server generates a patient information entry form and makes it accessible to the user.

[1158] Step 2:

[1159] The device displays a form in the user's browser or app and accepts input of symptoms and health information.

[1160] Step 3:

[1161] The user enters their lower back pain symptoms (e.g., location of pain, intensity of pain, duration of pain, etc.) and existing treatment information into the form and presses the "Submit" button.

[1162] Step 4:

[1163] The device sends the data sent by the user to the server in JSON format.

[1164] Step 5:

[1165] The server receives the input data and passes it to the analysis module.

[1166] Step 6:

[1167] The server's analysis module analyzes the data, classifies the symptoms, and lists suitable treatments and treatment centers.

[1168] Recommendation of the best clinic and treatment plan

[1169] Step 1:

[1170] Based on the analysis results, the server searches the database for appropriate clinic information.

[1171] Step 2:

[1172] The server collects information such as the ratings, specialties, and locations of each clinic, and creates a list of the most suitable clinics.

[1173] Step 3:

[1174] The server generates an optimized treatment plan for the patient, including the frequency of visits and specific treatment methods.

[1175] Step 4:

[1176] The terminal displays a list of recommended clinics and treatment plans to the patient.

[1177] Step 5:

[1178] The user checks the displayed information and selects the desired clinic.

[1179] Real-time sharing of treatment progress

[1180] Step 1:

[1181] The terminal provides the user with an interface for recording the progress of treatment.

[1182] Step 2:

[1183] The user enters and submits the progress of treatment (e.g., changes in pain, impressions, next appointment, etc.).

[1184] Step 3:

[1185] The terminal transmits the progress data transmitted by the user to the server.

[1186] Step 4:

[1187] The server receives the progress data and stores it in a database.

[1188] Step 5:

[1189] The server shares the saved progress data with the patient's doctor or trainer.

[1190] Step 6:

[1191] The server analyzes the progress data and modifies the treatment plan as needed.

[1192] Step 7:

[1193] The terminal notifies the user of the revised treatment plan.

[1194] Generative AI exercises

[1195] Step 1:

[1196] The server runs a generative AI engine and receives the user's health information and progress data as input.

[1197] Step 2:

[1198] The server uses generative AI to design exercises based on the patient's symptoms and condition.

[1199] Step 3:

[1200] The server generates a demonstration video and guide text for the designed exercise.

[1201] Step 4:

[1202] The device displays a link to the exercise video and guide text to the user.

[1203] Step 5:

[1204] Users watch exercise videos and follow the instructions to perform the exercises at home.

[1205] Step 6:

[1206] The user provides feedback on the results and impressions of the exercise.

[1207] Step 7:

[1208] The terminal transmits the feedback from the user to the server.

[1209] Step 8:

[1210] The server analyzes the feedback and reflects it in your next exercise plan.

[1211] In this way, the system performs specific operations at each processing step to help patients with lower back pain receive more effective treatment.

[1212] Example 1

[1213] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1214] In current systems for treating lower back pain, patient information entry, treatment plan generation, and treatment progress management are all performed separately, with no integrated management method available. This makes it difficult to recommend the most suitable treatment center for a patient or provide an individualized treatment plan, preventing improvements in treatment effectiveness. Real-time patient progress management is also difficult, limiting the provision of home exercises and the incorporation of feedback. A system that solves these issues is needed.

[1215] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1216] In this invention, the server includes a patient information input unit, a unit for receiving and analyzing the patient information, and a unit for analyzing the patient's feedback using a generative AI model and reflecting it in the next exercise plan, which enables integrated treatment management, not only realizing personalized treatment plans and recommendations of optimal treatment facilities, but also enabling real-time treatment progress management, providing exercises at home, and reflecting feedback.

[1217] "Patient information input means" refers to a means for providing an electronic form for patients to input their symptoms and health information.

[1218] The "analysis means" is a means of classifying the patient's symptoms using an artificial intelligence (AI) algorithm based on the received patient information and listing appropriate treatments and treatment facilities.

[1219] The "treatment facility recommendation means" is a means for searching for and recommending the most suitable treatment facility based on the analysis results.

[1220] The "treatment plan generating means" is a means for generating a customized treatment plan suitable for the patient based on the analysis results.

[1221] A "progress sharing tool" is a means for sharing a patient's treatment progress in real time with their doctor or trainer.

[1222] A "generative AI model means" is a means for using a generative AI model to design appropriate exercises and provide them to a patient.

[1223] The "feedback analysis means" is a means for analyzing the results and impressions of the exercise received from the patient and reflecting them in the next exercise plan.

[1224] The present invention is a platform that provides personalized treatment plans and advanced treatment management for patients with lower back pain. Detailed embodiments of the system are described below.

[1225] Entering patient information

[1226] The server generates an electronic form for entering patient information, including details of the patient's symptoms, pain level, existing treatments, past medical history, etc. For example, it uses an HTML form and is deployed as a web application.

[1227] The terminal displays this form to the patient via a browser or application.

[1228] The user (patient) enters symptoms and health information into the form and presses the send button to send the data to the server.

[1229] Patient information analysis

[1230] The server receives the patient information sent from the terminal and passes it to the analysis module.

[1231] This analysis module uses, for example, Python scripts and machine learning models (e.g., scikit-learn) to classify a patient's symptoms using AI algorithms and shortlist suitable treatments and treatment facilities.

[1232] Specifically, based on the information received, the analysis module tags the data with terms such as "lower back pain" or "chronic lower back pain" and recommends treatments such as acupuncture and stretching.

[1233] Recommendation of clinics and treatment plans

[1234] The server then searches for and lists the most suitable treatment facilities from a database based on the analysis results. The database includes information such as the facility's reputation, specialty, and location. For example, the server queries an SQL database to obtain information on treatment facilities that meet the criteria.

[1235] The server also uses the analysis to generate a customized treatment plan for the patient, including details on visit frequency, home exercises, and treatment methods.

[1236] The terminal displays recommended treatment facilities and customized treatment plans for the patient.

[1237] The user reviews the information presented and selects the desired treatment facility.

[1238] Recording and sharing treatment progress

[1239] The device provides an interface for recording treatment progress. This interface allows patients to enter changes in pain after treatment, their impressions, and the next treatment schedule. For example, the interface can be built using a web application using React.

[1240] The user inputs the treatment progress status and sends it to the server.

[1241] The server receives the progress data and shares it with the patient's doctor or trainer via email or in-app notifications.

[1242] Generative AI exercises

[1243] The server uses generative AI to design optimal exercises based on the patient's symptoms and treatment progress. For example, it sends prompts to a generative AI model (e.g., OpenAI GPT-3) to generate exercise content.

[1244] The device displays the generated exercise video and guide to the patient, possibly using a YouTube video link to play the video and review the exercise steps.

[1245] The user performs the exercises and provides feedback on their results and impressions.

[1246] The server analyzes the received feedback and incorporates it into the next exercise plan.

[1247] Examples of prompt statements

[1248] "Generate an appropriate exercise plan for a patient with lower back pain. Pain intensity is 7 / 10, duration is 2 months."

[1249] This system provides patients with personalized treatment plans and real-time progress monitoring for their treatment. It uses generative AI to enable correct exercises to be performed at home, and its unique feature is that feedback can be reflected in the next treatment.

[1250] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1251] Step 1: Enter patient information

[1252] The server generates an electronic form for entering patient information and deploys it as a web application, including details of symptoms, pain level, existing treatments, past medical history, etc. The server then sends the generated form to the terminal.

[1253] The terminal displays this form to the patient via a browser or application.

[1254] The user (patient) enters symptoms and health information into the form and presses the send button to send the data to the server.

[1255] Input: Patient's symptoms, pain level, existing treatments, past medical history

[1256] Output: Patient information sent to the server

[1257] Step 2: Analyze patient information

[1258] The server passes the patient information received from the device to the analysis module, which uses an AI algorithm to classify the patient's symptoms and create a list of suitable treatments and treatment facilities, using Python scripts and machine learning models (e.g., scikit-learn).

[1259] Input: Patient Information

[1260] Output: Analysis results (e.g., tags such as "lower back pain" and "chronic lower back pain") and a list of recommended treatments (e.g., acupuncture, stretching)

[1261] Step 3: Recommend a treatment facility and treatment plan

[1262] Based on the analysis results, the server searches for and lists the most suitable treatment facilities from a database that includes information such as the reputation, specialty, and location of the treatment facilities. It sends a query to the SQL database to retrieve information on treatment facilities that meet the criteria.

[1263] The server also uses the analysis to generate a customized treatment plan for the patient, including details on visit frequency, home exercises, and treatment methods.

[1264] The terminal displays the list of treatment facilities and treatment plan generated by the server to the patient.

[1265] The user reviews the information presented and selects the desired treatment facility.

[1266] Input: Analysis results

[1267] Output: List of treatment facilities, customized treatment plan

[1268] Step 4: Record and share your treatment progress

[1269] The device provides an interface for recording treatment progress. This interface includes fields for recording pain changes, impressions, and next treatment schedules. The interface is built using a web application using React.

[1270] The user inputs the changes in pain after treatment, their impressions, and the next treatment schedule, and sends it to the server.

[1271] The server stores the received progress data and shares it with the patient's doctor or trainer via email or in-app notifications.

[1272] Input: Treatment progress information

[1273] Output: Saved progress data, sharing notifications with your doctor or trainer

[1274] Step 5: Generative AI provides exercises

[1275] The server uses a generative AI model to design optimal exercises based on the patient's symptoms and treatment progress. The exercise content is generated by sending prompts to the generative AI model (e.g., OpenAI GPT-3). For example, the server sends the prompt, "If the patient has pain in the lower back, please generate an exercise plan appropriate for the patient. The pain intensity is 7 / 10, and the duration is 2 months."

[1276] The device displays the generated exercise video and guide to the patient, using a YouTube video link to play the video and check the exercise steps.

[1277] The user performs the exercise and provides feedback on the results and impressions to the server.

[1278] The server analyzes the received feedback and incorporates it into your next exercise plan.

[1279] Input: Patient symptoms, treatment progress, feedback

[1280] Output: Optimal exercise plan, exercise video and guide, reflection on next exercise plan

[1281] (Application example 1)

[1282] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1283] Managing employee health, especially reducing chronic physical strain such as lower back pain, is an important issue in many industries. However, conventional systems have struggled to provide individually customized treatment plans, monitor health conditions in real time, and provide appropriate exercise plans. Furthermore, they lacked the functionality to monitor employees' movements and identify areas of strain, or to record and share exercise progress in real time. This resulted in employees not receiving optimal treatment, leading to reduced work efficiency and increased health risks.

