system

Abstract

We provide the system. [Solution] A means of communication for receiving information on the health status of residents, An analytical means for analyzing the aforementioned health status information and extracting estimated diseases, A means for presenting management methods that proposes the optimal management method based on the analysis results, A monitoring system for collecting residents' biometric information and detecting abnormalities, A warning generation means that generates a notification when an anomaly is detected, A report generation means for generating an evaluation report including the aforementioned analysis results and management method, A system that includes this.

Description

Technical Field

[0004] , , ,

[0005] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern medical settings, doctors are facing an increasing chronic burden and the requirements for higher precision and faster speed in medical service provision. In particular, due to a shortage of doctors, it has become difficult to diagnose complex cases, and there is a continuous situation where rapid diagnosis and appropriate treatment suggestions are essential. In addition, during emergencies at night or on holidays, the doctor's workload further increases. Moreover, patients often have difficulty constantly grasping their own health conditions and cannot respond promptly when abnormalities occur.

Means for Solving the Problems

[0005] This invention provides an analysis means for receiving patient symptom data, analyzing that data, and extracting a presumed disease name. Furthermore, it includes means for suggesting the optimal treatment method based on the analysis results, and monitoring means for 24-hour monitoring of the patient's vital data to detect abnormalities, thereby solving this problem. In addition, if an abnormality is detected, it immediately generates an alert and provides necessary information to patients and medical professionals by generating a diagnostic report including the diagnosis result and treatment method. This improves the accuracy and speed of diagnosis, reduces the burden on doctors, and enhances patient confidence.

[0006] "Communication means" refers to a device or program that has the function of transmitting and receiving patient symptom data to and from a system.

[0007] "Analysis means" refers to an algorithm or device that extracts the name of a disease estimated based on the received symptom data.

[0008] A "treatment method suggestion device" is a device or program that has the function of showing the most appropriate treatment method to the patient or medical professional based on the analysis results.

[0009] A "monitoring device" is a device or program that can continuously observe a patient's vital data and detect abnormalities.

[0010] An "alert generation means" is a device or program that has the function of issuing a warning when an abnormality is detected as a result of monitoring.

[0011] "Report generation means" refers to a device or program that has the function of formatting and outputting information, including analysis results and treatment methods, as a diagnostic report. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is implemented as a diagnostic support system for medical settings. The program's processing details are described below in natural language.

[0034] First, users enter their symptoms using a smartphone application or web portal. This information includes the specific nature of the symptoms, their duration, and their severity. The device transmits the entered information to the server in real time, and secure communication protocols are used to protect data privacy.

[0035] The server uses an AI model to analyze the received symptom data. The AI ​​model interprets the content of the symptoms using natural language processing technology and predicts the most likely disease name by comparing it with a vast amount of medical case data. Furthermore, the server refers to the latest treatment database for each disease and selects the optimal treatment method.

[0036] Furthermore, if the user is wearing a wearable device, the device continuously monitors vital data and transmits that information to a server. Based on this data, the server detects any abnormalities and immediately sends an alert to the user. This alert function allows users to quickly understand their health status and take appropriate action.

[0037] A diagnostic report integrating the analysis results is automatically generated by the server. The report includes the diagnosis, recommended treatments, and urgency assessment, and is shared with healthcare professionals as needed. Based on this report, users can choose to consult further healthcare providers or opt for home care.

[0038] For example, if a user complains of "fatigue and a sore throat," the AI ​​model analyzes this as an early symptom of a cold or the flu and generates a diagnostic report recommending appropriate rest and the use of over-the-counter medication. Furthermore, through continuous monitoring, the server issues an alert prompting the user to seek medical attention if their temperature rises.

[0039] Thus, the present invention functions effectively as a diagnostic support system that assists physicians in their diagnoses and enhances patients' self-management abilities.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users launch a smartphone application or web portal and enter information about their symptoms. This includes specific symptoms, the duration since onset, and the severity of the symptoms. Once the user has finished entering the information, they press the submit button to save the information to their device.

[0043] Step 2:

[0044] The terminal converts the symptom data entered by the user into a structured format and sends it to the server using a secure communication protocol (e.g., HTTPS). This protects the confidentiality and privacy of the data.

[0045] Step 3:

[0046] The server inputs the received symptom data into the AI ​​model and begins analysis in real time. The AI ​​model uses natural language processing technology to interpret the input data and generates a list of possible disease names related to the symptoms.

[0047] Step 4:

[0048] The server matches the estimated disease name against an internal treatment database. This database is regularly updated with the latest medical information and contains the best treatment options for each disease.

[0049] Step 5:

[0050] Vital data from wearable devices and smartphones used by the user is collected by the terminal and sent to the server. The server monitors this data in real time and generates an alert if any abnormal values ​​are detected.

[0051] Step 6:

[0052] The server combines the analysis results and treatment recommendations to generate a detailed diagnostic report. This report describes the diagnosis, recommended treatments, and whether further tests are needed.

[0053] Step 7:

[0054] The server sends the generated diagnostic report to the user via email or in-app notification. It can also send the same information to medical professionals as needed.

[0055] Step 8:

[0056] Users review the received diagnostic report to determine recommended treatments and whether further medical consultations are necessary. They then use this information to manage their health in their daily lives.

[0057] (Example 1)

[0058] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0059] In modern healthcare, it is crucial for patients to respond quickly and appropriately to their symptoms, but many patients have difficulty immediately identifying their illness or the necessary treatment. Monitoring vital data and responding immediately to abnormalities in emergencies also pose challenges. Therefore, there is a need for a system that allows users to effectively manage their own health status.

[0060] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0061] In this invention, the server includes a device for acquiring user symptom information, a processing device for processing the symptom information and deriving the most likely disease name, and a method selection device for selecting the optimal treatment method based on the processing results. This enables users to quickly obtain an appropriate diagnosis and treatment method for their symptoms.

[0062] A "device for acquiring user symptom information" is a device that receives information about symptoms entered by the user and collects it in a format that can be processed as data.

[0063] A "processing device" is a device that analyzes accumulated symptom information and performs calculations and judgments to determine the most likely diagnosis.

[0064] A "treatment method selection device" is a device that has the function of selecting and presenting the optimal treatment plan for a disease name obtained through analysis.

[0065] A "monitoring device for acquiring vital signs information and monitoring abnormalities" is a device used to continuously collect users' health data and detect abnormalities.

[0066] A "notification device" is a device that issues warnings or instructions to users based on abnormalities detected by monitoring equipment.

[0067] A "document creation device" is a device that integrates information, including processing results and selected treatment methods, to generate a diagnostic document.

[0068] This invention functions as a medical diagnostic support system. Specific embodiments for carrying out this invention are described below.

[0069] The terminal allows users to input symptom information through a smartphone application or web portal. Users can enter specific symptoms, their duration, and their severity. This information is transmitted to the server via a secure communication method such as the HTTPS protocol.

[0070] The server uses a generative AI model to perform natural language processing to process the received symptom information. This AI model has been pre-trained on a large amount of medical case data and predicts the most likely disease name from the input symptoms. Based on the predicted disease name, the server also selects the optimal treatment method from the latest treatment database.

[0071] Furthermore, wearable devices worn by users continuously transmit vital signs information to a server via the terminal. Data such as heart rate and body temperature are monitored on the server, and if abnormal values ​​are detected, a notification is immediately sent to the user.

[0072] The generated diagnostic document includes the diagnosed illness, recommended treatment, and an assessment of urgency, which users can access via their device. It can also be provided to healthcare professionals via email or in-app message.

[0073] For example, if a user enters "headache and fever" into the application, the device sends this information to the server. The server uses a generative AI model to identify the possibility of a cold and suggests treatment options, including the use of over-the-counter medication. If a persistent fever is detected, the server will notify the user to seek medical attention.

[0074] An example of a prompt message is, "Based on the symptom data reported by the user, identify possible disease names and the most appropriate corresponding treatment."

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user opens a smartphone application or web portal and enters symptom information. This information includes specific symptoms, duration, and severity. The entered data is processed by the device and sent to the server via a secure communication protocol. This process allows the server to obtain the symptom data necessary for analysis.

[0078] Step 2:

[0079] The server receives symptom data sent from the terminal and analyzes the data using a generative AI model. Specifically, it interprets the input symptoms using natural language processing techniques and compares them with a large amount of medical case data. The input in this step is the user's symptom data, and the output is the most likely disease name. The server then uses this information to proceed to the next step.

[0080] Step 3:

[0081] The server consults the latest treatment database based on the disease name obtained through analysis. To select the appropriate treatment, the server retrieves relevant treatment options from the database and creates an optimized treatment plan based on them. The input for this step is the analyzed disease name, and the output is the selected treatment. The server then passes this information to the next report generation step.

[0082] Step 4:

[0083] When a user is wearing a wearable device, the terminal continuously monitors the user's vital signs and transmits the data to the server. The input is vital sign data acquired by the wearable device, which the terminal provides to the server in real time. The server monitors the received data for anomalies and immediately sends a notification if an anomaly is detected.

[0084] Step 5:

[0085] The server generates a diagnostic document based on analysis results, treatment options, and monitoring data. The document includes the diagnosis, recommended treatment, and urgency level, and is provided to the user. Input is all analysis results and selected treatments to date, and output is the diagnostic report.

[0086] Step 6:

[0087] The server provides the user with the final generated diagnostic document. The user can review this report on their device and, based on it, choose actions such as visiting a medical institution or staying home. The server also sends the diagnostic report to healthcare professionals using communication technology as needed. The output of this step is the diagnostic document provided to both the user and healthcare professionals.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] Conventional health management systems have made it difficult for residents to understand their own health status in real time and to quickly detect and address abnormalities early. Furthermore, the lack of technology to efficiently analyze collected health data and suggest optimal management methods has made it difficult to properly manage the health status of individuals and communities as a whole.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes communication means for receiving residents' health status information, analysis means for analyzing the health status information and extracting suspected diseases, management method presentation means for suggesting the optimal management method based on the analysis results, monitoring means for collecting residents' biometric information and detecting abnormalities, warning generation means for generating notifications when abnormalities are detected, and report generation means for generating an evaluation report including the analysis results and management methods. This allows residents to understand their own health status in real time, enabling early detection of abnormalities and appropriate responses. It can also contribute to understanding the health trends of the entire region.

[0093] "Residents" are people who live in a specific area and use the medical services and health management systems of that community.

[0094] "Health status information" refers to subjective health data and symptom records provided by residents, and is used to assess their health status.

[0095] "Biometric information" refers to objective data obtained from the bodies of residents, including information such as heart rate and body temperature.

[0096] "Communication means" refers to devices or methods for collecting residents' health status information and transmitting it to a server, and includes internet communication and wireless communication technologies.

[0097] "Analysis means" refers to a device or method that processes data using AI technology based on received health status information and extracts estimated diseases.

[0098] A "monitoring system" is a system that continuously collects biometric information and detects abnormalities, and is equipped with a function to warn the user when an abnormality occurs.

[0099] A "management method presentation means" is a device or method that provides users with the optimal health management method based on analyzed health status data.

[0100] A "warning generation system" is a system that issues notifications to residents or designated parties when an abnormality is detected in biometric information.

[0101] An "evaluation report" is a document that integrates analysis results and recommended health management methods to provide a clear and concise overview of the residents' health status.

[0102] This invention is a system for managing residents' health status in real time and detecting abnormalities early. The detailed configuration for realizing this system is described below.

[0103] The server receives health status and biometric information transmitted by residents from smart devices (e.g., smartphones and smartwatches). Internet communication is primarily used for communication, and a secure communication protocol (a separately configured method) is employed to protect data privacy.

[0104] Information entered on the terminal is immediately transmitted to the server. The server uses AI technology (for example, a generative AI model using TENSORFLOW®) to analyze the information and identify diseases that can be inferred from the health status information provided by residents. In addition, abnormal values ​​are detected in the collected biometric information using the AI ​​model, and the results are reflected in management reports as a risk assessment.

[0105] The optimal management methods based on the analysis results are presented to residents as if they were advice from a medical professional. This allows residents to take appropriate actions to maintain their health in their daily lives.

[0106] Furthermore, if biometric data transmitted through a device reaches abnormal levels, the server immediately generates a warning. This warning serves as an important source of information to prompt residents to take swift action. The content of the warning is delivered to residents as an in-app message on their smart devices or via email.

[0107] (Specific example)

[0108] For example, if a resident reports "mild headache and fatigue" and their smartwatch shows a temperature higher than normal, the server will estimate this to be a temporary illness due to overwork and generate a report recommending that they take appropriate rest. This report is sent to the resident's device, allowing them to quickly understand their health status and take rest or seek medical attention as needed.

[0109] Example prompt to input into the generating AI model: "The user reported symptoms of 'mild headache and fatigue,' and body temperature data from a smartwatch is 1 degree higher than normal. Based on these conditions, please list possible illnesses and recommended actions."

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The device acquires health status information and biometric information (such as heart rate and body temperature) entered by residents. The entered information is collected in real time via smartphones and smartwatches.

[0113] Step 2:

[0114] The device transmits acquired health status and biometric information to a server via the internet. Secure communication protocols such as SSL are used for this data transmission to ensure security.