[1284] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1285] In this invention, the server includes a patient information input means, a means for receiving and analyzing patient information, a means for recommending the most suitable treatment center based on the analysis results, a means for generating a customized treatment plan for the patient, a means for sharing treatment progress in real time, a means for providing exercise demonstrations using generative AI, a means for monitoring health status, a means for monitoring the employee's movements and identifying areas of strain, and a means for recording progress after performing the recommended exercise plan. This makes it possible to monitor the employee's health status in real time, provide individually customized treatment plans, demonstrate appropriate exercises, and record progress.

[1286] The "patient information input means" is an interface for patients to input their own symptoms and health information.

[1287] The "means for receiving and analyzing patient information" is a system for receiving input patient information and analyzing the data.

[1288] The "means for recommending the most suitable treatment center" is a system for recommending a treatment center suitable for the patient based on the analysis results.

[1289] The "means for generating a customized treatment plan" is a system that creates a treatment plan specifically for a patient based on the patient's symptoms and health condition.

[1290] "Means for sharing treatment progress in real time" is a system for sharing treatment progress and status with medical professionals in real time.

[1291] A "means for providing exercise demonstrations using generative AI" is a system for using a generative AI model to provide exercise demonstrations and guidance appropriate for a patient.

[1292] "Health monitoring means" refers to a system for constantly monitoring the health status of patients or employees and detecting abnormalities.

[1293] "Means for monitoring the employee's movements and identifying areas of stress" refers to a system for monitoring the employee's movements and identifying areas of the body that are under stress.

[1294] The "means for recording progress after carrying out a recommended exercise plan" is a system for recording the progress and effects of a provided exercise plan after carrying out the plan.

[1295] This invention is a system for managing the health of factory workers, and has the functions of providing and monitoring customized treatment plans to reduce physical strain, particularly lower back pain. This system is implemented using the following hardware and software configuration.

[1296] Hardware and software used

[1297] 1. Hardware:

[1298] Factory robots (e.g., industrial robots)

[1299] Health monitoring sensors (e.g., heart rate sensors, accelerometers)

[1300] Communication devices (e.g. smartphones, tablets)

[1301] 2. Software:

[1302] Python programming language

[1303] Robot System API

[1304] Health Management Applications

[1305] Program processing and data calculation

[1306] 1. Enter and analyze patient information:

[1307] The server generates a patient (employee) information entry form, and the employee enters their symptoms and health information, including the location of the pain, its intensity, and its duration.

[1308] The terminal transmits the input information to the server.

[1309] The server analyzes the received information and suggests appropriate treatment options, using an AI algorithm to analyze and recommend treatment options based on the employee's health status.

[1310] 2. Recommendation of the best clinic and treatment plan:

[1311] Based on the analysis results, the server searches the database for the most suitable clinic and recommends it to the employee.

[1312] The server generates a customized treatment plan for the employee, including visit frequency, home exercises, and detailed treatment regimens.

[1313] The terminal displays recommended clinics and treatment plans for the employee.

[1314] 3. Real-time sharing of treatment progress:

[1315] The terminal provides an interface for employees to record treatment progress.

[1316] The user inputs the progress of treatment through the application and sends it to the server.

[1317] The server analyzes the received progress data, shares it with the patient's doctor and trainer, and modifies the treatment plan as needed.

[1318] 4. Generative AI exercises:

[1319] The server uses generative AI to design optimal exercises based on the employee's symptoms and treatment progress. This AI generates exercise demonstration videos and guides taking into account the employee's feedback and progress.

[1320] The devices show employees exercise videos and guides.

[1321] Users watch exercise videos and perform exercises at home, then receive feedback on their results and impressions after the exercise.

[1322] The server analyzes the received feedback and incorporates it into the next exercise plan.

[1323] Specific examples

[1324] For example, if "employee_1" experiences pain in his lower back and enters the pain intensity as "7 / 10," the system will process it as follows:

[1325] The server analyzes the condition as "lower back pain" and "chronic pain" and suggests treatments including stretching.

[1326] The server recommends nearby fitness clubs specializing in stretching and generates a personalized treatment plan.

[1327] Employees can use this information to receive appropriate treatment. They can also do stretches at home while watching exercise videos, and record and share their progress using the app.

[1328] Prompt Sentence Examples

[1329] "Generate an exercise plan for an employee who is experiencing lower back pain. The pain is located in the lower back and the pain intensity is 7 / 10. Please suggest some exercises."

[1330] This allows for real-time monitoring of employee health, providing individually tailored treatment plans, demonstrating appropriate exercises and recording progress.

[1331] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1332] Step 1:

[1333] Enter and submit patient information

[1334] The terminal provides an interface for employees to input symptoms and health information through a patient information input form. For example, an employee may input "lower back pain, pain intensity 7 / 10, duration 2 months" and submit the information. The input data is sent to the server by the terminal. The input data includes details of the symptoms, pain level, existing treatments, and past medical history.

[1335] Step 2:

[1336] Receiving and analyzing patient information

[1337] The server receives patient information sent from the device. The received data is passed to the analysis module, which uses an AI algorithm to classify symptoms and create a list of suitable treatments and treatment centers. For example, the analysis might recommend "lower back pain → acupuncture and stretching." Here, the AI ​​algorithm refers to past data and existing medical knowledge to suggest the optimal treatment.

[1338] Step 3:

[1339] Generate optimal clinic and treatment plan

[1340] Based on the analysis results, the server searches the database for the most suitable clinic and recommends it to the employee. The recommendation includes each clinic's rating, specialty, location, etc. At the same time, a customized treatment plan is generated. The generated treatment plan includes details of the frequency of visits, home exercises, and treatment methods. For example, it recommends visiting the clinic twice a week and stretching daily.

[1341] Step 4:

[1342] View recommendations and treatment plans

[1343] The terminal displays the generated list of recommended clinics and the treatment plan to the employee. For example, the terminal's application screen may display a "list of nearby acupuncture clinics" and a "customized treatment plan." Employees can use this information to select the most suitable clinic and act according to the treatment plan.

[1344] Step 5:

[1345] Recording and sharing treatment progress

[1346] The device provides an interface for employees to record treatment progress. Employees enter information about changes in pain after treatment, their impressions, and future plans, and send the data to the server. The server analyzes the received progress data and shares it with the patient's doctor or trainer. It also modifies the treatment plan as needed. For example, if pain does not decrease, the treatment method is reevaluated.

[1347] Step 6:

[1348] Generative AI exercises

[1349] The server uses a generative AI model to design optimal exercises based on the employee's symptoms and treatment progress. This information includes exercise demonstration videos and procedures. For example, a "back stretch" video is generated. The generative AI model optimizes the exercises by taking into account the employee's feedback and progress. An example prompt is, "Please generate an exercise plan for an employee who is experiencing lower back pain. The pain is located in the lower back and the pain intensity is 7 / 10. Please recommend some exercises."

[1350] Step 7:

[1351] Exercise execution and feedback

[1352] The device displays the generated exercise video and guide to the employee. For example, a "back stretching" video is played on a smartphone or tablet. The employee performs the exercise and then records their results and thoughts afterwards. Post-exercise feedback is sent to the server and reflected in future exercise plans.

[1353] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1354] This invention is a platform that provides individualized treatment plans and advanced treatment management for patients with lower back pain, and also combines an emotion engine that recognizes the user's emotional state and optimizes treatment. The specific program processing and its functions are described in detail below.

[1355] 1. Entering and analyzing patient information

[1356] Server: Generates and makes accessible to users a patient information entry form, including details of symptoms, pain level, existing treatments, and past medical history.

[1357] Terminal: Patients access the device through a browser or app, enter their symptoms and health information, and then press the send button to send the data to the server.

[1358] User: Enter their back pain symptoms (e.g., location of pain, intensity of pain, duration of pain, etc.) and existing treatment information into the form and press the "Submit" button.

[1359] Terminal: Sends data sent by the user in JSON format to the server.

[1360] Server: Passes the received patient information to the analysis module, which uses an artificial intelligence (AI) algorithm to classify symptoms and create a list of suitable treatments and treatment centers.

[1361] 2. Recommendation of the best clinic and treatment plan

[1362] Server: Based on the analysis results, the server searches the database for information on the most suitable clinics and creates a list. It then makes recommendations based on the reputation, specialty, location, etc. of each clinic.

[1363] Server: Generates a personalized treatment plan for the patient, including details on visit frequency, home exercises, and treatment modalities.

[1364] Terminal: Displays recommended clinics and treatment plans to patients.

[1365] User: Check the information provided and select the most suitable clinic.

[1366] 3. Real-time sharing of treatment progress

[1367] Terminal: Provides patients with an interface for recording the progress of treatment. This interface allows them to input changes in pain after treatment, their impressions, and future plans.

[1368] User: Enters the progress of treatment and sends it to the server.

[1369] Terminal: Progress data sent by the user is sent to the server in JSON format.

[1370] Server: Receives progress data sent by patients and shares it with their doctors and trainers. Comprehensively analyzes the progress data and modifies the treatment plan as needed.

[1371] 4. Generative AI exercises

[1372] Server: Using generative AI, it designs optimal exercises based on the patient's symptoms and treatment progress. This AI takes into account the patient's feedback and progress and generates exercise demonstration videos and guides.

[1373] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[1374] User: Exercise at home while watching exercise videos. After the exercise, feedback on results and impressions is provided within the app.

[1375] Server: Analyzes the received feedback and incorporates it into your next exercise plan.

[1376] 5. Emotion recognition and reflection using the emotion engine

[1377] Server: Activates the emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using the comments and data entered by the user, as well as the user's behavioral data (e.g., input speed, input content, voice data, etc.).

[1378] Device: When the user inputs their thoughts on treatment progress and exercise, the emotion engine analyzes the input data in real time and evaluates the user's emotional state.

[1379] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend relaxing exercises.

[1380] Device: Presents users with customized treatment plans and exercises that reflect their emotional state.

[1381] Specific examples

[1382] Patient information entry and analysis

[1383] User: In the lower back pain condition input form, enter "Location of pain: lower back, pain intensity: 7 / 10, duration: 2 months" and press the submit button.

[1384] Terminal: Sends input information to the server.

[1385] Server: Analyzes the received information, tags it as "lower back pain" and "chronic lower back pain," and recommends appropriate treatments (e.g., acupuncture, stretching).

[1386] Recommendation of the best clinic and treatment plan

[1387] Server: Searches the database for nearby acupuncture clinics and fitness clubs specializing in stretching, and presents the patient with a list of clinics suitable for the patient and a customized treatment plan.

[1388] User: Select the desired clinic from the list of clinics presented.

[1389] Emotion engine recognizes and reflects emotions

[1390] User: Enters a comment about treatment progress, saying, "Today the pain has eased a little, but I'm still a little anxious," and submits it.

[1391] Terminal: Sends comments to the server.

[1392] Server: The emotion engine analyzes the comments and identifies the emotion "anxiety."

[1393] Server: Based on the analysis results, a new treatment plan is generated that includes relaxing exercises.

[1394] Terminal: Displays the new treatment plan to the user.

[1395] In this way, by combining the emotion engine, it is possible to provide customized treatment that takes into consideration the patient's emotions, thereby improving the effectiveness of treatment.

[1396] The processing flow will be explained below.

[1397] Patient information entry and analysis

[1398] Step 1:

[1399] The server generates a patient information entry form and makes it accessible to the user.

[1400] Step 2:

[1401] The device displays a form in the user's browser or app and accepts input of symptoms and health information.

[1402] Step 3:

[1403] The user enters their lower back pain symptoms (e.g., location of pain, intensity of pain, duration of pain, etc.) and existing treatment information into the form and presses the "Submit" button.

[1404] Step 4:

[1405] The device sends the data sent by the user to the server in JSON format.

[1406] Step 5:

[1407] The server receives the input data and passes it to the analysis module.

[1408] Step 6:

[1409] The server's analysis module analyzes the data, classifies the symptoms, and lists suitable treatments and treatment centers.

[1410] Recommendation of the best clinic and treatment plan

[1411] Step 1:

[1412] Based on the analysis results, the server searches the database for appropriate clinic information.

[1413] Step 2:

[1414] The server collects information such as the ratings, specialties, and locations of each clinic, and creates a list of the most suitable clinics.

[1415] Step 3:

[1416] The server generates an optimized treatment plan for the patient, including the frequency of visits and specific treatment methods.

[1417] Step 4:

[1418] The terminal displays a list of recommended clinics and treatment plans to the patient.

[1419] Step 5:

[1420] The user checks the displayed information and selects the desired clinic.

[1421] Real-time sharing of treatment progress

[1422] Step 1:

[1423] The terminal provides the user with an interface for recording the progress of treatment.

[1424] Step 2:

[1425] The user enters and submits the progress of treatment (e.g., changes in pain, impressions, next appointment, etc.).

[1426] Step 3:

[1427] The device sends the progress data sent by the user to the server in JSON format.

[1428] Step 4:

[1429] The server receives the progress data and stores it in a database.

[1430] Step 5:

[1431] The server shares the saved progress data with the patient's doctor or trainer.

[1432] Step 6:

[1433] The server analyzes the progress data and modifies the treatment plan as needed.

[1434] Step 7:

[1435] The terminal notifies the user of the revised treatment plan.

[1436] Generative AI exercises

[1437] Step 1:

[1438] The server runs a generative AI engine and receives the user's health information and progress data as input.

[1439] Step 2:

[1440] The server uses generative AI to design exercises based on the patient's symptoms and condition.

[1441] Step 3:

[1442] The server generates a demonstration video and guide text for the designed exercise.

[1443] Step 4:

[1444] The device displays a link to the exercise video and guide text to the user.

[1445] Step 5:

[1446] Users watch exercise videos and follow the instructions to perform the exercises at home.

[1447] Step 6:

[1448] The user provides feedback on the results and impressions of the exercise.

[1449] Step 7:

[1450] The terminal transmits the feedback from the user to the server.

[1451] Step 8:

[1452] The server analyzes the feedback and reflects it in your next exercise plan.

[1453] Emotion engine recognizes and reflects emotions

[1454] Step 1:

[1455] The server starts an emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using the comments and data entered by the user and the user's behavioral data (e.g., input speed, input content, voice data, etc.).

[1456] Step 2:

[1457] When the device inputs the user's treatment progress and impressions of the exercises, the emotion engine analyzes this in real time and evaluates the user's emotional state.

[1458] Step 3:

[1459] The server receives the analysis results from the emotion engine and reflects them in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend exercises that will have a relaxing effect.

[1460] Step 4:

[1461] The device displays customized treatment plans and exercises to the user that reflect their emotional state.

[1462] Specific examples

[1463] Patient information entry and analysis

[1464] Step 1:

[1465] The user inputs the symptoms of lower back pain (e.g., location of pain, intensity of pain, duration of pain) and presses the submit button.

[1466] Step 2:

[1467] The terminal sends the input information to the server.

[1468] Step 3:

[1469] The server analyzes the information it receives, tags it with "lower back pain" and "chronic lower back pain," and recommends appropriate treatments and clinics.

[1470] Recommendation of the best clinic and treatment plan

[1471] Step 1:

[1472] Based on the analysis results, the server searches the database for appropriate treatment centers and creates a list of treatment centers and a treatment plan suitable for the patient.

[1473] Step 2:

[1474] The user selects the desired clinic from the displayed list of clinics and checks the specific treatment plan.

[1475] Emotion engine recognizes and reflects emotions

[1476] Step 1:

[1477] The user enters a comment about the progress of treatment, such as "Today the pain has eased a little, but I'm still a little worried," and submits it.

[1478] Step 2:

[1479] The device sends the comment to the server.

[1480] Step 3:

[1481] The server analyzes the comments using an emotion engine and identifies the emotion of "anxiety."

[1482] Step 4:

[1483] Based on the analysis results, the server generates a new treatment plan that includes relaxing exercises.

[1484] Step 5:

[1485] The device displays the new treatment plan to the user.

[1486] By combining emotion engines in this way, it is possible to provide customized treatment that takes into account the patient's emotional state, improving the effectiveness of treatment.

[1487] Example 2

[1488] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1489] Conventional lower back pain treatment systems are lacking in providing appropriate treatment plans for individual patients' symptoms and emotional states, leaving the need for improved treatment effectiveness. Furthermore, there are issues with the lack of real-time sharing of treatment progress and the provision of generative AI-based exercises, resulting in a suboptimal treatment experience.

[1490] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1491] In this invention, the server includes a patient information input means, a means for receiving and analyzing the patient information, a means for recommending an optimal treatment facility based on the analysis results, a means for generating a treatment plan customized for the patient, a means for sharing treatment progress in real time, a means for providing exercise demonstrations using a generative AI, and a means for recognizing the patient's emotional state using an emotion analysis means and optimizing the treatment plan based thereon. This makes it possible to provide an optimal treatment plan based on the patient's individual symptoms and emotional state, thereby improving treatment effectiveness and patient satisfaction.

[1492] The "patient information input means" is a means for providing an interface for patients to input their own symptoms and health information.

[1493] The "means for receiving and analyzing the patient information" refers to a means for receiving information entered by the patient on a server and using an AI algorithm or the like to classify symptoms and recommend treatment methods based on that information.

[1494] The "means for recommending the most suitable treatment facility" is a means for searching a database for the most suitable treatment facility for the patient based on the analysis results, and listing it based on information such as rating, specialty, and location.

[1495] The "means for generating a customized treatment plan" refers to a means for generating an individually optimized treatment plan based on the patient's symptoms and analysis results.

[1496] "Means for sharing treatment progress in real time" refers to a means for transmitting a patient's treatment progress data to a server and sharing it with the attending physician or trainer in real time.

[1497] "Means for providing exercise demonstrations using generative AI" refers to means for using generative AI to create an optimal exercise plan based on the patient's symptoms and progress of treatment, and providing the plan to the patient as a video or guide.

[1498] "Means for recognizing a patient's emotional state using emotion analysis means and optimizing a treatment plan based on that" refers to means for analyzing the patient's input data and behavioral data, recognizing the patient's emotional state, and dynamically optimizing a treatment plan based on that.

[1499] This invention is a platform that provides individualized treatment plans and advanced treatment management for patients with lower back pain, and also combines an emotion engine that recognizes the user's emotional state and optimizes treatment. The specific program processing and its functions are described in detail below.

[1500] Patient information entry and analysis

[1501] Server: Generates a patient information input form that users can access. This form includes details of symptoms, pain level, existing treatments, past medical history, etc. Specifically, the form is generated using HTML and CSS, and real-time data validation and submission functionality is implemented using JavaScript.

[1502] Device: Patients access the device through a browser or app and enter their symptoms and health information. Specifically, they can use a smartphone or PC browser. They then press the send button to send the data to the server.

[1503] Server: Passes the received patient information to the analysis module. This analysis module is implemented in Python and uses artificial intelligence (AI) algorithms with libraries such as TensorFlow and PyTorch to classify symptoms. It then lists candidates for appropriate treatments and treatment facilities.

[1504] Example: A user enters "Pain location is lower back, pain intensity is 7 / 10, duration is 2 months" into a form to input their lower back pain condition, and presses the submit button. The device converts the input information into JSON format and sends it to the server. The server analyzes the received information, tags it as "lower back pain" and "chronic lower back pain," and recommends appropriate treatments (e.g., acupuncture, stretching).

[1505] Recommendation of the best treatment facility and treatment plan

[1506] Server: Based on the analysis results, the server searches the database for information on the most suitable treatment facilities and creates a list. Specifically, it uses a relational database such as MySQL or PostgreSQL. It makes recommendations based on the reputation, specialty, location, etc. of each treatment facility.

[1507] Server: Generates an optimized treatment plan for the patient. This treatment plan includes details on the frequency of visits, home exercises, and treatment modalities. It is automatically generated using a Python script based on the analysis results from the AI ​​module.

[1508] Terminal: Display recommended treatment facilities and treatment plans to patients. Specifically, the data is rendered using a front-end framework (e.g., React or Vue.js).

[1509] Example: The server searches the database for nearby acupuncture clinics and fitness clubs specializing in stretching, and presents a list of treatment facilities suitable for the patient and a customized treatment plan. The user selects the desired facility from the presented list of treatment facilities.

[1510] Real-time sharing of treatment progress

[1511] Terminal: Provides an interface for recording the progress of treatment. This interface allows users to input changes in pain after treatment, their impressions, and future plans. Specifically, data entry is facilitated using a mobile application.

[1512] User: Enters the progress of treatment and sends it to the server. For example, enters information such as "The pain has eased a little."

[1513] Server: Shares the received progress data with the patient's doctor and trainer. The data is shared using cloud storage (e.g., Amazon S3). The progress data is comprehensively analyzed and the treatment plan is revised as necessary. This analysis is performed using machine learning algorithms.

[1514] Generative AI exercises

[1515] Server: Using generative AI, the server designs optimal exercises based on the patient's symptoms and treatment progress. The AI ​​takes into account the patient's feedback and progress and generates exercise demonstration videos and guides. Specifically, it uses a video generation engine (e.g., OpenAI's DALL-E or GPT-3).

[1516] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[1517] User: Exercise at home while watching exercise videos. After the exercise, feedback on results and impressions is provided within the app.

[1518] Server: Analyzes the received feedback and incorporates it into your next exercise plan.

[1519] Emotion engine recognizes and reflects emotions

[1520] Server: Launches the emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using comments and data entered by the user and their behavioral data (e.g., input speed, input content, voice data, etc.). It uses a natural language processing (NLP) library (e.g., SpaCy, NLTK).

[1521] Device: When the user inputs their thoughts on treatment progress and exercise, the emotion engine analyzes the input data in real time and evaluates the user's emotional state.