[0115] Step 3:

[0116] The server stores the received health status information in a database and performs analysis using a generative AI model. Specifically, it interprets the input symptoms using natural language processing and extracts the estimated disease name. Based on this analysis, the server outputs a list of disease names.

[0117] Step 4:

[0118] Based on the analysis results, the server proposes management methods for possible diseases. To do this, it queries the latest medical treatment databases for appropriate management methods and selects the information that should be provided to the population. The output is a list of recommended management methods.

[0119] Step 5:

[0120] The server detects anomalies in biometric data. Based on the acquired biometric data, it applies an algorithm to determine abnormal values ​​and generates a warning if the threshold is exceeded. This warning is immediately sent to residents and, if necessary, to relevant health professionals.

[0121] Step 6:

[0122] The server generates an evaluation report that includes analysis results and proposed management methods. This report is organized in a format that is easy for residents to understand and sent to them as a message within the smartphone application or as an email.

[0123] Step 7:

[0124] Based on the evaluation report received, users can decide on their own health management actions. If necessary, they can consult with a healthcare professional and consider visiting an appropriate medical institution as the next step.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] This invention is implemented in a medical diagnostic support system that incorporates an emotion engine capable of recognizing patients' emotions. This system integrates and analyzes patients' symptom data and emotion data to provide more appropriate treatment recommendations.

[0127] First, the user enters their physical symptoms using a smartphone or web interface. This information can include specific symptoms typically reported by patients, as well as subjective feelings about them (e.g., "severe pain," "anxiety," etc.). The device converts this information into a digital format and sends it to the server.

[0128] Following this process, the server feeds the received symptom data into an advanced AI model for analysis. Furthermore, the system utilizes an emotion engine to evaluate the user's emotional state in real time based on user input data and separately collected data (e.g., voice tone, facial recognition, text analysis, etc.). This allows for analysis that takes the user's psychological state into account.

[0129] Next, the server integrates symptom data and emotional data, and based on this, selects the optimal treatment method. When selecting a treatment method, psychological aspects are also considered; for example, if the user's stress level is high, therapies that promote relaxation will be prioritized.

[0130] In addition, vital data transmitted from wearable devices and smartphones is also monitored. If this data shows abnormal values, the server immediately generates an alert and notifies the user. Based on the analysis results of the emotion engine, the alert may include appropriate stress relief measures and mental health support.

[0131] Finally, the server generates a detailed diagnostic report based on these analysis results and sends it to the user via email or in-app notification. This report includes recommended treatments, suspected illnesses, and mental health recommendations based on the user's emotional state. It is also shared with healthcare professionals as needed to support a comprehensive diagnosis and recommendations.

[0132] For example, if a user reports "recent anxiety and headaches," the emotion engine can analyze the user's text input to detect a high level of anxiety. Based on this, the server will include recommendations for stress management techniques in addition to standard headache treatments in the user's report. In this way, the system is configured to support the patient's overall health.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] Users input symptoms related to their physical condition using their smartphones or computers via an application or web interface. They describe specific physical symptoms they experience and associated emotions (e.g., "anxiety," "depression," etc.) in an input form.

[0136] Step 2:

[0137] The terminal receives the entered symptom and emotion data and converts it into the appropriate format. This data is sent to the server, and security protocols are applied to protect the communication.

[0138] Step 3:

[0139] The server feeds the received data into an AI model to analyze the symptoms. This allows the model to estimate possible diseases and further refine the analysis by referencing relevant medical databases.

[0140] Step 4:

[0141] The server activates the emotion engine and analyzes the user's emotional data in detail. It uses text analysis, and in some cases voice tone and facial image analysis, to quantify or categorize the user's psychological state.

[0142] Step 5:

[0143] The server integrates the analyzed symptom and emotional data and selects the optimal treatment method based on this information. In situations where psychological state plays a role, it proposes a treatment method that takes into account the balance between mind and body.

[0144] Step 6:

[0145] If a user is using a wearable device, the device sends the vital data it collects to a server. The server monitors this data and immediately sends an alert to the user if an anomaly is detected.

[0146] Step 7:

[0147] The server generates a detailed diagnostic report based on the final analysis results. This report includes recommended treatments, mental care methods based on emotional state, and other health advice.

[0148] Step 8:

[0149] The server sends the generated diagnostic report to the user via email or in-app notification. If necessary, the report is also shared with medical professionals and used for follow-up at healthcare facilities.

[0150] Step 9:

[0151] Users review the diagnostic report they receive and incorporate recommended treatments and mental care techniques into their daily lives. This allows users to manage their health more comprehensively.

[0152] (Example 2)

[0153] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0154] Conventional medical diagnostic systems often rely solely on a patient's physical symptom data for diagnosis, failing to consider the patient's emotional state, making it difficult to select appropriate treatment. Therefore, there is a need for more appropriate treatment recommendations based on a comprehensive analysis that includes the patient's psychological state.

[0155] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0156] In this invention, the server includes communication means for receiving patient symptom data, analysis means for analyzing the symptom data and extracting disease names, and emotion evaluation means for evaluating emotional states and generating emotion data. This enables a comprehensive diagnosis that takes into account the physical and emotional aspects of the patient, and the suggestion of more appropriate treatment methods.

[0157] "Communication means" refers to a device or program that has the function of receiving symptom data from patients and transferring it to a server.

[0158] "Analysis means" refers to a device or program that processes received symptom data and performs calculations to extract the estimated disease name.

[0159] "Emotional evaluation means" refers to a device or program that has the function of performing voice, visual, and text analysis in order to evaluate the emotional state of a user and generate emotional data.

[0160] "Treatment selection means" refers to a device or program that has the function of integrating analyzed symptom data and emotional data and presenting the optimal treatment method based on that.

[0161] "Monitoring means" refers to a device or program that has a monitoring function to collect a patient's vital data and detect abnormalities.

[0162] "Alert generation means" refers to a device or program that has the function of generating a warning and notifying the user when an anomaly is detected.

[0163] "Report generation means" refers to a device or program that has the function of creating a diagnostic report including analysis results and treatment methods.

[0164] An "information processing device" refers to a device that has the function of processing data on devices such as wearable devices and smartphones and transmitting it to a server.

[0165] This invention relates to a medical diagnostic support system that provides more appropriate treatment methods by comprehensively analyzing a patient's physical and emotional information.

[0166] Users input their physical symptoms using a smartphone or web interface. This includes general symptom descriptions and information about their feelings (e.g., "I feel anxious," "I have a headache"). The device converts this information into a digital format and sends it to the server via a secure communication protocol.

[0167] The server receives symptom data and analyzes it using algorithms. A pre-trained generative AI model is used for the analysis, which estimates the disease name. Furthermore, emotion data of the user is generated using emotion evaluation methods that utilize voice analysis software and facial recognition tools. This enables an analysis that also takes the user's psychological state into account.

[0168] After symptom and emotional data are integrated, the server selects the optimal treatment. For example, if the analysis results indicate a high level of anxiety, stress reduction therapy can be recommended. Furthermore, if an abnormality is detected based on the collected vital data, an alert is generated and immediately notified to the user's device.

[0169] A detailed diagnostic report is generated by the server and sent to the user via email or in-app notification. This report includes recommended treatments and mental health support based on emotional state, and is shared with medical professionals as needed.

[0170] For example, if a user reports "recent anxiety and headaches," the emotion engine analyzes the user's input and recognizes a high level of anxiety. Based on this, the server generates and presents a report that includes not only "standard headache treatments" but also "stress management techniques."

[0171] Examples of prompts to input into the generating AI model include, "Please tell me the appropriate way to deal with headaches. In particular, I've been feeling very anxious lately." This allows the system to provide appropriate medical support tailored to the user's specific symptoms and emotional state.

[0172] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0173] Step 1:

[0174] Users input their physical symptoms and emotional state using a smartphone or web interface. The input data includes information such as "I have a headache" or "I feel very anxious." The device uses natural language processing technology to convert this information into digital data and transmits it to the server via a secure communication protocol. The input data is raw symptom text data, while the output data is in a structured digital format.

[0175] Step 2:

[0176] The server feeds structured data received from the terminal into a generating AI model. Based on the received data, the AI ​​model uses machine learning algorithms to analyze it and extract the estimated disease name. In this process, symptom data is input, and the disease name and related information are generated as output.

[0177] Step 3:

[0178] The server evaluates the user's emotional state using emotion assessment tools. Voice analysis, facial recognition, and text analysis are performed, and emotion data is generated based on user input and data collected through wearable devices. The input is unstructured data of the user's experience and emotional state, and the output generates emotion scores and emotion categories.

[0179] Step 4:

[0180] The server integrates symptom and emotional data and selects the optimal treatment using treatment selection tools. An AI model analyzes the integrated data and prioritizes treatments based on overall health status, including psychological factors. The input is integrated symptom and emotional information, and the output is a list of recommended treatments and health support.

[0181] Step 5:

[0182] The server receives vital data from the user's wearable device as needed and detects anomalies using monitoring means. If an anomaly is detected, an alert generation means immediately generates a warning and notifies the user. The input is real-time vital data, and the output is anomaly detection alert information.

[0183] Step 6:

[0184] The server generates a detailed diagnostic report based on the analysis results so far and sends it to the user. The report generation method creates a report that summarizes recommended treatments, suspected diseases, and mental health support based on emotional state. The input is analyzed medical and emotional data, and the output is a diagnostic report for the user and medical professionals.

[0185] (Application Example 2)

[0186] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0187] There is a need for a system that comprehensively evaluates the health and psychological state of care recipients, including the elderly, and provides more appropriate care and treatment. In particular, there is a challenge in analyzing the emotional state of care recipients in real time and proposing the optimal care plan based on that analysis.

[0188] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0189] In this invention, the server includes communication means for receiving patient physiological data, analysis means for analyzing the physiological data and extracting an estimated health status, and emotion analysis means for evaluating the patient's psychological data. This enables an integrated analysis of the health and emotional status of the person receiving care, making it possible to propose individually appropriate care and treatment methods.

[0190] "Communication means" refers to technological devices used to receive patient physiological data and health data from information terminals, etc.

[0191] "Analysis means" refers to a processing device that analyzes and extracts estimated health status based on received physiological data.

[0192] A "treatment suggestion device" is an output device that indicates the most suitable treatment or therapy for a patient based on the analysis results.

[0193] A "monitoring device" is a measuring device used to monitor collected health data from patients and detect abnormalities.

[0194] A "notification generation means" is a technical device for issuing a warning when an anomaly is detected.

[0195] A "report generation means" is a generation device for creating an analysis report that includes analysis results and recommended actions.

[0196] An "emotional analysis device" is an analytical device used to evaluate a patient's psychological data and analyze their emotional state.

[0197] A "care proposal device" is an output device that proposes a suitable care plan, taking into account the patient's psychological state.

[0198] This invention is a system for comprehensively evaluating the health and emotional state of a person receiving care and proposing an appropriate care plan. This system collects data via portable devices or information terminals, transmits it to a server for processing, and specifically receives the patient's physiological and psychological data from the information terminal using communication means.

[0199] The server processes the received data using analysis tools to extract the estimated health status. It also analyzes psychological data using emotion analysis tools to evaluate the patient's emotional state. Based on the analyzed data, the treatment suggestion tool proposes the optimal treatment or care plan. Furthermore, the monitoring tool continuously monitors the data, and if an abnormality is detected, the notification generation tool issues a warning.

[0200] The generated reports are created by a report generation system and provided to users and health professionals via electronic communication. This system enables the provision of individually tailored care to those receiving care.

[0201] For example, if a person receiving care reports to the application that they "haven't been able to sleep at night recently," the emotion analysis tool will analyze their anxiety level, and the health analysis tool will use that data to recommend measures to promote relaxation.

[0202] Example of a prompt:

[0203] "In managing the health of the elderly, it is necessary to propose care plans that take into account both physical symptoms and emotional state. Please propose appropriate measures for cases where an elderly person reports difficulty sleeping at night."

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The terminal transmits physical symptoms and psychological emotion data entered by the care recipient to the server via communication means. The input includes text and sensor data indicating emotional states, and the output is generated as digitally converted information. This data forms the basis for analysis on the server.

[0207] Step 2:

[0208] The server inputs the received physiological data into an analysis system to estimate the patient's health status. During this process, a generative AI model is used to process the data and identify potential diseases. Furthermore, emotional analysis is performed on the input data to extract the patient's psychological state, and the analysis results are generated as output.

[0209] Step 3:

[0210] The server presents the optimal treatment based on the analysis results using a treatment suggestion system. The analysis results of health status and emotional state are used as input, and these are integrated to propose an optimal care plan. Specific treatment and care suggestions are generated as output and sent to the terminal.

[0211] Step 4:

[0212] The terminal displays the provided care plan to both care staff and care recipients. This information is output to the terminal screen in a visually understandable format. Users can also provide feedback at this stage.

[0213] Step 5:

[0214] The server continuously monitors health data using monitoring devices. The latest health data is used as input, and if an anomaly is detected, a notification generation device generates a warning. This output is sent to the terminal as an alert.

[0215] Step 6:

[0216] The server generates an analysis report using a report generation system based on all analysis results. The input includes information on health status, emotional state, and care plan, and this report is output electronically to the user and health professionals.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0233] This invention is implemented as a diagnostic support system for medical settings. The program's processing details are described below in natural language.

[0234] First, users enter their symptoms using a smartphone application or web portal. This information includes the specific nature of the symptoms, their duration, and their severity. The device transmits the entered information to the server in real time, and secure communication protocols are used to protect data privacy.

[0235] The server uses an AI model to analyze the received symptom data. The AI ​​model interprets the content of the symptoms using natural language processing technology and predicts the most likely disease name by comparing it with a vast amount of medical case data. Furthermore, the server refers to the latest treatment database for each disease and selects the optimal treatment method.

[0236] Furthermore, if the user is wearing a wearable device, the device continuously monitors vital data and transmits that information to a server. Based on this data, the server detects any abnormalities and immediately sends an alert to the user. This alert function allows users to quickly understand their health status and take appropriate action.

[0237] A diagnostic report integrating the analysis results is automatically generated by the server. The report includes the diagnosis, recommended treatments, and urgency assessment, and is shared with healthcare professionals as needed. Based on this report, users can choose to consult further healthcare providers or opt for home care.

[0238] For example, if a user complains of "fatigue and a sore throat," the AI ​​model analyzes this as an early symptom of a cold or the flu and generates a diagnostic report recommending appropriate rest and the use of over-the-counter medication. Furthermore, through continuous monitoring, the server issues an alert prompting the user to seek medical attention if their temperature rises.

[0239] Thus, the present invention functions effectively as a diagnostic support system that assists physicians in their diagnoses and enhances patients' self-management abilities.

[0240] The following describes the processing flow.

[0241] Step 1:

[0242] Users launch a smartphone application or web portal and enter information about their symptoms. This includes specific symptoms, the duration since onset, and the severity of the symptoms. Once the user has finished entering the information, they press the submit button to save the information to their device.

[0243] Step 2:

[0244] The terminal converts the symptom data entered by the user into a structured format and sends it to the server using a secure communication protocol (e.g., HTTPS). This protects the confidentiality and privacy of the data.

[0245] Step 3:

[0246] The server inputs the received symptom data into the AI ​​model and begins analysis in real time. The AI ​​model uses natural language processing technology to interpret the input data and generates a list of possible disease names related to the symptoms.

[0247] Step 4:

[0248] The server matches the estimated disease name against an internal treatment database. This database is regularly updated with the latest medical information and contains the best treatment options for each disease.

[0249] Step 5:

[0250] Vital data from wearable devices and smartphones used by the user is collected by the terminal and sent to the server. The server monitors this data in real time and generates an alert if any abnormal values ​​are detected.

[0251] Step 6:

[0252] The server combines the analysis results and treatment recommendations to generate a detailed diagnostic report. This report describes the diagnosis, recommended treatments, and whether further tests are needed.

[0253] Step 7:

[0254] The server sends the generated diagnostic report to the user via email or in-app notification. It can also send the same information to medical professionals as needed.

[0255] Step 8:

[0256] Users review the received diagnostic report to determine recommended treatments and whether further medical consultations are necessary. They then use this information to manage their health in their daily lives.

[0257] (Example 1)

[0258] Next, we will describe Example 1. 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."

[0259] In modern healthcare, it is crucial for patients to respond quickly and appropriately to their symptoms, but many patients have difficulty immediately identifying their illness or the necessary treatment. Monitoring vital data and responding immediately to abnormalities in emergencies also pose challenges. Therefore, there is a need for a system that allows users to effectively manage their own health status.

[0260] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0261] In this invention, the server includes a device for acquiring user symptom information, a processing device for processing the symptom information and deriving the most likely disease name, and a method selection device for selecting the optimal treatment method based on the processing results. This enables users to quickly obtain an appropriate diagnosis and treatment method for their symptoms.

[0262] A "device for acquiring user symptom information" is a device that receives information about symptoms entered by the user and collects it in a format that can be processed as data.

[0263] A "processing device" is a device that analyzes accumulated symptom information and performs calculations and judgments to determine the most likely diagnosis.

[0264] A "treatment method selection device" is a device that has the function of selecting and presenting the optimal treatment plan for a disease name obtained through analysis.

[0265] A "monitoring device for acquiring vital signs information and monitoring abnormalities" is a device used to continuously collect users' health data and detect abnormalities.

[0266] A "notification device" is a device that issues warnings or instructions to users based on abnormalities detected by monitoring equipment.

[0267] A "document creation device" is a device that integrates information, including processing results and selected treatment methods, to generate a diagnostic document.

[0268] This invention functions as a medical diagnostic support system. Specific embodiments for carrying out this invention are described below.

[0269] The terminal allows users to input symptom information through a smartphone application or web portal. Users can enter specific symptoms, their duration, and their severity. This information is transmitted to the server via a secure communication method such as the HTTPS protocol.

[0270] The server uses a generative AI model to perform natural language processing to process the received symptom information. This AI model has been pre-trained on a large amount of medical case data and predicts the most likely disease name from the input symptoms. Based on the predicted disease name, the server also selects the optimal treatment method from the latest treatment database.

[0271] Furthermore, wearable devices worn by users continuously transmit vital signs information to a server via the terminal. Data such as heart rate and body temperature are monitored on the server, and if abnormal values ​​are detected, a notification is immediately sent to the user.

[0272] The generated diagnostic document includes the diagnosed illness, recommended treatment, and an assessment of urgency, which users can access via their device. It can also be provided to healthcare professionals via email or in-app message.

[0273] For example, if a user enters "headache and fever" into the application, the device sends this information to the server. The server uses a generative AI model to identify the possibility of a cold and suggests treatment options, including the use of over-the-counter medication. If a persistent fever is detected, the server will notify the user to seek medical attention.

[0274] An example of a prompt message is, "Based on the symptom data reported by the user, identify possible disease names and the most appropriate corresponding treatment."

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The user opens a smartphone application or web portal and enters symptom information. This information includes specific symptoms, duration, and severity. The entered data is processed by the device and sent to the server via a secure communication protocol. This process allows the server to obtain the symptom data necessary for analysis.

[0278] Step 2:

[0279] The server receives the symptom data sent from the terminal and analyzes the data using a generative AI model. Specifically, it interprets the input symptoms using natural language processing technology and matches them against a large amount of medical case data. The input at this step is the user's symptom data, and the output is the likely disease names. The server proceeds with the processing in the next step based on this information.

[0280] Step 3:

[0281] Based on the disease names obtained from the analysis, the server refers to the latest treatment method database. To select an appropriate treatment method, the server retrieves relevant treatment options from the database and creates an optimized treatment plan based on them. The input for this step is the analyzed disease name, and the output is the selected treatment method. The server passes this information to the next report generation step.

[0282] Step 4:

[0283] When the user is wearing a wearable device, the terminal continuously monitors the user's vital signs and sends the data to the server. The input is the vital sign data obtained by the wearable device, and the terminal provides this to the server in real time. The server monitors for abnormalities in the received data and immediately sends out a notification if an abnormality is detected.

[0284] Step 5:

[0285] The server generates a diagnostic document based on the analysis results, treatment methods, and monitoring data. The document describes the diagnosis name, recommended treatment method, and urgency, and this is provided to the user. The input is all the previous analysis results and the selected treatment method, and the output is the diagnostic report.

[0286] Step 6:

[0287] The server provides the user with the final generated diagnostic document. The user can review this report on their device and, based on it, choose actions such as visiting a medical institution or staying home. The server also sends the diagnostic report to healthcare professionals using communication technology as needed. The output of this step is the diagnostic document provided to both the user and healthcare professionals.

[0288] (Application Example 1)

[0289] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0290] Conventional health management systems have made it difficult for residents to understand their own health status in real time and to quickly detect and address abnormalities early. Furthermore, the lack of technology to efficiently analyze collected health data and suggest optimal management methods has made it difficult to properly manage the health status of individuals and communities as a whole.

[0291] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0292] In this invention, the server includes communication means for receiving residents' health status information, analysis means for analyzing the health status information and extracting suspected diseases, management method presentation means for suggesting the optimal management method based on the analysis results, monitoring means for collecting residents' biometric information and detecting abnormalities, warning generation means for generating notifications when abnormalities are detected, and report generation means for generating an evaluation report including the analysis results and management methods. This allows residents to understand their own health status in real time, enabling early detection of abnormalities and appropriate responses. It can also contribute to understanding the health trends of the entire region.

[0293] "Residents" are people who live in a specific area and use the medical services and health management systems of that community.

[0294] "Health status information" refers to subjective health data and symptom records provided by residents, and is used to assess their health status.

[0295] "Biometric information" refers to objective data obtained from the bodies of residents, including information such as heart rate and body temperature.

[0296] "Communication means" refers to devices or methods for collecting residents' health status information and transmitting it to a server, and includes internet communication and wireless communication technologies.

[0297] "Analysis means" refers to a device or method that processes data using AI technology based on received health status information and extracts estimated diseases.

[0298] A "monitoring system" is a system that continuously collects biometric information and detects abnormalities, and is equipped with a function to warn the user when an abnormality occurs.

[0299] A "management method presentation means" is a device or method that provides users with the optimal health management method based on analyzed health status data.

[0300] A "warning generation system" is a system that issues notifications to residents or designated parties when an abnormality is detected in biometric information.

[0301] An "evaluation report" is a document that integrates analysis results and recommended health management methods to provide a clear and concise overview of the residents' health status.

[0302] This invention is a system for managing residents' health status in real time and detecting abnormalities early. The detailed configuration for realizing this system is described below.

[0303] The server receives health status information and biometric information sent by residents from smart devices (e.g., smartphones and smartwatches). Internet communication is mainly used for the communication technology, and a secure communication protocol (a separately set method) is adopted to protect the privacy of data.

[0304] The information input on the terminal is immediately sent to the server. In the analysis of the information, the server makes full use of AI technology (e.g., a generative AI model using TensorFlow) to identify the diseases inferred from the health status information provided by the residents. For the collected biometric information, abnormal values are detected through analysis using the AI model, and the results are reflected in the management report as risk assessments.

[0305] The presentation of the optimal management method based on the analysis results is provided to the residents as if it were advice from medical experts. As a result, the residents can take appropriate actions to maintain their health in their daily lives.

[0306] In addition, when the biometric information reaches an abnormal value via the terminal, the server immediately generates a warning. This warning functions as an important information source to prompt the residents to take prompt action. The content of the warning is distributed to the residents as an in-application message on the smart device or as an email.

[0307] (Specific example)

[0308] For example, if a certain resident reports "mild headache and fatigue" and the smartwatch shows a value higher than the normal body temperature, the server estimates this as a temporary physical discomfort due to overwork and generates a report recommending appropriate rest. This report is notified to the resident's terminal, so that the resident can immediately grasp their own health status and receive rest or medical examination at a medical institution if necessary.

[0309] Example prompt to input into the generating AI model: "The user reported symptoms of 'mild headache and fatigue,' and body temperature data from a smartwatch is 1 degree higher than normal. Based on these conditions, please list possible illnesses and recommended actions."

[0310] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0311] Step 1:

[0312] The device acquires health status information and biometric information (such as heart rate and body temperature) entered by residents. The entered information is collected in real time via smartphones and smartwatches.

[0313] Step 2:

[0314] The device transmits acquired health status and biometric information to a server via the internet. Secure communication protocols such as SSL are used for this data transmission to ensure security.

[0315] Step 3:

[0316] The server stores the received health status information in a database and performs analysis using a generative AI model. Specifically, it interprets the input symptoms using natural language processing and extracts the estimated disease name. Based on this analysis, the server outputs a list of disease names.

[0317] Step 4:

[0318] Based on the analysis results, the server proposes management methods for possible diseases. To do this, it queries the latest medical treatment databases for appropriate management methods and selects the information that should be provided to the population. The output is a list of recommended management methods.

[0319] Step 5:

[0320] The server detects anomalies in biometric data. Based on the acquired biometric data, it applies an algorithm to determine abnormal values ​​and generates a warning if the threshold is exceeded. This warning is immediately sent to residents and, if necessary, to relevant health professionals.

[0321] Step 6:

[0322] The server generates an evaluation report that includes analysis results and proposed management methods. This report is organized in a format that is easy for residents to understand and sent to them as a message within the smartphone application or as an email.

[0323] Step 7:

[0324] Based on the evaluation report received, users can decide on their own health management actions. If necessary, they can consult with a healthcare professional and consider visiting an appropriate medical institution as the next step.

[0325] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0326] This invention is implemented in a medical diagnostic support system that incorporates an emotion engine capable of recognizing patients' emotions. This system integrates and analyzes patients' symptom data and emotion data to provide more appropriate treatment recommendations.

[0327] First, the user enters their physical symptoms using a smartphone or web interface. This information can include specific symptoms typically reported by patients, as well as subjective feelings about them (e.g., "severe pain," "anxiety," etc.). The device converts this information into a digital format and sends it to the server.

[0328] Following this process, the server feeds the received symptom data into an advanced AI model for analysis. Furthermore, the system utilizes an emotion engine to evaluate the user's emotional state in real time based on user input data and separately collected data (e.g., voice tone, facial recognition, text analysis, etc.). This allows for analysis that takes the user's psychological state into account.