[1522] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend relaxing exercises.

[1523] Example: A user inputs and sends a comment about their treatment progress, such as "Today the pain has eased a little, but I'm still a little anxious." The device sends the comment to the server, which uses an emotion engine to analyze the comment and identify the emotion of "anxiety." Based on the analysis results, a new treatment plan including relaxation exercises is generated and the new treatment plan is displayed to the user on the device.

[1524] In this way, by combining the emotion engine, it is possible to provide customized treatment that takes into consideration the patient's emotions, thereby improving the effectiveness of treatment.

[1525] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1526] Step 1:

[1527] Server: Generates a patient information input form and makes it accessible to users. The input is a web form using HTML and CSS, and items include details of symptoms, pain level, existing treatments, and past medical history. The output is an interface that accepts user input. Specifically, it places form elements in an HTML file and implements real-time validation functionality using JavaScript.

[1528] Step 2:

[1529] Device: The patient accesses the input form through a browser or app and enters symptoms and health information. Input involves entering information such as symptoms into each field. Output involves saving the entered data on the device. Specifically, the user enters information into text boxes and drop-down menus on the form and presses the submit button.

[1530] Step 3:

[1531] Terminal: Data entered by the user is sent to the server in JSON format. Input is the information entered in each field. Output is the data converted to JSON format and sent to the server. Specifically, form data is serialized into JSON using a JavaScript function and sent to the server via an HTTP POST request.

[1532] Step 4:

[1533] Server: Passes the received patient information to the analysis module. The input is the JSON data sent to the server. The output is the symptom classification results and a list of recommended treatments and treatment facilities. Specifically, it runs a Python script and performs analysis using AI libraries such as TensorFlow and PyTorch.

[1534] Step 5:

[1535] Server: Based on the analysis results, it searches the database for information on the most suitable treatment facilities and creates a list. The input is the analysis results by AI. The output is a list of treatment facilities that are best suited to the user. Specifically, it executes an SQL query to retrieve treatment facility information from the database and filters it based on the analysis results.

[1536] Step 6:

[1537] Server: Generates a treatment plan optimized for the patient. The input is the analysis results and treatment facility information obtained from the database. The output is a customized treatment plan. Specifically, the treatment plan is automatically generated using a Python script based on the results of the AI ​​module.

[1538] Step 7:

[1539] Terminal: Displays recommended treatment facilities and treatment plans to patients. The input is the list of treatment facilities and customized treatment plans sent from the server. The output is an interface that displays them to the user. Specific operations include rendering the data using front-end frameworks such as React and Vue.js.

[1540] Step 8:

[1541] User: Check the presented information and select the most suitable treatment facility. The input is the list of treatment facilities displayed on the terminal. The output is the selected treatment facility information. Specifically, the user selects the desired treatment facility from the list and presses the decision button.

[1542] Step 9:

[1543] Terminal: Provides an interface for recording the progress of treatment. Input includes changes in pain after treatment, impressions, and future plans. Output is the progress data entered by the user. Specific operations include displaying a form with text fields and check boxes.

[1544] Step 10:

[1545] User: Enters the progress of treatment and sends it to the server. The input is the progress information of treatment. The output is the progress data sent to the server. The specific operation is to enter the progress and press the send button.

[1546] Step 11:

[1547] Terminal: The progress data sent by the user is sent to the server in JSON format. The input is the entered progress information. The output is the JSON data sent to the server. Specifically, the input content is converted to JSON and sent to the server via an HTTP POST request.

[1548] Step 12:

[1549] Server: Shares the received progress data with the doctor or trainer. The input is the progress data sent by the user. The output is the shared progress data. Specifically, it stores the progress data in a database and shares it using a relational database management system (RDBMS).

[1550] Step 13:

[1551] Server: Uses generative AI to design optimal exercises based on the patient's symptoms and treatment progress. Input is patient feedback and progress data. Output is exercise demonstration videos and guides. Specific operations include running the generative AI algorithm to generate individual exercise plans and create videos and guidelines.

[1552] Step 14:

[1553] Device: Displays exercise videos and guides to patients. The input is the generated exercise video and guide. The output is the exercise video and guide displayed in a playable format on the display screen. The specific operation is to display the generated video and guide in a playable format on a web page or within the app.

[1554] Step 15:

[1555] User: Exercises at home while watching an exercise video, and provides feedback on the results and impressions. The input is the results and impressions of the exercise. The output is feedback data sent within the app. Specifically, the user exercises while watching the video, and after finishing, they enter and send their impressions and results.

[1556] Step 16:

[1557] Server: Analyzes the received feedback and reflects it in the next exercise plan. The input is the feedback data sent by the user. The output is the next exercise plan. Specifically, the feedback data is passed to the AI ​​module for analysis and automatically reflected in the next plan.

[1558] Step 17:

[1559] Server: Launches the emotion engine and analyzes user input data and interactions. Inputs are comments and behavioral data entered by users. The output is analyzed emotion data. Specific operations include running the emotion analysis algorithm and extracting emotions from text and behavioral data.

[1560] Step 18:

[1561] Terminal: When the user inputs their thoughts on treatment progress or exercise, the emotion engine analyzes the input data in real time and evaluates their emotional state. The input is the user's thoughts and behavioral data. The output is the evaluation result of their emotional state. Specific operations include instantly analyzing the input data and interactions and determining their emotional state.

[1562] Step 19:

[1563] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. The input is the emotional data from the emotion analysis engine. The output is an optimized treatment plan. Specifically, the emotion data is passed to the treatment plan generation module, which generates a new treatment plan.

[1564] Step 20:

[1565] Terminal: Displays to the user a customized treatment plan or exercises that reflect the user's emotional state. The input is a new treatment plan sent from the server. The output is rendering the customized treatment plan or exercises on the display screen. The specific operation is to render the new treatment plan or exercises on the display screen.

[1566] (Application example 2)

[1567] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1568] Current low back pain treatments are still lacking in providing treatment plans that take into account each patient's individual symptoms and emotional state. Even for the same symptoms, the optimal treatment and treatment center may differ depending on the patient's emotions and daily life. There is also a lack of a system for sharing treatment progress in real time and providing individually customized meal plans. Therefore, there is a need for a system that can provide optimal treatment for each patient and maximize its effectiveness.

[1569] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a patient information input means, a means for receiving and analyzing patient information, a means for recommending an optimal treatment center based on the analysis results, a means for generating a treatment plan customized for the patient, a means for sharing treatment progress in real time, a means for providing exercise demonstrations using a generation AI, a means for analyzing the user's emotional state using an emotion engine and reflecting the analysis in the treatment plan, and a means for generating an optimal meal plan based on the user's emotional state and providing the meal in cooperation with a delivery service. This makes it possible to provide an optimal treatment plan that takes into account the symptoms and emotional state of each patient, share treatment progress in real time, and provide individually customized meal plans.

[1570] The "patient information input means" is an interface for patients to input their own symptoms and health information.

[1571] The "means for receiving and analyzing" is a module that receives input patient information and analyzes it using AI or other methods.

[1572] "Means for recommending the most suitable treatment center" is a function that suggests a treatment center that is suitable for the patient based on the analysis results.

[1573] A "means for generating a customized treatment plan" is a system that creates a treatment plan tailored to the symptoms and conditions of each individual patient.

[1574] "Means for sharing treatment progress in real time" is a function that allows patients and healthcare providers to check the progress of treatment in real time.

[1575] The "means for providing exercise demonstrations using generative AI" is a system that uses AI to provide patients with optimal exercise procedures and videos.

[1576] "Means of analyzing the user's emotional state using an emotion engine and reflecting it in the treatment plan" is a function that analyzes emotions based on the user's input data and behavior, and takes this into consideration and reflects it in the treatment plan.

[1577] "Means for generating optimal meal plans and providing meals in cooperation with delivery services" refers to a system that creates meal menus tailored to the user's health and emotional state and provides those meals in cooperation with delivery services.

[1578] This invention is a system including a patient information input means, a receiving and analyzing means, a recommending optimal treatment center, a generating a customized treatment plan, a sharing means for sharing treatment progress in real time, a providing exercise demonstrations using a generative AI, a means for analyzing a user's emotional state using an emotion engine and reflecting it in a treatment plan, and a generating an optimal meal plan and providing meals in cooperation with a delivery service.

[1579] Patient information entry and analysis

[1580] Server: Generates and makes accessible to users a patient information entry form, including details of symptoms, pain level, existing treatments, and past medical history.

[1581] Terminal: Patients access the terminal through a browser or app, enter their health information, and then press the send button to send the data to the server.

[1582] Server: Passes the received patient information to the analysis module, which uses an artificial intelligence (AI) algorithm to classify symptoms and create a list of suitable treatments and treatment centers.

[1583] Recommendation of the best clinic and treatment plan

[1584] Server: Based on the analysis results, the server searches the database for information on the most suitable clinics and creates a list. It then makes recommendations based on the reputation, specialty, location, etc. of each clinic.

[1585] Server: Generates a personalized treatment plan for the patient, including details on visit frequency, home exercises, and treatment modalities.

[1586] Terminal: Displays recommended clinics and treatment plans to patients.

[1587] Real-time sharing of treatment progress

[1588] Terminal: Provides patients with an interface for recording the progress of treatment. This interface allows them to input changes in pain after treatment, their impressions, and future plans.

[1589] User: Enters the progress of treatment and sends it to the server.

[1590] Server: Receives progress data sent by patients and shares it with their doctors and trainers. Comprehensively analyzes the progress data and modifies the treatment plan as needed.

[1591] Generative AI exercises

[1592] Server: Using generative AI, it designs optimal exercises based on the patient's symptoms and treatment progress. This AI takes into account the patient's feedback and progress and generates exercise videos and guides.

[1593] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[1594] User: Exercise at home while watching exercise videos. After the exercise, feedback on results and impressions is provided within the app.

[1595] Emotion engine recognizes and reflects emotions

[1596] Server: Activates the emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using the comments and data entered by the user, as well as the user's behavioral data (e.g., input speed, input content, voice data, etc.).

[1597] Device: When the user inputs their thoughts on treatment progress and exercise, the emotion engine analyzes the input data in real time and evaluates the user's emotional state.

[1598] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend relaxing exercises.

[1599] Meal plan generation and delivery service

[1600] Server: Using an emotion engine and generative AI, it generates an optimal meal plan based on the user's emotional state and symptoms. This plan takes into account nutritional balance and relaxation effects.

[1601] Device: Provides an interface where users can view meal plans within the app and order delivery with one tap.

[1602] Users: After receiving their meal, they provide feedback in the app to be reflected in their next meal plan.

[1603] Specific examples

[1604] Example of input

[1605] User: "I'm feeling a bit tired today and would like a relaxing meal."

[1606] Server: Analyzes the input information, and the emotion engine identifies "feeling tired" and "desire to relax."

[1607] Generative AI model prompt: "The user is tired and wants to relax. Please suggest recipes using ingredients that have a relaxing effect."

[1608] Results: The AI ​​suggests relaxing meals such as "chicken soup with lemongrass" and "lavender tea."

[1609] In this way, the entire system works together to provide personalized treatments and meal plans based on the user's health and emotional state.

[1610] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1611] Step 1:

[1612] The user opens the smartphone app and enters symptoms and health information into the patient information input form.

[1613] The data entered includes the location of the pain, the intensity of the pain, and the duration of the pain.

[1614] The terminal sends this input data to the server in JSON format.

[1615] Step 2:

[1616] The server passes the received JSON data to the analysis module.

[1617] This module uses AI algorithms such as TensorFlow to analyze and classify patient symptoms.

[1618] As a result of the analysis, tags such as "lower back pain" and "chronic lower back pain" are assigned.

[1619] Step 3:

[1620] The server searches the database based on the tagged analysis results and generates a list of suitable clinics.

[1621] The list includes information such as each clinic's rating, specialty, treatment features, and location.

[1622] This information is transmitted to the user terminal.

[1623] Step 4:

[1624] The terminal displays the customized treatment plan to the user along with a list of recommended clinics.

[1625] The treatment plan includes details of how often to visit, exercises to do at home, and treatment options.

[1626] The user selects the desired clinic from this list.

[1627] Step 5:

[1628] The app records the user's progress as they visit a clinic or do exercises at home and sends the information to a server.

[1629] The information entered includes changes in pain after treatment, impressions, and future plans.

[1630] The device sends this progress data to the server in JSON format.

[1631] Step 6:

[1632] The server receives the progress data and shares it with the patient's doctor or trainer.

[1633] Comprehensive analysis of progress data will determine whether the treatment plan is working or if adjustments are needed.

[1634] Modify the treatment plan as needed.

[1635] Step 7:

[1636] The server uses generative AI to design optimal exercises based on progress data and treatment status.

[1637] Exercise demonstration videos and guides are generated and sent to the user's device.

[1638] Step 8:

[1639] Users can exercise at home by watching exercise videos and guides.

[1640] After the exercise, feedback on results and impressions is provided within the app, and the data is sent to the server.

[1641] Step 9:

[1642] The server runs the emotion engine and analyzes the user's input data and interactions.

[1643] The system recognizes the user's emotional state (e.g., "stressed" or "relaxed") from comments, input data, voice data, etc.

[1644] Emotional data is also reflected in optimizing treatment plans.

[1645] Step 10:

[1646] The server and generating AI generate an optimal meal plan based on the user's emotional state.

[1647] For example, if you feel tired and want to relax, we will suggest a meal plan using ingredients that have a relaxing effect.

[1648] Step 11:

[1649] The device displays a customized meal plan to the user and allows them to order delivery with just one tap.

[1650] After receiving their meal, users provide feedback within the app and send the data to be reflected in their next plan.

[1651] By following these steps, the entire system works together to provide optimal treatment and meal plans that take into account each user's individual symptoms and emotional state.

[1652] 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.

[1653] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1654] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1655] [Fourth embodiment]

[1656] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1657] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1658] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1659] 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.

[1660] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1661] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1662] 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.

[1663] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1664] 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.

[1665] 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 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.

[1666] 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.

[1667] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1668] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1669] This invention is a platform that provides individualized treatment plans and advanced treatment management for patients with lower back pain. Specific program processing and its functions will be described in detail below.

[1670] 1. Entering and analyzing patient information

[1671] Server: Generates and makes accessible to patients a patient information entry form, including details of symptoms, pain level, existing treatments, and past medical history.

[1672] Terminal: Patients access the device through a browser or app, enter their symptoms and health information, and then press the send button to send the data to the server.

[1673] Server: Passes the received patient information to the analysis module, which uses an artificial intelligence (AI) algorithm to classify symptoms and create a list of suitable treatments and treatment centers.

[1674] 2. Recommendation of the best clinic and treatment plan

[1675] Server: Based on the analysis results, the server searches the database for information on the most suitable clinics and creates a list. It then makes recommendations based on the reputation, specialty, location, etc. of each clinic.

[1676] Server: Generates a personalized treatment plan for the patient, including details on visit frequency, home exercises, and treatment modalities.

[1677] Terminal: Displays recommended clinics and customized treatment plans to patients.

[1678] User: Check the information provided and select the most suitable clinic.

[1679] 3. Real-time sharing of treatment progress

[1680] Terminal: Provides patients with an interface for recording the progress of treatment. This interface allows them to input changes in pain after treatment, their impressions, and future plans.

[1681] User: Enters the progress of treatment and sends it to the server.

[1682] Server: Receives progress data sent by patients and shares it with their doctors and trainers. Comprehensively analyzes the progress data and modifies the treatment plan as needed.

[1683] 4. Generative AI exercises

[1684] Server: Using generative AI, it designs optimal exercises based on the patient's symptoms and treatment progress. This AI takes into account the patient's feedback and progress and generates exercise demonstration videos and guides.

[1685] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[1686] User: Exercise at home while watching exercise videos. After the exercise, provide feedback on the results and impressions.

[1687] Server: Analyzes the received feedback and incorporates it into your next exercise plan.

[1688] Specific examples

[1689] Patient information entry and analysis

[1690] The user enters the symptoms of lower back pain as "Location of pain: lower back, pain intensity: 7 / 10, duration: 2 months" and presses the send button.

[1691] The terminal sends the information to the server.

[1692] The server analyzes the received information, tags it as "lower back pain" and "chronic lower back pain," and recommends appropriate treatments (e.g., acupuncture, stretching).

[1693] Recommendation of the best clinic and treatment plan

[1694] The server searches the database for nearby acupuncture clinics and fitness clubs specializing in stretching, and presents the patient with a list of clinics suitable for the patient and a customized treatment plan.

[1695] The user selects the desired clinic from the list of recommended clinics and checks the specific treatment plan.

[1696] This allows the system to provide more effective and personalized treatment to patients with lower back pain, monitor their progress in real time, and utilizes generative AI to help patients perform the correct exercises at home.

[1697] The processing flow will be explained below.

[1698] Patient information entry and analysis

[1699] Step 1:

[1700] The server generates a patient information entry form and makes it accessible to the user.

[1701] Step 2:

[1702] The device displays a form in the user's browser or app and accepts input of symptoms and health information.

[1703] Step 3:

[1704] The user enters their lower back pain symptoms (e.g., location of pain, intensity of pain, duration of pain, etc.) and existing treatment information into the form and presses the "Submit" button.

[1705] Step 4:

[1706] The device sends the data sent by the user to the server in JSON format.

[1707] Step 5:

[1708] The server receives the input data and passes it to the analysis module.

[1709] Step 6:

[1710] The server's analysis module analyzes the data, classifies the symptoms, and lists suitable treatments and treatment centers.

[1711] Recommendation of the best clinic and treatment plan

[1712] Step 1:

[1713] Based on the analysis results, the server searches the database for appropriate clinic information.

[1714] Step 2:

[1715] The server collects information such as the ratings, specialties, and locations of each clinic, and creates a list of the most suitable clinics.

[1716] Step 3:

[1717] The server generates an optimized treatment plan for the patient, including the frequency of visits and specific treatment methods.

[1718] Step 4:

[1719] The terminal displays a list of recommended clinics and treatment plans to the patient.

[1720] Step 5:

[1721] The user checks the displayed information and selects the desired clinic.

[1722] Real-time sharing of treatment progress

[1723] Step 1:

[1724] The terminal provides the user with an interface for recording the progress of treatment.

[1725] Step 2:

[1726] The user enters and submits the progress of treatment (e.g., changes in pain, impressions, next appointment, etc.).

[1727] Step 3:

[1728] The terminal transmits the progress data transmitted by the user to the server.

[1729] Step 4:

[1730] The server receives the progress data and stores it in a database.

[1731] Step 5:

[1732] The server shares the saved progress data with the patient's doctor or trainer.

[1733] Step 6:

[1734] The server analyzes the progress data and modifies the treatment plan as needed.

[1735] Step 7:

[1736] The terminal notifies the user of the revised treatment plan.

[1737] Generative AI exercises

[1738] Step 1:

[1739] The server runs a generative AI engine and receives the user's health information and progress data as input.

[1740] Step 2:

[1741] The server uses generative AI to design exercises based on the patient's symptoms and condition.

[1742] Step 3:

[1743] The server generates a demonstration video and guide text for the designed exercise.

[1744] Step 4:

[1745] The device displays a link to the exercise video and guide text to the user.

[1746] Step 5:

[1747] Users watch exercise videos and follow the instructions to perform the exercises at home.

[1748] Step 6:

[1749] The user provides feedback on the results and impressions of the exercise.

[1750] Step 7:

[1751] The terminal transmits the feedback from the user to the server.

[1752] Step 8:

[1753] The server analyzes the feedback and reflects it in your next exercise plan.

[1754] In this way, the system performs specific operations at each processing step to help patients with lower back pain receive more effective treatment.

[1755] Example 1

[1756] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1757] In current systems for treating lower back pain, patient information entry, treatment plan generation, and treatment progress management are all performed separately, with no integrated management method available. This makes it difficult to recommend the most suitable treatment center for a patient or provide an individualized treatment plan, preventing improvements in treatment effectiveness. Real-time patient progress management is also difficult, limiting the provision of home exercises and the incorporation of feedback. A system that solves these issues is needed.

[1758] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1759] In this invention, the server includes a patient information input unit, a unit for receiving and analyzing the patient information, and a unit for analyzing the patient's feedback using a generative AI model and reflecting it in the next exercise plan, which enables integrated treatment management, not only realizing personalized treatment plans and recommendations of optimal treatment facilities, but also enabling real-time treatment progress management, providing exercises at home, and reflecting feedback.

[1760] "Patient information input means" refers to a means for providing an electronic form for patients to input their symptoms and health information.

[1761] The "analysis means" is a means of classifying the patient's symptoms using an artificial intelligence (AI) algorithm based on the received patient information and listing appropriate treatments and treatment facilities.

[1762] The "treatment facility recommendation means" is a means for searching for and recommending the most suitable treatment facility based on the analysis results.

[1763] The "treatment plan generating means" is a means for generating a customized treatment plan suitable for the patient based on the analysis results.

[1764] A "progress sharing tool" is a means for sharing a patient's treatment progress in real time with their doctor or trainer.

[1765] A "generative AI model means" is a means for using a generative AI model to design appropriate exercises and provide them to a patient.

[1766] The "feedback analysis means" is a means for analyzing the results and impressions of the exercise received from the patient and reflecting them in the next exercise plan.

[1767] The present invention is a platform that provides personalized treatment plans and advanced treatment management for patients with lower back pain. Detailed embodiments of the system are described below.