[0329] Next, the server integrates symptom data and emotional data, and based on this, selects the optimal treatment method. When selecting a treatment method, psychological aspects are also considered; for example, if the user's stress level is high, therapies that promote relaxation will be prioritized.

[0330] In addition, vital data transmitted from wearable devices and smartphones is also monitored. If this data shows abnormal values, the server immediately generates an alert and notifies the user. Based on the analysis results of the emotion engine, the alert may include appropriate stress relief measures and mental health support.

[0331] Finally, the server generates a detailed diagnostic report based on these analysis results and sends it to the user via email or in-app notification. This report includes recommended treatments, suspected illnesses, and mental health recommendations based on the user's emotional state. It is also shared with healthcare professionals as needed to support a comprehensive diagnosis and recommendations.

[0332] For example, if a user reports "recent anxiety and headaches," the emotion engine can analyze the user's text input to detect a high level of anxiety. Based on this, the server will include recommendations for stress management techniques in addition to standard headache treatments in the user's report. In this way, the system is configured to support the patient's overall health.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] Users input symptoms related to their physical condition using their smartphones or computers via an application or web interface. They describe specific physical symptoms they experience and associated emotions (e.g., "anxiety," "depression," etc.) in an input form.

[0336] Step 2:

[0337] The terminal receives the entered symptom and emotion data and converts it into the appropriate format. This data is sent to the server, and security protocols are applied to protect the communication.

[0338] Step 3:

[0339] The server feeds the received data into an AI model to analyze the symptoms. This allows the model to estimate possible diseases and further refine the analysis by referencing relevant medical databases.

[0340] Step 4:

[0341] The server activates the emotion engine and analyzes the user's emotional data in detail. It uses text analysis, and in some cases voice tone and facial image analysis, to quantify or categorize the user's psychological state.

[0342] Step 5:

[0343] The server integrates the analyzed symptom and emotional data and selects the optimal treatment method based on this information. In situations where psychological state plays a role, it proposes a treatment method that takes into account the balance between mind and body.

[0344] Step 6:

[0345] If a user is using a wearable device, the device sends the vital data it collects to a server. The server monitors this data and immediately sends an alert to the user if an anomaly is detected.

[0346] Step 7:

[0347] The server generates a detailed diagnostic report based on the final analysis results. This report includes recommended treatments, mental care methods based on emotional state, and other health advice.

[0348] Step 8:

[0349] The server sends the generated diagnostic report to the user via email or in-app notification. If necessary, the report is also shared with medical professionals and used for follow-up at healthcare facilities.

[0350] Step 9:

[0351] Users review the diagnostic report they receive and incorporate recommended treatments and mental care techniques into their daily lives. This allows users to manage their health more comprehensively.

[0352] (Example 2)

[0353] Next, we will describe Example 2. 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".

[0354] Conventional medical diagnostic systems often rely solely on a patient's physical symptom data for diagnosis, failing to consider the patient's emotional state, making it difficult to select appropriate treatment. Therefore, there is a need for more appropriate treatment recommendations based on a comprehensive analysis that includes the patient's psychological state.

[0355] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0356] In this invention, the server includes communication means for receiving patient symptom data, analysis means for analyzing the symptom data and extracting disease names, and emotion evaluation means for evaluating emotional states and generating emotion data. This enables a comprehensive diagnosis that takes into account the physical and emotional aspects of the patient, and the suggestion of more appropriate treatment methods.

[0357] "Communication means" refers to a device or program that has the function of receiving symptom data from patients and transferring it to a server.

[0358] "Analysis means" refers to a device or program that processes received symptom data and performs calculations to extract the estimated disease name.

[0359] "Emotional evaluation means" refers to a device or program that has the function of performing voice, visual, and text analysis in order to evaluate the emotional state of a user and generate emotional data.

[0360] "Treatment selection means" refers to a device or program that has the function of integrating analyzed symptom data and emotional data and presenting the optimal treatment method based on that.

[0361] "Monitoring means" refers to a device or program that has a monitoring function to collect a patient's vital data and detect abnormalities.

[0362] "Alert generation means" refers to a device or program that has the function of generating a warning and notifying the user when an anomaly is detected.

[0363] "Report generation means" refers to a device or program that has the function of creating a diagnostic report including analysis results and treatment methods.

[0364] An "information processing device" refers to a device that has the function of processing data on devices such as wearable devices and smartphones and transmitting it to a server.

[0365] This invention relates to a medical diagnostic support system that provides more appropriate treatment methods by comprehensively analyzing a patient's physical and emotional information.

[0366] Users input their physical symptoms using a smartphone or web interface. This includes general symptom descriptions and information about their feelings (e.g., "I feel anxious," "I have a headache"). The device converts this information into a digital format and sends it to the server via a secure communication protocol.

[0367] The server receives symptom data and analyzes it using algorithms. A pre-trained generative AI model is used for the analysis, which estimates the disease name. Furthermore, emotion data of the user is generated using emotion evaluation methods that utilize voice analysis software and facial recognition tools. This enables an analysis that also takes the user's psychological state into account.

[0368] After symptom and emotional data are integrated, the server selects the optimal treatment. For example, if the analysis results indicate a high level of anxiety, stress reduction therapy can be recommended. Furthermore, if an abnormality is detected based on the collected vital data, an alert is generated and immediately notified to the user's device.

[0369] A detailed diagnostic report is generated by the server and sent to the user via email or in-app notification. This report includes recommended treatments and mental health support based on emotional state, and is shared with medical professionals as needed.

[0370] For example, if a user reports "recent anxiety and headaches," the emotion engine analyzes the user's input and recognizes a high level of anxiety. Based on this, the server generates and presents a report that includes not only "standard headache treatments" but also "stress management techniques."

[0371] Examples of prompts to input into the generating AI model include, "Please tell me the appropriate way to deal with headaches. In particular, I've been feeling very anxious lately." This allows the system to provide appropriate medical support tailored to the user's specific symptoms and emotional state.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] Users input their physical symptoms and emotional state using a smartphone or web interface. The input data includes information such as "I have a headache" or "I feel very anxious." The device uses natural language processing technology to convert this information into digital data and transmits it to the server via a secure communication protocol. The input data is raw symptom text data, while the output data is in a structured digital format.

[0375] Step 2:

[0376] The server feeds structured data received from the terminal into a generating AI model. Based on the received data, the AI ​​model uses machine learning algorithms to analyze it and extract the estimated disease name. In this process, symptom data is input, and the disease name and related information are generated as output.

[0377] Step 3:

[0378] The server evaluates the user's emotional state using emotion assessment tools. Voice analysis, facial recognition, and text analysis are performed, and emotion data is generated based on user input and data collected through wearable devices. The input is unstructured data of the user's experience and emotional state, and the output generates emotion scores and emotion categories.

[0379] Step 4:

[0380] The server integrates symptom and emotional data and selects the optimal treatment using treatment selection tools. An AI model analyzes the integrated data and prioritizes treatments based on overall health status, including psychological factors. The input is integrated symptom and emotional information, and the output is a list of recommended treatments and health support.

[0381] Step 5:

[0382] The server receives vital data from the user's wearable device as needed and detects anomalies using monitoring means. If an anomaly is detected, an alert generation means immediately generates a warning and notifies the user. The input is real-time vital data, and the output is anomaly detection alert information.

[0383] Step 6:

[0384] The server generates a detailed diagnostic report based on the analysis results so far and sends it to the user. The report generation method creates a report that summarizes recommended treatments, suspected diseases, and mental health support based on emotional state. The input is analyzed medical and emotional data, and the output is a diagnostic report for the user and medical professionals.

[0385] (Application Example 2)

[0386] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0387] There is a need for a system that comprehensively evaluates the health and psychological state of care recipients, including the elderly, and provides more appropriate care and treatment. In particular, there is a challenge in analyzing the emotional state of care recipients in real time and proposing the optimal care plan based on that analysis.

[0388] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0389] In this invention, the server includes communication means for receiving patient physiological data, analysis means for analyzing the physiological data and extracting an estimated health status, and emotion analysis means for evaluating the patient's psychological data. This enables an integrated analysis of the health and emotional status of the person receiving care, making it possible to propose individually appropriate care and treatment methods.

[0390] "Communication means" refers to technological devices used to receive patient physiological data and health data from information terminals, etc.

[0391] "Analysis means" refers to a processing device that analyzes and extracts estimated health status based on received physiological data.

[0392] A "treatment suggestion device" is an output device that indicates the most suitable treatment or therapy for a patient based on the analysis results.

[0393] A "monitoring device" is a measuring device used to monitor collected health data from patients and detect abnormalities.

[0394] A "notification generation means" is a technical device for issuing a warning when an anomaly is detected.

[0395] A "report generation means" is a generation device for creating an analysis report that includes analysis results and recommended actions.

[0396] An "emotional analysis device" is an analytical device used to evaluate a patient's psychological data and analyze their emotional state.

[0397] A "care proposal device" is an output device that proposes a suitable care plan, taking into account the patient's psychological state.

[0398] This invention is a system for comprehensively evaluating the health and emotional state of a person receiving care and proposing an appropriate care plan. This system collects data via portable devices or information terminals, transmits it to a server for processing, and specifically receives the patient's physiological and psychological data from the information terminal using communication means.

[0399] The server processes the received data using analysis tools to extract the estimated health status. It also analyzes psychological data using emotion analysis tools to evaluate the patient's emotional state. Based on the analyzed data, the treatment suggestion tool proposes the optimal treatment or care plan. Furthermore, the monitoring tool continuously monitors the data, and if an abnormality is detected, the notification generation tool issues a warning.

[0400] The generated reports are created by a report generation system and provided to users and health professionals via electronic communication. This system enables the provision of individually tailored care to those receiving care.

[0401] For example, if a person receiving care reports to the application that they "haven't been able to sleep at night recently," the emotion analysis tool will analyze their anxiety level, and the health analysis tool will use that data to recommend measures to promote relaxation.

[0402] Example of a prompt:

[0403] "In managing the health of the elderly, it is necessary to propose care plans that take into account both physical symptoms and emotional state. Please propose appropriate measures for cases where an elderly person reports difficulty sleeping at night."

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The terminal transmits physical symptoms and psychological emotion data entered by the care recipient to the server via communication means. The input includes text and sensor data indicating emotional states, and the output is generated as digitally converted information. This data forms the basis for analysis on the server.

[0407] Step 2:

[0408] The server inputs the received physiological data into an analysis system to estimate the patient's health status. During this process, a generative AI model is used to process the data and identify potential diseases. Furthermore, emotional analysis is performed on the input data to extract the patient's psychological state, and the analysis results are generated as output.

[0409] Step 3:

[0410] The server presents the optimal treatment based on the analysis results using a treatment suggestion system. The analysis results of health status and emotional state are used as input, and these are integrated to propose an optimal care plan. Specific treatment and care suggestions are generated as output and sent to the terminal.

[0411] Step 4:

[0412] The terminal displays the provided care plan to both care staff and care recipients. This information is output to the terminal screen in a visually understandable format. Users can also provide feedback at this stage.

[0413] Step 5:

[0414] The server continuously monitors health data using monitoring devices. The latest health data is used as input, and if an anomaly is detected, a notification generation device generates a warning. This output is sent to the terminal as an alert.

[0415] Step 6:

[0416] The server generates an analysis report using a report generation system based on all analysis results. The input includes information on health status, emotional state, and care plan, and this report is output electronically to the user and health professionals.

[0417] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0418] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0419] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0420] [Third Embodiment]

[0421] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0422] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0423] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0424] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0425] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0426] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0427] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0428] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0429] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0430] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0431] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0432] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0433] This invention is implemented as a diagnostic support system for medical settings. The program's processing details are described below in natural language.

[0434] First, users enter their symptoms using a smartphone application or web portal. This information includes the specific nature of the symptoms, their duration, and their severity. The device transmits the entered information to the server in real time, and secure communication protocols are used to protect data privacy.

[0435] The server uses an AI model to analyze the received symptom data. The AI ​​model interprets the content of the symptoms using natural language processing technology and predicts the most likely disease name by comparing it with a vast amount of medical case data. Furthermore, the server refers to the latest treatment database for each disease and selects the optimal treatment method.

[0436] Furthermore, if the user is wearing a wearable device, the device continuously monitors vital data and transmits that information to a server. Based on this data, the server detects any abnormalities and immediately sends an alert to the user. This alert function allows users to quickly understand their health status and take appropriate action.

[0437] A diagnostic report integrating the analysis results is automatically generated by the server. The report includes the diagnosis, recommended treatments, and urgency assessment, and is shared with healthcare professionals as needed. Based on this report, users can choose to consult further healthcare providers or opt for home care.

[0438] For example, if a user complains of "fatigue and a sore throat," the AI ​​model analyzes this as an early symptom of a cold or the flu and generates a diagnostic report recommending appropriate rest and the use of over-the-counter medication. Furthermore, through continuous monitoring, the server issues an alert prompting the user to seek medical attention if their temperature rises.

[0439] Thus, the present invention functions effectively as a diagnostic support system that assists physicians in their diagnoses and enhances patients' self-management abilities.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] Users launch a smartphone application or web portal and enter information about their symptoms. This includes specific symptoms, the duration since onset, and the severity of the symptoms. Once the user has finished entering the information, they press the submit button to save the information to their device.