[1768] Entering patient information

[1769] The server generates an electronic form for entering patient information, including details of the patient's symptoms, pain level, existing treatments, past medical history, etc. For example, it uses an HTML form and is deployed as a web application.

[1770] The terminal displays this form to the patient via a browser or application.

[1771] The user (patient) enters symptoms and health information into the form and presses the send button to send the data to the server.

[1772] Patient information analysis

[1773] The server receives the patient information sent from the terminal and passes it to the analysis module.

[1774] This analysis module uses, for example, Python scripts and machine learning models (e.g., scikit-learn) to classify a patient's symptoms using AI algorithms and shortlist suitable treatments and treatment facilities.

[1775] Specifically, based on the information received, the analysis module tags the data with terms such as "lower back pain" or "chronic lower back pain" and recommends treatments such as acupuncture and stretching.

[1776] Recommendation of clinics and treatment plans

[1777] The server then searches for and lists the most suitable treatment facilities from a database based on the analysis results. The database includes information such as the facility's reputation, specialty, and location. For example, the server queries an SQL database to obtain information on treatment facilities that meet the criteria.

[1778] The server also uses the analysis to generate a customized treatment plan for the patient, including details on visit frequency, home exercises, and treatment methods.

[1779] The terminal displays recommended treatment facilities and customized treatment plans for the patient.

[1780] The user reviews the information presented and selects the desired treatment facility.

[1781] Recording and sharing treatment progress

[1782] The device provides an interface for recording treatment progress. This interface allows patients to enter changes in pain after treatment, their impressions, and the next treatment schedule. For example, the interface can be built using a web application using React.

[1783] The user inputs the treatment progress status and sends it to the server.

[1784] The server receives the progress data and shares it with the patient's doctor or trainer via email or in-app notifications.

[1785] Generative AI exercises

[1786] The server uses generative AI to design optimal exercises based on the patient's symptoms and treatment progress. For example, it sends prompts to a generative AI model (e.g., OpenAI GPT-3) to generate exercise content.

[1787] The device displays the generated exercise video and guide to the patient, possibly using a YouTube video link to play the video and review the exercise steps.

[1788] The user performs the exercises and provides feedback on their results and impressions.

[1789] The server analyzes the received feedback and incorporates it into the next exercise plan.

[1790] Examples of prompt statements

[1791] "Generate an appropriate exercise plan for a patient with lower back pain. Pain intensity is 7 / 10, duration is 2 months."

[1792] This system provides patients with personalized treatment plans and real-time progress monitoring for their treatment. It uses generative AI to enable correct exercises to be performed at home, and its unique feature is that feedback can be reflected in the next treatment.

[1793] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1794] Step 1: Enter patient information

[1795] The server generates an electronic form for entering patient information and deploys it as a web application, including details of symptoms, pain level, existing treatments, past medical history, etc. The server then sends the generated form to the terminal.

[1796] The terminal displays this form to the patient via a browser or application.

[1797] The user (patient) enters symptoms and health information into the form and presses the send button to send the data to the server.

[1798] Input: Patient's symptoms, pain level, existing treatments, past medical history

[1799] Output: Patient information sent to the server

[1800] Step 2: Analyze patient information

[1801] The server passes the patient information received from the device to the analysis module, which uses an AI algorithm to classify the patient's symptoms and create a list of suitable treatments and treatment facilities, using Python scripts and machine learning models (e.g., scikit-learn).

[1802] Input: Patient Information

[1803] Output: Analysis results (e.g., tags such as "lower back pain" and "chronic lower back pain") and a list of recommended treatments (e.g., acupuncture, stretching)

[1804] Step 3: Recommend a treatment facility and treatment plan

[1805] Based on the analysis results, the server searches for and lists the most suitable treatment facilities from a database that includes information such as the reputation, specialty, and location of the treatment facilities. It sends a query to the SQL database to retrieve information on treatment facilities that meet the criteria.

[1806] The server also uses the analysis to generate a customized treatment plan for the patient, including details on visit frequency, home exercises, and treatment methods.

[1807] The terminal displays the list of treatment facilities and treatment plan generated by the server to the patient.

[1808] The user reviews the information presented and selects the desired treatment facility.

[1809] Input: Analysis results

[1810] Output: List of treatment facilities, customized treatment plan

[1811] Step 4: Record and share your treatment progress

[1812] The device provides an interface for recording treatment progress. This interface includes fields for recording pain changes, impressions, and next treatment schedules. The interface is built using a web application using React.

[1813] The user inputs the changes in pain after treatment, their impressions, and the next treatment schedule, and sends it to the server.

[1814] The server stores the received progress data and shares it with the patient's doctor or trainer via email or in-app notifications.

[1815] Input: Treatment progress information

[1816] Output: Saved progress data, sharing notifications with your doctor or trainer

[1817] Step 5: Generative AI provides exercises

[1818] The server uses a generative AI model to design optimal exercises based on the patient's symptoms and treatment progress. The exercise content is generated by sending prompts to the generative AI model (e.g., OpenAI GPT-3). For example, the server sends the prompt, "If the patient has pain in the lower back, please generate an exercise plan appropriate for the patient. The pain intensity is 7 / 10, and the duration is 2 months."

[1819] The device displays the generated exercise video and guide to the patient, using a YouTube video link to play the video and check the exercise steps.

[1820] The user performs the exercise and provides feedback on the results and impressions to the server.

[1821] The server analyzes the received feedback and incorporates it into your next exercise plan.

[1822] Input: Patient symptoms, treatment progress, feedback

[1823] Output: Optimal exercise plan, exercise video and guide, reflection on next exercise plan

[1824] (Application example 1)

[1825] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1826] Managing employee health, especially reducing chronic physical strain such as lower back pain, is an important issue in many industries. However, conventional systems have struggled to provide individually customized treatment plans, monitor health conditions in real time, and provide appropriate exercise plans. Furthermore, they lacked the functionality to monitor employees' movements and identify areas of strain, or to record and share exercise progress in real time. This resulted in employees not receiving optimal treatment, leading to reduced work efficiency and increased health risks.

[1827] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1828] In this invention, the server includes a patient information input means, a means for receiving and analyzing patient information, a means for recommending the most suitable treatment center based on the analysis results, a means for generating a customized treatment plan for the patient, a means for sharing treatment progress in real time, a means for providing exercise demonstrations using generative AI, a means for monitoring health status, a means for monitoring the employee's movements and identifying areas of strain, and a means for recording progress after performing the recommended exercise plan. This makes it possible to monitor the employee's health status in real time, provide individually customized treatment plans, demonstrate appropriate exercises, and record progress.

[1829] The "patient information input means" is an interface for patients to input their own symptoms and health information.

[1830] The "means for receiving and analyzing patient information" is a system for receiving input patient information and analyzing the data.

[1831] The "means for recommending the most suitable treatment center" is a system for recommending a treatment center suitable for the patient based on the analysis results.

[1832] The "means for generating a customized treatment plan" is a system that creates a treatment plan specifically for a patient based on the patient's symptoms and health condition.

[1833] "Means for sharing treatment progress in real time" is a system for sharing treatment progress and status with medical professionals in real time.

[1834] A "means for providing exercise demonstrations using generative AI" is a system for using a generative AI model to provide exercise demonstrations and guidance appropriate for a patient.

[1835] "Health monitoring means" refers to a system for constantly monitoring the health status of patients or employees and detecting abnormalities.

[1836] "Means for monitoring the employee's movements and identifying areas of stress" refers to a system for monitoring the employee's movements and identifying areas of the body that are under stress.

[1837] The "means for recording progress after carrying out a recommended exercise plan" is a system for recording the progress and effects of a provided exercise plan after carrying out the plan.

[1838] This invention is a system for managing the health of factory workers, and has the functions of providing and monitoring customized treatment plans to reduce physical strain, particularly lower back pain. This system is implemented using the following hardware and software configuration.

[1839] Hardware and software used

[1840] 1. Hardware:

[1841] Factory robots (e.g., industrial robots)

[1842] Health monitoring sensors (e.g., heart rate sensors, accelerometers)

[1843] Communication devices (e.g. smartphones, tablets)

[1844] 2. Software:

[1845] Python programming language

[1846] Robot System API

[1847] Health Management Applications

[1848] Program processing and data calculation

[1849] 1. Enter and analyze patient information:

[1850] The server generates a patient (employee) information entry form, and the employee enters their symptoms and health information, including the location of the pain, its intensity, and its duration.

[1851] The terminal transmits the input information to the server.

[1852] The server analyzes the received information and suggests appropriate treatment options, using an AI algorithm to analyze and recommend treatment options based on the employee's health status.

[1853] 2. Recommendation of the best clinic and treatment plan:

[1854] Based on the analysis results, the server searches the database for the most suitable clinic and recommends it to the employee.

[1855] The server generates a customized treatment plan for the employee, including visit frequency, home exercises, and detailed treatment regimens.

[1856] The terminal displays recommended clinics and treatment plans for the employee.

[1857] 3. Real-time sharing of treatment progress:

[1858] The terminal provides an interface for employees to record treatment progress.

[1859] The user inputs the progress of treatment through the application and sends it to the server.

[1860] The server analyzes the received progress data, shares it with the patient's doctor and trainer, and modifies the treatment plan as needed.

[1861] 4. Generative AI exercises:

[1862] The server uses generative AI to design optimal exercises based on the employee's symptoms and treatment progress. This AI generates exercise demonstration videos and guides taking into account the employee's feedback and progress.

[1863] The devices show employees exercise videos and guides.

[1864] Users watch exercise videos and perform exercises at home, then receive feedback on their results and impressions after the exercise.

[1865] The server analyzes the received feedback and incorporates it into the next exercise plan.

[1866] Specific examples

[1867] For example, if "employee_1" experiences pain in his lower back and enters the pain intensity as "7 / 10," the system will process it as follows:

[1868] The server analyzes the condition as "lower back pain" and "chronic pain" and suggests treatments including stretching.

[1869] The server recommends nearby fitness clubs specializing in stretching and generates a personalized treatment plan.

[1870] Employees can use this information to receive appropriate treatment. They can also do stretches at home while watching exercise videos, and record and share their progress using the app.

[1871] Prompt Sentence Examples

[1872] "Generate an exercise plan for an employee who is experiencing lower back pain. The pain is located in the lower back and the pain intensity is 7 / 10. Please suggest some exercises."

[1873] This allows for real-time monitoring of employee health, providing individually tailored treatment plans, demonstrating appropriate exercises and recording progress.

[1874] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1875] Step 1:

[1876] Enter and submit patient information

[1877] The terminal provides an interface for employees to input symptoms and health information through a patient information input form. For example, an employee may input "lower back pain, pain intensity 7 / 10, duration 2 months" and submit the information. The input data is sent to the server by the terminal. The input data includes details of the symptoms, pain level, existing treatments, and past medical history.