[0443] Step 2:

[0444] The terminal converts the symptom data entered by the user into a structured format and sends it to the server using a secure communication protocol (e.g., HTTPS). This protects the confidentiality and privacy of the data.

[0445] Step 3:

[0446] The server inputs the received symptom data into the AI ​​model and begins analysis in real time. The AI ​​model uses natural language processing technology to interpret the input data and generates a list of possible disease names related to the symptoms.

[0447] Step 4:

[0448] The server matches the estimated disease name against an internal treatment database. This database is regularly updated with the latest medical information and contains the best treatment options for each disease.

[0449] Step 5:

[0450] Vital data from wearable devices and smartphones used by the user is collected by the terminal and sent to the server. The server monitors this data in real time and generates an alert if any abnormal values ​​are detected.

[0451] Step 6:

[0452] The server combines the analysis results and treatment recommendations to generate a detailed diagnostic report. This report describes the diagnosis, recommended treatments, and whether further tests are needed.

[0453] Step 7:

[0454] The server sends the generated diagnostic report to the user via email or in-app notification. It can also send the same information to medical professionals as needed.

[0455] Step 8:

[0456] Users review the received diagnostic report to determine recommended treatments and whether further medical consultations are necessary. They then use this information to manage their health in their daily lives.

[0457] (Example 1)

[0458] Next, we will describe Example 1. 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."

[0459] In modern healthcare, it is crucial for patients to respond quickly and appropriately to their symptoms, but many patients have difficulty immediately identifying their illness or the necessary treatment. Monitoring vital data and responding immediately to abnormalities in emergencies also pose challenges. Therefore, there is a need for a system that allows users to effectively manage their own health status.

[0460] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0461] In this invention, the server includes a device for acquiring user symptom information, a processing device for processing the symptom information and deriving the most likely disease name, and a method selection device for selecting the optimal treatment method based on the processing results. This enables users to quickly obtain an appropriate diagnosis and treatment method for their symptoms.

[0462] A "device for acquiring user symptom information" is a device that receives information about symptoms entered by the user and collects it in a format that can be processed as data.

[0463] A "processing device" is a device that analyzes accumulated symptom information and performs calculations and judgments to determine the most likely diagnosis.

[0464] A "treatment method selection device" is a device that has the function of selecting and presenting the optimal treatment plan for a disease name obtained through analysis.

[0465] A "monitoring device for acquiring vital signs information and monitoring abnormalities" is a device used to continuously collect users' health data and detect abnormalities.

[0466] A "notification device" is a device that issues warnings or instructions to users based on abnormalities detected by monitoring equipment.

[0467] A "document creation device" is a device that integrates information, including processing results and selected treatment methods, to generate a diagnostic document.

[0468] This invention functions as a medical diagnostic support system. Specific embodiments for carrying out this invention are described below.

[0469] The terminal allows users to input symptom information through a smartphone application or web portal. Users can enter specific symptoms, their duration, and their severity. This information is transmitted to the server via a secure communication method such as the HTTPS protocol.

[0470] The server uses a generative AI model to perform natural language processing to process the received symptom information. This AI model has been pre-trained on a large amount of medical case data and predicts the most likely disease name from the input symptoms. Based on the predicted disease name, the server also selects the optimal treatment method from the latest treatment database.

[0471] Furthermore, wearable devices worn by users continuously transmit vital signs information to a server via the terminal. Data such as heart rate and body temperature are monitored on the server, and if abnormal values ​​are detected, a notification is immediately sent to the user.

[0472] The generated diagnostic document includes the diagnosed illness, recommended treatment, and an assessment of urgency, which users can access via their device. It can also be provided to healthcare professionals via email or in-app message.

[0473] For example, if a user enters "headache and fever" into the application, the device sends this information to the server. The server uses a generative AI model to identify the possibility of a cold and suggests treatment options, including the use of over-the-counter medication. If a persistent fever is detected, the server will notify the user to seek medical attention.

[0474] An example of a prompt message is, "Based on the symptom data reported by the user, identify possible disease names and the most appropriate corresponding treatment."

[0475] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0476] Step 1:

[0477] The user opens a smartphone application or web portal and enters symptom information. This information includes specific symptoms, duration, and severity. The entered data is processed by the device and sent to the server via a secure communication protocol. This process allows the server to obtain the symptom data necessary for analysis.

[0478] Step 2:

[0479] The server receives symptom data sent from the terminal and analyzes the data using a generative AI model. Specifically, it interprets the input symptoms using natural language processing techniques and compares them with a large amount of medical case data. The input in this step is the user's symptom data, and the output is the most likely disease name. The server then uses this information to proceed to the next step.

[0480] Step 3:

[0481] The server consults the latest treatment database based on the disease name obtained through analysis. To select the appropriate treatment, the server retrieves relevant treatment options from the database and creates an optimized treatment plan based on them. The input for this step is the analyzed disease name, and the output is the selected treatment. The server then passes this information to the next report generation step.

[0482] Step 4:

[0483] When a user is wearing a wearable device, the terminal continuously monitors the user's vital signs and transmits the data to the server. The input is vital sign data acquired by the wearable device, which the terminal provides to the server in real time. The server monitors the received data for anomalies and immediately sends a notification if an anomaly is detected.

[0484] Step 5:

[0485] The server generates a diagnostic document based on analysis results, treatment options, and monitoring data. The document includes the diagnosis, recommended treatment, and urgency level, and is provided to the user. Input is all analysis results and selected treatments to date, and output is the diagnostic report.

[0486] Step 6:

[0487] The server provides the user with the final generated diagnostic document. The user can review this report on their device and, based on it, choose actions such as visiting a medical institution or staying home. The server also sends the diagnostic report to healthcare professionals using communication technology as needed. The output of this step is the diagnostic document provided to both the user and healthcare professionals.

[0488] (Application Example 1)

[0489] Next, we will explain Application Example 1. In the following explanation, 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."

[0490] Conventional health management systems have made it difficult for residents to understand their own health status in real time and to quickly detect and address abnormalities early. Furthermore, the lack of technology to efficiently analyze collected health data and suggest optimal management methods has made it difficult to properly manage the health status of individuals and communities as a whole.

[0491] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0492] In this invention, the server includes communication means for receiving residents' health status information, analysis means for analyzing the health status information and extracting suspected diseases, management method presentation means for suggesting the optimal management method based on the analysis results, monitoring means for collecting residents' biometric information and detecting abnormalities, warning generation means for generating notifications when abnormalities are detected, and report generation means for generating an evaluation report including the analysis results and management methods. This allows residents to understand their own health status in real time, enabling early detection of abnormalities and appropriate responses. It can also contribute to understanding the health trends of the entire region.

[0493] "Residents" are people who live in a specific area and use the medical services and health management systems of that community.

[0494] "Health status information" refers to subjective health data and symptom records provided by residents, and is used to assess their health status.

[0495] "Biometric information" refers to objective data obtained from the bodies of residents, including information such as heart rate and body temperature.

[0496] "Communication means" refers to devices or methods for collecting residents' health status information and transmitting it to a server, and includes internet communication and wireless communication technologies.

[0497] "Analysis means" refers to a device or method that processes data using AI technology based on received health status information and extracts estimated diseases.

[0498] A "monitoring system" is a system that continuously collects biometric information and detects abnormalities, and is equipped with a function to warn the user when an abnormality occurs.

[0499] A "management method presentation means" is a device or method that provides users with the optimal health management method based on analyzed health status data.

[0500] A "warning generation system" is a system that issues notifications to residents or designated parties when an abnormality is detected in biometric information.

[0501] An "evaluation report" is a document that integrates analysis results and recommended health management methods to provide a clear and concise overview of the residents' health status.

[0502] This invention is a system for managing residents' health status in real time and detecting abnormalities early. The detailed configuration for realizing this system is described below.

[0503] The server receives health status and biometric information transmitted by residents from smart devices (e.g., smartphones and smartwatches). Internet communication is primarily used for communication, and a secure communication protocol (a separately configured method) is employed to protect data privacy.

[0504] Information entered on the terminal is immediately transmitted to the server. The server uses AI technology (e.g., generative AI models using TensorFlow) to analyze the information and identify diseases that can be inferred from the health status information provided by residents. Furthermore, abnormal values ​​are detected in the collected biometric information using the AI ​​model, and the results are reflected in management reports as a risk assessment.

[0505] The optimal management methods based on the analysis results are presented to residents as if they were advice from a medical professional. This allows residents to take appropriate actions to maintain their health in their daily lives.

[0506] Furthermore, if biometric data transmitted through a device reaches abnormal levels, the server immediately generates a warning. This warning serves as an important source of information to prompt residents to take swift action. The content of the warning is delivered to residents as an in-app message on their smart devices or via email.

[0507] (Specific example)

[0508] For example, if a resident reports "mild headache and fatigue" and their smartwatch shows a temperature higher than normal, the server will estimate this to be a temporary illness due to overwork and generate a report recommending that they take appropriate rest. This report is sent to the resident's device, allowing them to quickly understand their health status and take rest or seek medical attention as needed.

[0509] Example prompt to input into the generating AI model: "The user reported symptoms of 'mild headache and fatigue,' and body temperature data from a smartwatch is 1 degree higher than normal. Based on these conditions, please list possible illnesses and recommended actions."

[0510] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0511] Step 1:

[0512] The device acquires health status information and biometric information (such as heart rate and body temperature) entered by residents. The entered information is collected in real time via smartphones and smartwatches.

[0513] Step 2:

[0514] The device transmits acquired health status and biometric information to a server via the internet. Secure communication protocols such as SSL are used for this data transmission to ensure security.

[0515] Step 3:

[0516] The server stores the received health status information in a database and performs analysis using a generative AI model. Specifically, it interprets the input symptoms using natural language processing and extracts the estimated disease name. Based on this analysis, the server outputs a list of disease names.

[0517] Step 4:

[0518] Based on the analysis results, the server proposes management methods for possible diseases. To do this, it queries the latest medical treatment databases for appropriate management methods and selects the information that should be provided to the population. The output is a list of recommended management methods.

[0519] Step 5:

[0520] The server detects anomalies in biometric data. Based on the acquired biometric data, it applies an algorithm to determine abnormal values ​​and generates a warning if the threshold is exceeded. This warning is immediately sent to residents and, if necessary, to relevant health professionals.

[0521] Step 6:

[0522] The server generates an evaluation report that includes analysis results and proposed management methods. This report is organized in a format that is easy for residents to understand and sent to them as a message within the smartphone application or as an email.

[0523] Step 7:

[0524] Based on the evaluation report received, users can decide on their own health management actions. If necessary, they can consult with a healthcare professional and consider visiting an appropriate medical institution as the next step.

[0525] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0526] The present invention is implemented in a medical diagnostic support system that incorporates an emotion engine capable of recognizing patients' emotions. This system integrates and analyzes patients' symptom data and emotion data to provide more appropriate treatment recommendations.

[0527] First, the user enters their physical symptoms using a smartphone or web interface. This information can include specific symptoms typically reported by patients, as well as subjective feelings about them (e.g., "severe pain," "anxiety," etc.). The device converts this information into a digital format and sends it to the server.

[0528] Following this process, the server feeds the received symptom data into an advanced AI model for analysis. Furthermore, the system utilizes an emotion engine to evaluate the user's emotional state in real time based on user input data and separately collected data (e.g., voice tone, facial recognition, text analysis, etc.). This allows for analysis that takes the user's psychological state into account.

[0529] Next, the server integrates symptom data and emotional data, and based on this, selects the optimal treatment method. When selecting a treatment method, psychological aspects are also considered; for example, if the user's stress level is high, therapies that promote relaxation will be prioritized.

[0530] In addition, vital data transmitted from wearable devices and smartphones is also monitored. If this data shows abnormal values, the server immediately generates an alert and notifies the user. Based on the analysis results of the emotion engine, the alert may include appropriate stress relief measures and mental health support.

[0531] Finally, the server generates a detailed diagnostic report based on these analysis results and sends it to the user via email or in-app notification. This report includes recommended treatments, suspected illnesses, and mental health recommendations based on the user's emotional state. It is also shared with healthcare professionals as needed to support a comprehensive diagnosis and recommendations.

[0532] For example, if a user reports "recent anxiety and headaches," the emotion engine can analyze the user's text input to detect a high level of anxiety. Based on this, the server will include recommendations for stress management techniques in addition to standard headache treatments in the user's report. In this way, the system is configured to support the patient's overall health.

[0533] The following describes the processing flow.

[0534] Step 1:

[0535] Users input symptoms related to their physical condition using their smartphones or computers via an application or web interface. They describe specific physical symptoms they experience and associated emotions (e.g., "anxiety," "depression," etc.) in an input form.

[0536] Step 2:

[0537] The terminal receives the entered symptom and emotion data and converts it into the appropriate format. This data is sent to the server, and security protocols are applied to protect the communication.

[0538] Step 3:

[0539] The server feeds the received data into an AI model to analyze the symptoms. This allows the model to estimate possible diseases and further refine the analysis by referencing relevant medical databases.

[0540] Step 4:

[0541] The server activates the emotion engine and analyzes the user's emotional data in detail. It uses text analysis, and in some cases voice tone and facial image analysis, to quantify or categorize the user's psychological state.

[0542] Step 5:

[0543] The server integrates the analyzed symptom and emotional data and selects the optimal treatment method based on this information. In situations where psychological state plays a role, it proposes a treatment method that takes into account the balance between mind and body.

[0544] Step 6:

[0545] If a user is using a wearable device, the device sends the vital data it collects to a server. The server monitors this data and immediately sends an alert to the user if an anomaly is detected.

[0546] Step 7:

[0547] The server generates a detailed diagnostic report based on the final analysis results. This report includes recommended treatments, mental care methods based on emotional state, and other health advice.

[0548] Step 8:

[0549] The server sends the generated diagnostic report to the user via email or in-app notification. If necessary, the report is also shared with medical professionals and used for follow-up at healthcare facilities.

[0550] Step 9:

[0551] Users review the diagnostic report they receive and incorporate recommended treatments and mental care techniques into their daily lives. This allows users to manage their health more comprehensively.

[0552] (Example 2)

[0553] Next, we will describe Example 2. 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."

[0554] Conventional medical diagnostic systems often rely solely on a patient's physical symptom data for diagnosis, failing to consider the patient's emotional state, making it difficult to select appropriate treatment. Therefore, there is a need for more appropriate treatment recommendations based on a comprehensive analysis that includes the patient's psychological state.

[0555] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0556] In this invention, the server includes communication means for receiving patient symptom data, analysis means for analyzing the symptom data and extracting disease names, and emotion evaluation means for evaluating emotional states and generating emotion data. This enables a comprehensive diagnosis that takes into account the physical and emotional aspects of the patient, and the suggestion of more appropriate treatment methods.

[0557] "Communication means" refers to a device or program that has the function of receiving symptom data from patients and transferring it to a server.

[0558] "Analysis means" refers to a device or program that processes received symptom data and performs calculations to extract the estimated disease name.

[0559] "Emotional evaluation means" refers to a device or program that has the function of performing voice, visual, and text analysis in order to evaluate the emotional state of a user and generate emotional data.

[0560] "Treatment selection means" refers to a device or program that has the function of integrating analyzed symptom data and emotional data and presenting the optimal treatment method based on that.

[0561] "Monitoring means" refers to a device or program that has a monitoring function to collect a patient's vital data and detect abnormalities.

[0562] "Alert generation means" refers to a device or program that has the function of generating a warning and notifying the user when an anomaly is detected.

[0563] "Report generation means" refers to a device or program that has the function of creating a diagnostic report including analysis results and treatment methods.

[0564] An "information processing device" refers to a device that has the function of processing data on devices such as wearable devices and smartphones and transmitting it to a server.

[0565] This invention relates to a medical diagnostic support system that provides more appropriate treatment methods by comprehensively analyzing a patient's physical and emotional information.

[0566] Users input their physical symptoms using a smartphone or web interface. This includes general symptom descriptions and information about their feelings (e.g., "I feel anxious," "I have a headache"). The device converts this information into a digital format and sends it to the server via a secure communication protocol.

[0567] The server receives symptom data and analyzes it using algorithms. A pre-trained generative AI model is used for the analysis, which estimates the disease name. Furthermore, emotion data of the user is generated using emotion evaluation methods that utilize voice analysis software and facial recognition tools. This enables an analysis that also takes the user's psychological state into account.

[0568] After symptom and emotional data are integrated, the server selects the optimal treatment. For example, if the analysis results indicate a high level of anxiety, stress reduction therapy can be recommended. Furthermore, if an abnormality is detected based on the collected vital data, an alert is generated and immediately notified to the user's device.

[0569] A detailed diagnostic report is generated by the server and sent to the user via email or in-app notification. This report includes recommended treatments and mental health support based on emotional state, and will be shared with medical professionals as needed.

[0570] For example, if a user reports "recent anxiety and headaches," the emotion engine analyzes the user's input and recognizes a high level of anxiety. Based on this, the server generates and presents a report that includes not only "standard headache treatments" but also "stress management techniques."

[0571] Examples of prompts to input into the generating AI model include, "Please tell me the appropriate way to deal with headaches. In particular, I've been feeling very anxious lately." This allows the system to provide appropriate medical support tailored to the user's specific symptoms and emotional state.

[0572] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0573] Step 1:

[0574] Users input their physical symptoms and emotional state using a smartphone or web interface. The input data includes information such as "I have a headache" or "I feel very anxious." The device uses natural language processing technology to convert this information into digital data and transmits it to the server via a secure communication protocol. The input data is raw symptom text data, while the output data is in a structured digital format.

[0575] Step 2:

[0576] The server feeds structured data received from the terminal into a generating AI model. Based on the received data, the AI ​​model uses machine learning algorithms to analyze it and extract the estimated disease name. In this process, symptom data is input, and the disease name and related information are generated as output.

[0577] Step 3:

[0578] The server evaluates the user's emotional state using emotion assessment tools. Voice analysis, facial recognition, and text analysis are performed, and emotion data is generated based on user input and data collected through wearable devices. The input is unstructured data of the user's experience and emotional state, and the output generates emotion scores and emotion categories.

[0579] Step 4:

[0580] The server integrates symptom and emotional data and selects the optimal treatment using treatment selection tools. An AI model analyzes the integrated data and prioritizes treatments based on overall health status, including psychological factors. The input is integrated symptom and emotional information, and the output is a list of recommended treatments and health support.

[0581] Step 5:

[0582] The server receives vital data from the user's wearable device as needed and detects anomalies using monitoring means. If an anomaly is detected, an alert generation means immediately generates a warning and notifies the user. The input is real-time vital data, and the output is anomaly detection alert information.

[0583] Step 6:

[0584] The server generates a detailed diagnostic report based on the analysis results so far and sends it to the user. The report generation method creates a report that summarizes recommended treatments, suspected diseases, and mental health support based on emotional state. The input is analyzed medical and emotional data, and the output is a diagnostic report for the user and medical professionals.

[0585] (Application Example 2)

[0586] Next, we will explain Application Example 2. In the following explanation, 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."

[0587] There is a need for a system that comprehensively evaluates the health and psychological state of care recipients, including the elderly, and provides more appropriate care and treatment. In particular, there is a challenge in analyzing the emotional state of care recipients in real time and proposing the optimal care plan based on that analysis.

[0588] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0589] In this invention, the server includes communication means for receiving patient physiological data, analysis means for analyzing the physiological data and extracting an estimated health status, and emotion analysis means for evaluating the patient's psychological data. This enables an integrated analysis of the health and emotional status of the person receiving care, making it possible to propose individually appropriate care and treatment methods.

[0590] "Communication means" refers to technological devices used to receive patient physiological data and health data from information terminals, etc.

[0591] "Analysis means" refers to a processing device that analyzes and extracts estimated health status based on received physiological data.

[0592] A "treatment suggestion device" is an output device that indicates the most suitable treatment or therapy for a patient based on the analysis results.

[0593] A "monitoring device" is a measuring device used to monitor collected health data from patients and detect abnormalities.

[0594] A "notification generation means" is a technical device for issuing a warning when an anomaly is detected.

[0595] A "report generation means" is a generation device for creating an analysis report that includes analysis results and recommended actions.

[0596] An "emotional analysis device" is an analytical device used to evaluate a patient's psychological data and analyze their emotional state.

[0597] A "care proposal device" is an output device that proposes a suitable care plan, taking into account the patient's psychological state.

[0598] This invention is a system for comprehensively evaluating the health and emotional state of a person receiving care and proposing an appropriate care plan. This system collects data via portable devices or information terminals, transmits it to a server for processing, and specifically receives the patient's physiological and psychological data from the information terminal using communication means.

[0599] The server processes the received data using analysis tools to extract the estimated health status. It also analyzes psychological data using emotion analysis tools to evaluate the patient's emotional state. Based on the analyzed data, the treatment suggestion tool proposes the optimal treatment or care plan. Furthermore, the monitoring tool continuously monitors the data, and if an abnormality is detected, the notification generation tool issues a warning.

[0600] The generated reports are created by a report generation system and provided to users and health professionals via electronic communication. This system enables the provision of individually tailored care to those receiving care.

[0601] For example, if a person receiving care reports to the application that they "haven't been able to sleep at night recently," the emotion analysis tool will analyze their anxiety level, and the health analysis tool will use that data to recommend measures to promote relaxation.

[0602] Example of a prompt:

[0603] "In managing the health of the elderly, it is necessary to propose care plans that take into account both physical symptoms and emotional state. Please propose appropriate measures for cases where an elderly person reports difficulty sleeping at night."

[0604] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0605] Step 1:

[0606] The terminal transmits physical symptoms and psychological emotion data entered by the care recipient to the server via communication means. The input includes text and sensor data indicating emotional states, and the output is generated as digitally converted information. This data forms the basis for analysis on the server.

[0607] Step 2:

[0608] The server inputs the received physiological data into an analysis system to estimate the patient's health status. During this process, a generative AI model is used to process the data and identify potential diseases. Furthermore, emotional analysis is performed on the input data to extract the patient's psychological state, and the analysis results are generated as output.

[0609] Step 3:

[0610] The server presents the optimal treatment based on the analysis results using a treatment suggestion system. The analysis results of health status and emotional state are used as input, and these are integrated to propose an optimal care plan. Specific treatment and care suggestions are generated as output and sent to the terminal.

[0611] Step 4:

[0612] The terminal displays the provided care plan to both care staff and care recipients. This information is output to the terminal screen in a visually understandable format. Users can also provide feedback at this stage.

[0613] Step 5:

[0614] The server continuously monitors health data using monitoring devices. The latest health data is used as input, and if an anomaly is detected, a notification generation device generates a warning. This output is sent to the terminal as an alert.

[0615] Step 6:

[0616] The server generates an analysis report using a report generation system based on all analysis results. The input includes information on health status, emotional state, and care plan, and this report is output electronically to the user and health professionals.

[0617] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0618] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0619] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0620] [Fourth Embodiment]

[0621] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0622] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0623] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0624] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0625] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0626] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0627] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0628] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0629] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0630] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0631] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0632] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0633] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0634] This invention is implemented as a diagnostic support system for medical settings. The program's processing details are described below in natural language.

[0635] First, users enter their symptoms using a smartphone application or web portal. This information includes the specific nature of the symptoms, their duration, and their severity. The device transmits the entered information to the server in real time, and secure communication protocols are used to protect data privacy.

[0636] The server uses an AI model to analyze the received symptom data. The AI ​​model interprets the content of the symptoms using natural language processing technology and predicts the most likely disease name by comparing it with a vast amount of medical case data. Furthermore, the server refers to the latest treatment database for each disease and selects the optimal treatment method.

[0637] Furthermore, if the user is wearing a wearable device, the device continuously monitors vital data and transmits that information to a server. Based on this data, the server detects any abnormalities and immediately sends an alert to the user. This alert function allows users to quickly understand their health status and take appropriate action.

[0638] A diagnostic report integrating the analysis results is automatically generated by the server. The report includes the diagnosis, recommended treatments, and urgency assessment, and is shared with healthcare professionals as needed. Based on this report, users can choose to consult further healthcare providers or opt for home care.

[0639] For example, if a user complains of "fatigue and a sore throat," the AI ​​model analyzes this as an early symptom of a cold or the flu and generates a diagnostic report recommending appropriate rest and the use of over-the-counter medication. Furthermore, through continuous monitoring, the server issues an alert prompting the user to seek medical attention if their temperature rises.

[0640] Thus, the present invention functions effectively as a diagnostic support system that assists physicians in their diagnoses and enhances patients' self-management abilities.

[0641] The following describes the processing flow.

[0642] Step 1:

[0643] Users launch a smartphone application or web portal and enter information about their symptoms. This includes specific symptoms, the duration since onset, and the severity of the symptoms. Once the user has finished entering the information, they press the submit button to save the information to their device.

[0644] Step 2:

[0645] The terminal converts the symptom data entered by the user into a structured format and sends it to the server using a secure communication protocol (e.g., HTTPS). This protects the confidentiality and privacy of the data.

[0646] Step 3:

[0647] The server inputs the received symptom data into the AI ​​model and begins analysis in real time. The AI ​​model uses natural language processing technology to interpret the input data and generates a list of possible disease names related to the symptoms.

[0648] Step 4:

[0649] The server matches the estimated disease name against an internal treatment database. This database is regularly updated with the latest medical information and contains the best treatment options for each disease.

[0650] Step 5:

[0651] Vital data from wearable devices and smartphones used by the user is collected by the terminal and sent to the server. The server monitors this data in real time and generates an alert if any abnormal values ​​are detected.

[0652] Step 6:

[0653] The server combines the analysis results and treatment recommendations to generate a detailed diagnostic report. This report describes the diagnosis, recommended treatments, and whether further tests are needed.

[0654] Step 7:

[0655] The server sends the generated diagnostic report to the user via email or in-app notification. It can also send the same information to medical professionals as needed.

[0656] Step 8:

[0657] Users review the received diagnostic report to determine recommended treatments and whether further medical consultations are necessary. They then use this information to manage their health in their daily lives.

[0658] (Example 1)

[0659] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0660] In modern healthcare, it is crucial for patients to respond quickly and appropriately to their symptoms, but many patients have difficulty immediately identifying their illness or the necessary treatment. Monitoring vital data and responding immediately to abnormalities in emergencies also pose challenges. Therefore, there is a need for a system that allows users to effectively manage their own health status.