[1878] Step 2:

[1879] Receiving and analyzing patient information

[1880] The server receives patient information sent from the device. The received data is passed to the analysis module, which uses an AI algorithm to classify symptoms and create a list of suitable treatments and treatment centers. For example, the analysis might recommend "lower back pain → acupuncture and stretching." Here, the AI ​​algorithm refers to past data and existing medical knowledge to suggest the optimal treatment.

[1881] Step 3:

[1882] Generate optimal clinic and treatment plan

[1883] Based on the analysis results, the server searches the database for the most suitable clinic and recommends it to the employee. The recommendation includes each clinic's rating, specialty, location, etc. At the same time, a customized treatment plan is generated. The generated treatment plan includes details of the frequency of visits, home exercises, and treatment methods. For example, it recommends visiting the clinic twice a week and stretching daily.

[1884] Step 4:

[1885] View recommendations and treatment plans

[1886] The terminal displays the generated list of recommended clinics and the treatment plan to the employee. For example, the terminal's application screen may display a "list of nearby acupuncture clinics" and a "customized treatment plan." Employees can use this information to select the most suitable clinic and act according to the treatment plan.

[1887] Step 5:

[1888] Recording and sharing treatment progress

[1889] The device provides an interface for employees to record treatment progress. Employees enter information about changes in pain after treatment, their impressions, and future plans, and send the data to the server. The server analyzes the received progress data and shares it with the patient's doctor or trainer. It also modifies the treatment plan as needed. For example, if pain does not decrease, the treatment method is reevaluated.

[1890] Step 6:

[1891] Generative AI exercises

[1892] The server uses a generative AI model to design optimal exercises based on the employee's symptoms and treatment progress. This information includes exercise demonstration videos and procedures. For example, a "back stretch" video is generated. The generative AI model optimizes the exercises by taking into account the employee's feedback and progress. An example prompt is, "Please generate an exercise plan for an employee who is experiencing lower back pain. The pain is located in the lower back and the pain intensity is 7 / 10. Please recommend some exercises."

[1893] Step 7:

[1894] Exercise execution and feedback

[1895] The device displays the generated exercise video and guide to the employee. For example, a "back stretching" video is played on a smartphone or tablet. The employee performs the exercise and then records their results and thoughts afterwards. Post-exercise feedback is sent to the server and reflected in future exercise plans.

[1896] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1897] This invention is a platform that provides individualized treatment plans and advanced treatment management for patients with lower back pain, and also combines an emotion engine that recognizes the user's emotional state and optimizes treatment. The specific program processing and its functions are described in detail below.

[1898] 1. Entering and analyzing patient information

[1899] Server: Generates and makes accessible to users a patient information entry form, including details of symptoms, pain level, existing treatments, and past medical history.

[1900] Terminal: Patients access the device through a browser or app, enter their symptoms and health information, and then press the send button to send the data to the server.

[1901] User: Enter their back pain symptoms (e.g., location of pain, intensity of pain, duration of pain, etc.) and existing treatment information into the form and press the "Submit" button.

[1902] Terminal: Sends data sent by the user in JSON format to the server.

[1903] Server: Passes the received patient information to the analysis module, which uses an artificial intelligence (AI) algorithm to classify symptoms and create a list of suitable treatments and treatment centers.

[1904] 2. Recommendation of the best clinic and treatment plan

[1905] Server: Based on the analysis results, the server searches the database for information on the most suitable clinics and creates a list. It then makes recommendations based on the reputation, specialty, location, etc. of each clinic.

[1906] Server: Generates a personalized treatment plan for the patient, including details on visit frequency, home exercises, and treatment modalities.

[1907] Terminal: Displays recommended clinics and treatment plans to patients.

[1908] User: Check the information provided and select the most suitable clinic.

[1909] 3. Real-time sharing of treatment progress

[1910] Terminal: Provides patients with an interface for recording the progress of treatment. This interface allows them to input changes in pain after treatment, their impressions, and future plans.

[1911] User: Enters the progress of treatment and sends it to the server.

[1912] Terminal: Progress data sent by the user is sent to the server in JSON format.

[1913] Server: Receives progress data sent by patients and shares it with their doctors and trainers. Comprehensively analyzes the progress data and modifies the treatment plan as needed.

[1914] 4. Generative AI exercises

[1915] Server: Using generative AI, it designs optimal exercises based on the patient's symptoms and treatment progress. This AI takes into account the patient's feedback and progress and generates exercise demonstration videos and guides.

[1916] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[1917] User: Exercise at home while watching exercise videos. After the exercise, feedback on results and impressions is provided within the app.

[1918] Server: Analyzes the received feedback and incorporates it into your next exercise plan.

[1919] 5. Emotion recognition and reflection using the emotion engine

[1920] Server: Activates the emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using the comments and data entered by the user, as well as the user's behavioral data (e.g., input speed, input content, voice data, etc.).

[1921] Device: When the user inputs their thoughts on treatment progress and exercise, the emotion engine analyzes the input data in real time and evaluates the user's emotional state.

[1922] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend relaxing exercises.

[1923] Device: Presents users with customized treatment plans and exercises that reflect their emotional state.

[1924] Specific examples

[1925] Patient information entry and analysis

[1926] User: In the lower back pain condition input form, enter "Location of pain: lower back, pain intensity: 7 / 10, duration: 2 months" and press the submit button.

[1927] Terminal: Sends input information to the server.

[1928] Server: Analyzes the received information, tags it as "lower back pain" and "chronic lower back pain," and recommends appropriate treatments (e.g., acupuncture, stretching).

[1929] Recommendation of the best clinic and treatment plan

[1930] Server: Searches the database for nearby acupuncture clinics and fitness clubs specializing in stretching, and presents the patient with a list of clinics suitable for the patient and a customized treatment plan.

[1931] User: Select the desired clinic from the list of clinics presented.

[1932] Emotion engine recognizes and reflects emotions

[1933] User: Enters a comment about treatment progress, saying, "Today the pain has eased a little, but I'm still a little anxious," and submits it.

[1934] Terminal: Sends comments to the server.

[1935] Server: The emotion engine analyzes the comments and identifies the emotion "anxiety."

[1936] Server: Based on the analysis results, a new treatment plan is generated that includes relaxing exercises.

[1937] Terminal: Displays the new treatment plan to the user.

[1938] In this way, by combining the emotion engine, it is possible to provide customized treatment that takes into consideration the patient's emotions, thereby improving the effectiveness of treatment.

[1939] The processing flow will be explained below.

[1940] Patient information entry and analysis

[1941] Step 1:

[1942] The server generates a patient information entry form and makes it accessible to the user.

[1943] Step 2:

[1944] The device displays a form in the user's browser or app and accepts input of symptoms and health information.

[1945] Step 3:

[1946] The user enters their lower back pain symptoms (e.g., location of pain, intensity of pain, duration of pain, etc.) and existing treatment information into the form and presses the "Submit" button.

[1947] Step 4:

[1948] The device sends the data sent by the user to the server in JSON format.

[1949] Step 5:

[1950] The server receives the input data and passes it to the analysis module.

[1951] Step 6:

[1952] The server's analysis module analyzes the data, classifies the symptoms, and lists suitable treatments and treatment centers.

[1953] Recommendation of the best clinic and treatment plan

[1954] Step 1:

[1955] Based on the analysis results, the server searches the database for appropriate clinic information.

[1956] Step 2:

[1957] The server collects information such as the ratings, specialties, and locations of each clinic, and creates a list of the most suitable clinics.

[1958] Step 3:

[1959] The server generates an optimized treatment plan for the patient, including the frequency of visits and specific treatment methods.

[1960] Step 4:

[1961] The terminal displays a list of recommended clinics and treatment plans to the patient.

[1962] Step 5:

[1963] The user checks the displayed information and selects the desired clinic.

[1964] Real-time sharing of treatment progress

[1965] Step 1:

[1966] The terminal provides the user with an interface for recording the progress of treatment.

[1967] Step 2:

[1968] The user enters and submits the progress of treatment (e.g., changes in pain, impressions, next appointment, etc.).

[1969] Step 3:

[1970] The device sends the progress data sent by the user to the server in JSON format.

[1971] Step 4:

[1972] The server receives the progress data and stores it in a database.

[1973] Step 5:

[1974] The server shares the saved progress data with the patient's doctor or trainer.

[1975] Step 6:

[1976] The server analyzes the progress data and modifies the treatment plan as needed.

[1977] Step 7:

[1978] The terminal notifies the user of the revised treatment plan.

[1979] Generative AI exercises

[1980] Step 1:

[1981] The server runs a generative AI engine and receives the user's health information and progress data as input.

[1982] Step 2:

[1983] The server uses generative AI to design exercises based on the patient's symptoms and condition.

[1984] Step 3:

[1985] The server generates a demonstration video and guide text for the designed exercise.

[1986] Step 4:

[1987] The device displays a link to the exercise video and guide text to the user.

[1988] Step 5:

[1989] Users watch exercise videos and follow the instructions to perform the exercises at home.

[1990] Step 6:

[1991] The user provides feedback on the results and impressions of the exercise.

[1992] Step 7:

[1993] The terminal transmits the feedback from the user to the server.

[1994] Step 8:

[1995] The server analyzes the feedback and reflects it in your next exercise plan.

[1996] Emotion engine recognizes and reflects emotions

[1997] Step 1:

[1998] The server starts an emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using the comments and data entered by the user and the user's behavioral data (e.g., input speed, input content, voice data, etc.).

[1999] Step 2:

[2000] When the device inputs the user's treatment progress and impressions of the exercises, the emotion engine analyzes this in real time and evaluates the user's emotional state.

[2001] Step 3:

[2002] The server receives the analysis results from the emotion engine and reflects them in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend exercises that will have a relaxing effect.

[2003] Step 4:

[2004] The device displays customized treatment plans and exercises to the user that reflect their emotional state.

[2005] Specific examples

[2006] Patient information entry and analysis

[2007] Step 1:

[2008] The user inputs the symptoms of lower back pain (e.g., location of pain, intensity of pain, duration of pain) and presses the submit button.

[2009] Step 2:

[2010] The terminal sends the input information to the server.

[2011] Step 3:

[2012] The server analyzes the information it receives, tags it with "lower back pain" and "chronic lower back pain," and recommends appropriate treatments and clinics.

[2013] Recommendation of the best clinic and treatment plan

[2014] Step 1:

[2015] Based on the analysis results, the server searches the database for appropriate treatment centers and creates a list of treatment centers and a treatment plan suitable for the patient.

[2016] Step 2:

[2017] The user selects the desired clinic from the displayed list of clinics and checks the specific treatment plan.

[2018] Emotion engine recognizes and reflects emotions

[2019] Step 1:

[2020] The user enters a comment about the progress of treatment, such as "Today the pain has eased a little, but I'm still a little worried," and submits it.