[0661] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0662] In this invention, the server includes a device for acquiring user symptom information, a processing device for processing the symptom information and deriving the most likely disease name, and a method selection device for selecting the optimal treatment method based on the processing results. This enables users to quickly obtain an appropriate diagnosis and treatment method for their symptoms.

[0663] A "device for acquiring user symptom information" is a device that receives information about symptoms entered by the user and collects it in a format that can be processed as data.

[0664] A "processing device" is a device that analyzes accumulated symptom information and performs calculations and judgments to determine the most likely diagnosis.

[0665] A "treatment method selection device" is a device that has the function of selecting and presenting the optimal treatment plan for a disease name obtained through analysis.

[0666] A "monitoring device for acquiring vital signs information and monitoring abnormalities" is a device used to continuously collect users' health data and detect abnormalities.

[0667] A "notification device" is a device that issues warnings or instructions to users based on abnormalities detected by monitoring equipment.

[0668] A "document creation device" is a device that integrates information, including processing results and selected treatment methods, to generate a diagnostic document.

[0669] This invention functions as a medical diagnostic support system. Specific embodiments for carrying out this invention are described below.

[0670] The terminal allows users to input symptom information through a smartphone application or web portal. Users can enter specific symptoms, their duration, and their severity. This information is transmitted to the server via a secure communication method such as the HTTPS protocol.

[0671] The server uses a generative AI model to perform natural language processing to process the received symptom information. This AI model has been pre-trained on a large amount of medical case data and predicts the most likely disease name from the input symptoms. Based on the predicted disease name, the server also selects the optimal treatment method from the latest treatment database.

[0672] Furthermore, wearable devices worn by users continuously transmit vital signs information to a server via the terminal. Data such as heart rate and body temperature are monitored on the server, and if abnormal values ​​are detected, a notification is immediately sent to the user.

[0673] The generated diagnostic document includes the diagnosed illness, recommended treatment, and an assessment of urgency, which users can access via their device. It can also be provided to healthcare professionals via email or in-app message.

[0674] For example, if a user enters "headache and fever" into the application, the device sends this information to the server. The server uses a generative AI model to identify the possibility of a cold and suggests treatment options, including the use of over-the-counter medication. If a persistent fever is detected, the server will notify the user to seek medical attention.

[0675] An example of a prompt message is, "Based on the symptom data reported by the user, identify possible disease names and the most appropriate corresponding treatment."

[0676] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0677] Step 1:

[0678] The user opens a smartphone application or web portal and enters symptom information. This information includes specific symptoms, duration, and severity. The entered data is processed by the device and sent to the server via a secure communication protocol. This process allows the server to obtain the symptom data necessary for analysis.

[0679] Step 2:

[0680] The server receives symptom data sent from the terminal and analyzes the data using a generative AI model. Specifically, it interprets the input symptoms using natural language processing techniques and compares them with a large amount of medical case data. The input in this step is the user's symptom data, and the output is the most likely disease name. The server then uses this information to proceed to the next step.

[0681] Step 3:

[0682] The server consults the latest treatment database based on the disease name obtained through analysis. To select the appropriate treatment, the server retrieves relevant treatment options from the database and creates an optimized treatment plan based on them. The input for this step is the analyzed disease name, and the output is the selected treatment. The server then passes this information to the next report generation step.

[0683] Step 4:

[0684] When a user is wearing a wearable device, the terminal continuously monitors the user's vital signs and transmits the data to the server. The input is vital sign data acquired by the wearable device, which the terminal provides to the server in real time. The server monitors the received data for anomalies and immediately sends a notification if an anomaly is detected.

[0685] Step 5:

[0686] The server generates a diagnostic document based on analysis results, treatment options, and monitoring data. The document includes the diagnosis, recommended treatment, and urgency level, and is provided to the user. Input is all analysis results and selected treatments to date, and output is the diagnostic report.

[0687] Step 6:

[0688] The server provides the user with the final generated diagnostic document. The user can review this report on their device and, based on it, choose actions such as visiting a medical institution or staying home. The server also sends the diagnostic report to healthcare professionals using communication technology as needed. The output of this step is the diagnostic document provided to both the user and healthcare professionals.

[0689] (Application Example 1)

[0690] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0691] Conventional health management systems have made it difficult for residents to understand their own health status in real time and to quickly detect and address abnormalities early. Furthermore, the lack of technology to efficiently analyze collected health data and suggest optimal management methods has made it difficult to properly manage the health status of individuals and communities as a whole.

[0692] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0693] In this invention, the server includes communication means for receiving residents' health status information, analysis means for analyzing the health status information and extracting suspected diseases, management method presentation means for suggesting the optimal management method based on the analysis results, monitoring means for collecting residents' biometric information and detecting abnormalities, warning generation means for generating notifications when abnormalities are detected, and report generation means for generating an evaluation report including the analysis results and management methods. This allows residents to understand their own health status in real time, enabling early detection of abnormalities and appropriate responses. It can also contribute to understanding the health trends of the entire region.

[0694] "Residents" are people who live in a specific area and use the medical services and health management systems of that community.

[0695] "Health status information" refers to subjective health data and symptom records provided by residents, and is used to assess their health status.

[0696] "Biometric information" refers to objective data obtained from the bodies of residents, including information such as heart rate and body temperature.

[0697] "Communication means" refers to devices or methods for collecting residents' health status information and transmitting it to a server, and includes internet communication and wireless communication technologies.

[0698] "Analysis means" refers to a device or method that processes data using AI technology based on received health status information and extracts estimated diseases.

[0699] A "monitoring system" is a system that continuously collects biometric information and detects abnormalities, and is equipped with a function to warn the user when an abnormality occurs.

[0700] A "management method presentation means" is a device or method that provides users with the optimal health management method based on analyzed health status data.

[0701] A "warning generation system" is a system that issues notifications to residents or designated parties when an abnormality is detected in biometric information.

[0702] An "evaluation report" is a document that integrates analysis results and recommended health management methods to provide a clear and concise overview of the residents' health status.

[0703] This invention is a system for managing residents' health status in real time and detecting abnormalities early. The detailed configuration for realizing this system is described below.

[0704] The server receives health status and biometric information transmitted by residents from smart devices (e.g., smartphones and smartwatches). Internet communication is primarily used for communication, and a secure communication protocol (a separately configured method) is employed to protect data privacy.

[0705] Information entered on the terminal is immediately transmitted to the server. The server uses AI technology (e.g., generative AI models using TensorFlow) to analyze the information and identify diseases that can be inferred from the health status information provided by residents. Furthermore, abnormal values ​​are detected in the collected biometric information using the AI ​​model, and the results are reflected in management reports as a risk assessment.

[0706] The optimal management methods based on the analysis results are presented to residents as if they were advice from a medical professional. This allows residents to take appropriate actions to maintain their health in their daily lives.

[0707] Furthermore, if biometric data transmitted through a device reaches abnormal levels, the server immediately generates a warning. This warning serves as an important source of information to prompt residents to take swift action. The content of the warning is delivered to residents as an in-app message on their smart devices or via email.

[0708] (Specific example)

[0709] For example, if a resident reports "mild headache and fatigue" and their smartwatch shows a temperature higher than normal, the server will estimate this to be a temporary illness due to overwork and generate a report recommending that they take appropriate rest. This report is sent to the resident's device, allowing them to quickly understand their health status and take rest or seek medical attention as needed.

[0710] Example prompt to input into the generating AI model: "The user reported symptoms of 'mild headache and fatigue,' and body temperature data from a smartwatch is 1 degree higher than normal. Based on these conditions, please list possible illnesses and recommended actions."

[0711] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0712] Step 1:

[0713] The device acquires health status information and biometric information (such as heart rate and body temperature) entered by residents. The entered information is collected in real time via smartphones and smartwatches.

[0714] Step 2:

[0715] The device transmits acquired health status and biometric information to a server via the internet. Secure communication protocols such as SSL are used for this data transmission to ensure security.

[0716] Step 3:

[0717] The server stores the received health status information in a database and performs analysis using a generative AI model. Specifically, it interprets the input symptoms using natural language processing and extracts the estimated disease name. Based on this analysis, the server outputs a list of disease names.

[0718] Step 4:

[0719] Based on the analysis results, the server proposes management methods for possible diseases. To do this, it queries the latest medical treatment databases for appropriate management methods and selects the information that should be provided to the population. The output is a list of recommended management methods.

[0720] Step 5:

[0721] The server detects anomalies in biometric data. Based on the acquired biometric data, it applies an algorithm to determine abnormal values ​​and generates a warning if the threshold is exceeded. This warning is immediately sent to residents and, if necessary, to relevant health professionals.

[0722] Step 6:

[0723] The server generates an evaluation report that includes analysis results and proposed management methods. This report is organized in a format that is easy for residents to understand and sent to them as a message within the smartphone application or as an email.

[0724] Step 7:

[0725] Based on the evaluation report received, users can decide on their own health management actions. If necessary, they can consult with a healthcare professional and consider visiting an appropriate medical institution as the next step.

[0726] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0727] The present invention is implemented in a medical diagnostic support system that incorporates an emotion engine capable of recognizing patients' emotions. This system integrates and analyzes patients' symptom data and emotion data to provide more appropriate treatment recommendations.

[0728] First, the user enters their physical symptoms using a smartphone or web interface. This information can include specific symptoms typically reported by patients, as well as subjective feelings about them (e.g., "severe pain," "anxiety," etc.). The device converts this information into a digital format and sends it to the server.

[0729] Following this process, the server feeds the received symptom data into an advanced AI model for analysis. Furthermore, the system utilizes an emotion engine to evaluate the user's emotional state in real time based on user input data and separately collected data (e.g., voice tone, facial recognition, text analysis, etc.). This allows for analysis that takes the user's psychological state into account.

[0730] Next, the server integrates symptom data and emotional data, and based on this, selects the optimal treatment method. When selecting a treatment method, psychological aspects are also considered; for example, if the user's stress level is high, therapies that promote relaxation will be prioritized.

[0731] In addition, vital data transmitted from wearable devices and smartphones is also monitored. If this data shows abnormal values, the server immediately generates an alert and notifies the user. Based on the analysis results of the emotion engine, the alert may include appropriate stress relief measures and mental health support.

[0732] Finally, the server generates a detailed diagnostic report based on these analysis results and sends it to the user via email or in-app notification. This report includes recommended treatments, suspected illnesses, and mental health recommendations based on the user's emotional state. It is also shared with healthcare professionals as needed to support a comprehensive diagnosis and recommendations.

[0733] For example, if a user reports "recent anxiety and headaches," the emotion engine can analyze the user's text input to detect a high level of anxiety. Based on this, the server will include recommendations for stress management techniques in addition to standard headache treatments in the user's report. In this way, the system is configured to support the patient's overall health.

[0734] The following describes the processing flow.

[0735] Step 1:

[0736] Users input symptoms related to their physical condition using their smartphones or computers via an application or web interface. They describe specific physical symptoms they experience and associated emotions (e.g., "anxiety," "depression," etc.) in an input form.

[0737] Step 2:

[0738] The terminal receives the entered symptom and emotion data and converts it into the appropriate format. This data is sent to the server, and security protocols are applied to protect the communication.

[0739] Step 3:

[0740] The server feeds the received data into an AI model to analyze the symptoms. This allows the model to estimate possible diseases and further refine the analysis by referencing relevant medical databases.

[0741] Step 4:

[0742] The server activates the emotion engine and analyzes the user's emotional data in detail. It uses text analysis, and in some cases voice tone and facial image analysis, to quantify or categorize the user's psychological state.

[0743] Step 5:

[0744] The server integrates the analyzed symptom and emotional data and selects the optimal treatment method based on this information. In situations where psychological state plays a role, it proposes a treatment method that takes into account the balance between mind and body.

[0745] Step 6:

[0746] If a user is using a wearable device, the device sends the vital data it collects to a server. The server monitors this data and immediately sends an alert to the user if an anomaly is detected.

[0747] Step 7:

[0748] The server generates a detailed diagnostic report based on the final analysis results. This report includes recommended treatments, mental care methods based on emotional state, and other health advice.

[0749] Step 8:

[0750] The server sends the generated diagnostic report to the user via email or in-app notification. If necessary, the report is also shared with medical professionals and used for follow-up at healthcare facilities.

[0751] Step 9:

[0752] Users review the diagnostic report they receive and incorporate recommended treatments and mental care techniques into their daily lives. This allows users to manage their health more comprehensively.

[0753] (Example 2)

[0754] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0755] Conventional medical diagnostic systems often rely solely on a patient's physical symptom data for diagnosis, failing to consider the patient's emotional state, making it difficult to select appropriate treatment. Therefore, there is a need for more appropriate treatment recommendations based on a comprehensive analysis that includes the patient's psychological state.

[0756] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0757] In this invention, the server includes communication means for receiving patient symptom data, analysis means for analyzing the symptom data and extracting disease names, and emotion evaluation means for evaluating emotional states and generating emotion data. This enables a comprehensive diagnosis that takes into account the physical and emotional aspects of the patient, and the suggestion of more appropriate treatment methods.

[0758] "Communication means" refers to a device or program that has the function of receiving symptom data from patients and transferring it to a server.

[0759] "Analysis means" refers to a device or program that processes received symptom data and performs calculations to extract the estimated disease name.

[0760] "Emotional evaluation means" refers to a device or program that has the function of performing voice, visual, and text analysis in order to evaluate the emotional state of a user and generate emotional data.

[0761] "Treatment selection means" refers to a device or program that has the function of integrating analyzed symptom data and emotional data and presenting the optimal treatment method based on that.

[0762] "Monitoring means" refers to a device or program that has a monitoring function to collect a patient's vital data and detect abnormalities.

[0763] "Alert generation means" refers to a device or program that has the function of generating a warning and notifying the user when an anomaly is detected.

[0764] "Report generation means" refers to a device or program that has the function of creating a diagnostic report including analysis results and treatment methods.

[0765] An "information processing device" refers to a device that has the function of processing data on devices such as wearable devices and smartphones and transmitting it to a server.

[0766] This invention relates to a medical diagnostic support system that provides more appropriate treatment methods by comprehensively analyzing a patient's physical and emotional information.

[0767] Users input their physical symptoms using a smartphone or web interface. This includes general symptom descriptions and information about their feelings (e.g., "I feel anxious," "I have a headache"). The device converts this information into a digital format and sends it to the server via a secure communication protocol.

[0768] The server receives symptom data and analyzes it using algorithms. A pre-trained generative AI model is used for the analysis, which estimates the disease name. Furthermore, emotion data of the user is generated using emotion evaluation methods that utilize voice analysis software and facial recognition tools. This enables an analysis that also takes the user's psychological state into account.

[0769] After symptom and emotional data are integrated, the server selects the optimal treatment. For example, if the analysis results indicate a high level of anxiety, stress reduction therapy can be recommended. Furthermore, if an abnormality is detected based on the collected vital data, an alert is generated and immediately notified to the user's device.

[0770] A detailed diagnostic report is generated by the server and sent to the user via email or in-app notification. This report includes recommended treatments and mental health support based on emotional state, and will be shared with medical professionals as needed.

[0771] For example, if a user reports "recent anxiety and headaches," the emotion engine analyzes the user's input and recognizes a high level of anxiety. Based on this, the server generates and presents a report that includes not only "standard headache treatments" but also "stress management techniques."

[0772] Examples of prompts to input into the generating AI model include, "Please tell me the appropriate way to deal with headaches. In particular, I've been feeling very anxious lately." This allows the system to provide appropriate medical support tailored to the user's specific symptoms and emotional state.

[0773] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0774] Step 1:

[0775] Users input their physical symptoms and emotional state using a smartphone or web interface. The input data includes information such as "I have a headache" or "I feel very anxious." The device uses natural language processing technology to convert this information into digital data and transmits it to the server via a secure communication protocol. The input data is raw symptom text data, while the output data is in a structured digital format.

[0776] Step 2:

[0777] The server feeds structured data received from the terminal into a generating AI model. Based on the received data, the AI ​​model uses machine learning algorithms to analyze it and extract the estimated disease name. In this process, symptom data is input, and the disease name and related information are generated as output.

[0778] Step 3:

[0779] The server evaluates the user's emotional state using emotion assessment tools. Voice analysis, facial recognition, and text analysis are performed, and emotion data is generated based on user input and data collected through wearable devices. The input is unstructured data of the user's experience and emotional state, and the output generates emotion scores and emotion categories.

[0780] Step 4:

[0781] The server integrates symptom and emotional data and selects the optimal treatment using treatment selection tools. An AI model analyzes the integrated data and prioritizes treatments based on overall health status, including psychological factors. The input is integrated symptom and emotional information, and the output is a list of recommended treatments and health support.

[0782] Step 5:

[0783] The server receives vital data from the user's wearable device as needed and detects anomalies using monitoring means. If an anomaly is detected, an alert generation means immediately generates a warning and notifies the user. The input is real-time vital data, and the output is anomaly detection alert information.

[0784] Step 6:

[0785] The server generates a detailed diagnostic report based on the analysis results so far and sends it to the user. The report generation method creates a report that summarizes recommended treatments, suspected diseases, and mental health support based on emotional state. The input is analyzed medical and emotional data, and the output is a diagnostic report for the user and medical professionals.

[0786] (Application Example 2)

[0787] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0788] There is a need for a system that comprehensively evaluates the health and psychological state of care recipients, including the elderly, and provides more appropriate care and treatment. In particular, there is a challenge in analyzing the emotional state of care recipients in real time and proposing the optimal care plan based on that analysis.

[0789] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0790] In this invention, the server includes communication means for receiving patient physiological data, analysis means for analyzing the physiological data and extracting an estimated health status, and emotion analysis means for evaluating the patient's psychological data. This enables an integrated analysis of the health and emotional status of the person receiving care, making it possible to propose individually appropriate care and treatment methods.

[0791] "Communication means" refers to technological devices used to receive patient physiological data and health data from information terminals, etc.

[0792] "Analysis means" refers to a processing device that analyzes and extracts estimated health status based on received physiological data.

[0793] A "treatment suggestion device" is an output device that indicates the most suitable treatment or therapy for a patient based on the analysis results.

[0794] A "monitoring device" is a measuring device used to monitor collected health data from patients and detect abnormalities.

[0795] A "notification generation means" is a technical device for issuing a warning when an anomaly is detected.

[0796] A "report generation means" is a generation device for creating an analysis report that includes analysis results and recommended actions.

[0797] An "emotional analysis device" is an analytical device used to evaluate a patient's psychological data and analyze their emotional state.

[0798] A "care proposal device" is an output device that proposes a suitable care plan, taking into account the patient's psychological state.

[0799] This invention is a system for comprehensively evaluating the health and emotional state of a person receiving care and proposing an appropriate care plan. This system collects data via portable devices or information terminals, transmits it to a server for processing, and specifically receives the patient's physiological and psychological data from the information terminal using communication means.

[0800] The server processes the received data using analysis tools to extract the estimated health status. It also analyzes psychological data using emotion analysis tools to evaluate the patient's emotional state. Based on the analyzed data, the treatment suggestion tool proposes the optimal treatment or care plan. Furthermore, the monitoring tool continuously monitors the data, and if an abnormality is detected, the notification generation tool issues a warning.

[0801] The generated reports are created by a report generation system and provided to users and health professionals via electronic communication. This system enables the provision of individually tailored care to those receiving care.

[0802] For example, if a person receiving care reports to the application that they "haven't been able to sleep at night recently," the emotion analysis tool will analyze their anxiety level, and the health analysis tool will use that data to recommend measures to promote relaxation.

[0803] Example of a prompt:

[0804] "In managing the health of the elderly, it is necessary to propose care plans that take into account both physical symptoms and emotional state. Please propose appropriate measures for cases where an elderly person reports difficulty sleeping at night."

[0805] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0806] Step 1:

[0807] The terminal transmits physical symptoms and psychological emotion data entered by the care recipient to the server via communication means. The input includes text and sensor data indicating emotional states, and the output is generated as digitally converted information. This data forms the basis for analysis on the server.

[0808] Step 2:

[0809] The server inputs the received physiological data into an analysis system to estimate the patient's health status. During this process, a generative AI model is used to process the data and identify potential diseases. Furthermore, emotional analysis is performed on the input data to extract the patient's psychological state, and the analysis results are generated as output.

[0810] Step 3:

[0811] The server presents the optimal treatment based on the analysis results using a treatment suggestion system. The analysis results of health status and emotional state are used as input, and these are integrated to propose an optimal care plan. Specific treatment and care suggestions are generated as output and sent to the terminal.

[0812] Step 4:

[0813] The terminal displays the provided care plan to both care staff and care recipients. This information is output to the terminal screen in a visually understandable format. Users can also provide feedback at this stage.

[0814] Step 5:

[0815] The server continuously monitors health data using monitoring devices. The latest health data is used as input, and if an anomaly is detected, a notification generation device generates a warning. This output is sent to the terminal as an alert.

[0816] Step 6:

[0817] The server generates an analysis report using a report generation system based on all analysis results. The input includes information on health status, emotional state, and care plan, and this report is output electronically to the user and health professionals.

[0818] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0819] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0820] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0821] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0822] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0823] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0824] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0825] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0826] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0827] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0828] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0829] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0830] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0831] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0832] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0833] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0834] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0835] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0836] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0837] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0838] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0839] The following is further disclosed regarding the embodiments described above.

[0840] (Claim 1)

[0841] A means of communication for receiving patient symptom data,

[0842] An analysis means for analyzing the aforementioned symptom data and extracting the estimated disease name,

[0843] A treatment method presentation mechanism that presents the optimal treatment method based on the analysis results,

[0844] A monitoring system for collecting patient vital data and detecting abnormalities,

[0845] An alert generation means that generates a warning when an anomaly is detected,

[0846] A report generation means for generating a diagnostic report that includes the aforementioned analysis results and treatment methods,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, which receives the patient's vital data from a wearable device or a smartphone application.

[0850] (Claim 3)

[0851] The system according to claim 1, which transmits the diagnostic report to the user and medical professionals as an email or in-app message.

[0852] "Example 1"

[0853] (Claim 1)

[0854] A device for acquiring user symptom information,

[0855] A processing device that processes the aforementioned symptom information and derives the most likely disease name,

[0856] A method selection device for selecting the optimal treatment method based on processing results,

[0857] A monitoring device for acquiring vital signs information of users and monitoring for abnormalities,

[0858] A notification sending device for sending notifications when an anomaly is detected,

[0859] A document creation device for creating a diagnostic document including the processing results and treatment method,

[0860] A system equipped with these features.

[0861] (Claim 2)

[0862] The system according to claim 1, which acquires the user's vital signs information from a portable terminal or mobile communication device.

[0863] (Claim 3)

[0864] The system according to claim 1, which provides the aforementioned diagnostic document to users and medical professionals using communication technology.

[0865] "Application Example 1"

[0866] (Claim 1)

[0867] A means of communication for receiving information on the health status of residents,

[0868] An analytical means for analyzing the aforementioned health status information and extracting estimated diseases,

[0869] A means for presenting management methods that proposes the optimal management method based on the analysis results,

[0870] A monitoring system for collecting residents' biometric information and detecting abnormalities,

[0871] A warning generation means that generates a notification when an anomaly is detected,

[0872] A report generation means for generating an evaluation report including the aforementioned analysis results and management method,

[0873] A system that includes this.

[0874] (Claim 2)

[0875] The system according to claim 1, which receives the biometric information of the aforementioned residents from a mobile information terminal or mobile terminal application.

[0876] (Claim 3)

[0877] The system according to claim 1, wherein the evaluation report is sent to the user and health professionals as an email or in-app message.

[0878] "Example 2 of combining an emotion engine"

[0879] (Claim 1)

[0880] A means of communication for receiving patient symptom data,

[0881] An analysis means for analyzing the aforementioned symptom data and extracting the estimated disease name,

[0882] A means for evaluating a user's emotional state and generating emotional data,

[0883] A treatment selection method that integrates and analyzes symptom data and emotional data to suggest the optimal treatment method,

[0884] A monitoring system for collecting patient vital data and detecting abnormalities,

[0885] An alert generation means that generates a warning when an anomaly is detected,

[0886] A report generation means for generating a diagnostic report that includes the aforementioned analysis results and treatment methods,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, which receives the patient's vital data and emotional data from a wearable device or information processing device.

[0890] (Claim 3)

[0891] The system according to claim 1, wherein the diagnostic report is transmitted to the user and medical professionals via an information transmission means.

[0892] "Application example 2 when combining with an emotional engine"

[0893] (Claim 1)

[0894] A means of communication for receiving patient physiological data,

[0895] An analytical means for analyzing the aforementioned physiological data and extracting the estimated health status,

[0896] A treatment suggestion means that suggests the optimal treatment based on the analysis results,

[0897] A monitoring system for collecting patient health data and detecting abnormalities,

[0898] A notification generation means that generates a warning when an anomaly is detected,

[0899] A report generation means for generating an analysis report including the analysis results and treatment,

[0900] A means of analyzing emotions for evaluating patients' psychological data,

[0901] A care proposal method that proposes a care plan that takes into account the patient's psychological state,

[0902] A system that includes this.

[0903] (Claim 2)

[0904] The system according to claim 1, which receives the patient's health data from a portable device or information terminal.

[0905] (Claim 3)

[0906] The system according to claim 1, wherein the aforementioned analysis report is transmitted to the user and health professionals as an electronic communication means or as a message on an information terminal. [Explanation of Symbols]

[0907] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of communication for receiving information on the health status of residents, An analytical means for analyzing the aforementioned health status information and extracting estimated diseases, A means for presenting management methods that proposes the optimal management method based on the analysis results, A monitoring system for collecting residents' biometric information and detecting abnormalities, A warning generation means that generates a notification when an anomaly is detected, A report generation means for generating an evaluation report including the aforementioned analysis results and management method, A system that includes this.

2. The system according to claim 1, which receives the biometric information of the aforementioned residents from a mobile information terminal or a mobile terminal application.

3. The system according to claim 1, wherein the evaluation report is sent to the user and health professionals as an email or in-app message.

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