[2021] Step 2:

[2022] The device sends the comment to the server.

[2023] Step 3:

[2024] The server analyzes the comments using an emotion engine and identifies the emotion of "anxiety."

[2025] Step 4:

[2026] Based on the analysis results, the server generates a new treatment plan that includes relaxing exercises.

[2027] Step 5:

[2028] The device displays the new treatment plan to the user.

[2029] By combining emotion engines in this way, it is possible to provide customized treatment that takes into account the patient's emotional state, improving the effectiveness of treatment.

[2030] Example 2

[2031] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2032] Conventional lower back pain treatment systems are lacking in providing appropriate treatment plans for individual patients' symptoms and emotional states, leaving the need for improved treatment effectiveness. Furthermore, there are issues with the lack of real-time sharing of treatment progress and the provision of generative AI-based exercises, resulting in a suboptimal treatment experience.

[2033] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2034] In this invention, the server includes a patient information input means, a means for receiving and analyzing the patient information, a means for recommending an optimal treatment facility based on the analysis results, a means for generating a treatment plan customized for the patient, a means for sharing treatment progress in real time, a means for providing exercise demonstrations using a generative AI, and a means for recognizing the patient's emotional state using an emotion analysis means and optimizing the treatment plan based thereon. This makes it possible to provide an optimal treatment plan based on the patient's individual symptoms and emotional state, thereby improving treatment effectiveness and patient satisfaction.

[2035] The "patient information input means" is a means for providing an interface for patients to input their own symptoms and health information.

[2036] The "means for receiving and analyzing the patient information" refers to a means for receiving information entered by the patient on a server and using an AI algorithm or the like to classify symptoms and recommend treatment methods based on that information.

[2037] The "means for recommending the most suitable treatment facility" is a means for searching a database for the most suitable treatment facility for the patient based on the analysis results, and listing it based on information such as rating, specialty, and location.

[2038] The "means for generating a customized treatment plan" refers to a means for generating an individually optimized treatment plan based on the patient's symptoms and analysis results.

[2039] "Means for sharing treatment progress in real time" refers to a means for transmitting a patient's treatment progress data to a server and sharing it with the attending physician or trainer in real time.

[2040] "Means for providing exercise demonstrations using generative AI" refers to means for using generative AI to create an optimal exercise plan based on the patient's symptoms and progress of treatment, and providing the plan to the patient as a video or guide.

[2041] "Means for recognizing a patient's emotional state using emotion analysis means and optimizing a treatment plan based on that" refers to means for analyzing the patient's input data and behavioral data, recognizing the patient's emotional state, and dynamically optimizing a treatment plan based on that.

[2042] This invention is a platform that provides individualized treatment plans and advanced treatment management for patients with lower back pain, and also combines an emotion engine that recognizes the user's emotional state and optimizes treatment. The specific program processing and its functions are described in detail below.

[2043] Patient information entry and analysis

[2044] Server: Generates a patient information input form that users can access. This form includes details of symptoms, pain level, existing treatments, past medical history, etc. Specifically, the form is generated using HTML and CSS, and real-time data validation and submission functionality is implemented using JavaScript.

[2045] Device: Patients access the device through a browser or app and enter their symptoms and health information. Specifically, they can use a smartphone or PC browser. They then press the send button to send the data to the server.

[2046] Server: Passes the received patient information to the analysis module. This analysis module is implemented in Python and uses artificial intelligence (AI) algorithms with libraries such as TensorFlow and PyTorch to classify symptoms. It then lists candidates for appropriate treatments and treatment facilities.

[2047] Example: A user enters "Pain location is lower back, pain intensity is 7 / 10, duration is 2 months" into a form to input their lower back pain condition, and presses the submit button. The device converts the input information into JSON format and sends it to the server. The server analyzes the received information, tags it as "lower back pain" and "chronic lower back pain," and recommends appropriate treatments (e.g., acupuncture, stretching).

[2048] Recommendation of the best treatment facility and treatment plan

[2049] Server: Based on the analysis results, the server searches the database for information on the most suitable treatment facilities and creates a list. Specifically, it uses a relational database such as MySQL or PostgreSQL. It makes recommendations based on the reputation, specialty, location, etc. of each treatment facility.

[2050] Server: Generates an optimized treatment plan for the patient. This treatment plan includes details on the frequency of visits, home exercises, and treatment modalities. It is automatically generated using a Python script based on the analysis results from the AI ​​module.

[2051] Terminal: Display recommended treatment facilities and treatment plans to patients. Specifically, the data is rendered using a front-end framework (e.g., React or Vue.js).

[2052] Example: The server searches the database for nearby acupuncture clinics and fitness clubs specializing in stretching, and presents a list of treatment facilities suitable for the patient and a customized treatment plan. The user selects the desired facility from the presented list of treatment facilities.

[2053] Real-time sharing of treatment progress

[2054] Terminal: Provides an interface for recording the progress of treatment. This interface allows users to input changes in pain after treatment, their impressions, and future plans. Specifically, data entry is facilitated using a mobile application.

[2055] User: Enters the progress of treatment and sends it to the server. For example, enters information such as "The pain has eased a little."

[2056] Server: Shares the received progress data with the patient's doctor and trainer. The data is shared using cloud storage (e.g., Amazon S3). The progress data is comprehensively analyzed and the treatment plan is revised as necessary. This analysis is performed using machine learning algorithms.

[2057] Generative AI exercises

[2058] Server: Using generative AI, the server designs optimal exercises based on the patient's symptoms and treatment progress. The AI ​​takes into account the patient's feedback and progress and generates exercise demonstration videos and guides. Specifically, it uses a video generation engine (e.g., OpenAI's DALL-E or GPT-3).

[2059] Device: Displays exercise videos and guides to patients. This interface is used to play videos and check exercise procedures.

[2060] User: Exercise at home while watching exercise videos. After the exercise, feedback on results and impressions is provided within the app.

[2061] Server: Analyzes the received feedback and incorporates it into your next exercise plan.

[2062] Emotion engine recognizes and reflects emotions

[2063] Server: Launches the emotion engine and analyzes the user's input data and interactions. This emotion engine analyzes the user's emotional state using comments and data entered by the user and their behavioral data (e.g., input speed, input content, voice data, etc.). It uses a natural language processing (NLP) library (e.g., SpaCy, NLTK).

[2064] Device: When the user inputs their thoughts on treatment progress and exercise, the emotion engine analyzes the input data in real time and evaluates the user's emotional state.

[2065] Server: Receives the emotional data analyzed by the emotion engine and reflects it in optimizing the treatment plan. For example, if the user is feeling stressed, it will recommend relaxing exercises.

[2066] Example: A user inputs and sends a comment about their treatment progress, such as "Today the pain has eased a little, but I'm still a little anxious." The device sends the comment to the server, which uses an emotion engine to analyze the comment and identify the emotion of "anxiety." Based on the analysis results, a new treatment plan including relaxation exercises is generated and the new treatment plan is displayed to the user on the device.

[2067] In this way, by combining the emotion engine, it is possible to provide customized treatment that takes into consideration the patient's emotions, thereby improving the effectiveness of treatment.

[2068] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2069] Step 1:

[2070] Server: Generates a patient information input form and makes it accessible to users. The input is a web form using HTML and CSS, and items include details of symptoms, pain level, existing treatments, and past medical history. The output is an interface that accepts user input. Specifically, it places form elements in an HTML file and implements real-time validation functionality using JavaScript.

[2071] Step 2:

[2072] Device: The patient accesses the input form through a browser or app and enters symptoms and health information. Input involves entering information such as symptoms into each field. Output involves saving the entered data on the device. Specifically, the user enters information into text boxes and drop-down menus on the form and presses the submit button.

[2073] Step 3:

[2074] Terminal: Data entered by the user is sent to the server in JSON format. Input is the information entered in each field. Output is the data converted to JSON format and sent to the server. Specifically, form data is serialized into JSON using a JavaScript function and sent to the server via an HTTP POST request.

[2075] Step 4:

[2076] Server: Passes the received patient information to the analysis module. The input is the JSON data sent to the server. The output is the symptom classification results and a list of recommended treatments and treatment facilities. Specifically, it runs a Python script and performs analysis using AI libraries such as TensorFlow and PyTorch.

[2077] Step 5:

[2078] Server: Based on the analysis results, it searches the database for information on the most suitable treatment facilities and creates a list. The input is the analysis results by AI. The output is a list of treatment facilities that are best suited to the user. Specifically, it executes an SQL query to retrieve treatment facility information from the database and filters it based on the analysis results.

[2079] Step 6:

[2080] Server: Generates a treatment plan optimized for the patient. The input is the analysis results and treatment facility information obtained from the database. The output is a customized treatment plan. Specifically, the treatment plan is automatically generated using a Python script based on the results of the AI ​​module.

[2081] Step 7:

[2082] Terminal: Displays recommended treatment facilities and treatment plans to patients. The input is the list of treatment facilities and customized treatment plans sent from the server. The output is an interface that displays them to the user. Specific operations include rendering the data using front-end frameworks such as React and Vue.js.

[2083] Step 8:

[2084] User: Check the presented information and select the most suitable treatment facility. The input is the list of treatment facilities displayed on the terminal. The output is the selected treatment facility information. Specifically, the user selects the desired treatment facility from the list and presses the decision button.

[2085] Step 9:

[2086] Terminal: Provides an interface for recording the progress of treatment. Input includes changes in pain after treatment, impressions, and future plans. Output is the progress data entered by the user. Specific operations include displaying a form with text fields and check boxes.

[2087] Step 10:

[2088] User: Enters the progress of treatment and sends it to the server. The input is the progress information of treatment. The output is the progress data sent to the server. The specific operation is to enter the progress and press the send button.

[2089] Step 11:

[2090] Terminal: The progress data sent by the user is sent to the server in JSON format. The input is the entered progress information. The output is the JSON data sent to the server. Specifically, the input content is converted to JSON and sent to the server via an HTTP POST request.

[2091] Step 12:

[2092] Server: Shares the received progress data with the doc...

Claims

1. A patient information input means; means for receiving and analyzing said patient information; A means to recommend the most suitable treatment center based on the analysis results, means for generating a customized treatment plan for the patient; A means to share treatment progress in real time, A system including means for providing exercise demonstrations using generative AI.

2. 2. The system of claim 1, wherein said patient information input means provides a form for inputting patient symptoms and health information.

3. 2. The system according to claim 1, wherein said analysis means classifies symptoms based on the patient's symptoms and health information, and lists candidates for necessary treatments and treatment centers.

4. 2. The system according to claim 1, wherein the real-time sharing means collects and analyzes patient treatment progress data and modifies the treatment plan as necessary.

5. The system described in claim 1, characterized in that the exercise provision means using the generation AI designs appropriate exercises based on the patient's health information and treatment progress, and generates demonstration videos of the exercises.

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