system

Abstract

We provide the system. [Solution] A means of receiving information about damage from the user, A means for analyzing the relevant damage information using a generative AI model and generating a diagnostic result, A means for generating a treatment plan based on the generated diagnostic results, A means for presenting the aforementioned treatment plan to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an emergency medical situation, there is a problem that non-experts cannot receive a quick and accurate diagnosis and lack the knowledge to take appropriate emergency measures. In particular, in a situation where immediate access to a medical institution is not possible, incorrect responses may cause further health damage. Therefore, there is a need for a tool that provides a quick and reliable diagnosis and treatment plan and supports emergency measures.

Means for Solving the Problems

[0005] This invention provides a system that efficiently collects information about injuries from users and analyzes that information using a generated AI model to obtain immediate diagnostic results. Furthermore, it constructs an optimal treatment plan based on the generated diagnostic results and presents it to the user. This allows users without specialized medical knowledge to quickly understand appropriate first aid and subsequent medical actions. In addition, it provides online consultations and information on appropriate medical institutions as needed, realizing comprehensive support.

[0006] A "user" is an individual or group that operates the system and enters damage information.

[0007] "Injury information" refers to data entered by the user regarding certain physical conditions or circumstances.

[0008] A "generative AI model" is an artificial intelligence system that uses machine learning techniques to analyze data and generate diagnostic results.

[0009] The "diagnosis result" is the conclusion of a medical judgment, generated based on damage information analyzed by a generative AI model.

[0010] A "treatment plan" is a specific plan for appropriate emergency treatment and therapy, formulated based on the generated diagnostic results.

[0011] "Online consultation" refers to a function that allows users to ask questions and seek advice from medical professionals through a digital platform.

[0012] "Medical institution information" refers to data about related hospitals, clinics, and other facilities that the system provides to users. [Brief explanation of the drawing]

[0013] [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]It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

MODE FOR CARRYING OUT THE INVENTION

[0014] [[ID=4,3]] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered 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.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered 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, etc.

[0019] In the following embodiments, the numbered 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), etc.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] The system of this invention has a configuration in which a user's terminal and a server located in the cloud work in cooperation. The user inputs detailed information about injuries and symptoms via the terminal. This information is diverse, including the location, characteristics, circumstances of the injury, past medical history, and allergy information. The terminal transmits this information to the server.

[0035] The server utilizes advanced generative AI models to analyze the information it receives. Specifically, it performs diagnostic processing to identify the type and severity of damage based on the input information. The model is trained on large amounts of medical data and guidelines, giving it the ability to make rapid and accurate diagnoses. After the diagnosis is generated, the server uses it to build an effective treatment plan. The treatment plan includes first aid methods, recommended medications, and the type of medical facility to visit.

[0036] The terminal receives diagnostic results and treatment plans from the server and presents them visually to the user. This can include illustrations and video links demonstrating first aid procedures. If the user desires further assistance, a support system is available that allows them to consult with medical professionals online or access information on nearby medical facilities.

[0037] As a concrete example, consider a scenario where a user deeply cuts their finger while cooking. The user inputs details of the situation into the terminal. The server uses a generated AI model to determine how serious the wound is. For example, it might diagnose "heavy bleeding but not reaching an artery" and then create a treatment plan stating, "Wash the wound with running water and apply pressure to stop the bleeding, and go to the hospital if necessary." Along with this information, the terminal presents the user with a visual guide showing the specific method of applying pressure to stop the bleeding.

[0038] Thus, this system aims to support non-specialists in providing quick and appropriate responses even in complex medical situations.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user uses a device to enter information about their injury. This includes details such as the location and condition of the injury, the circumstances under which it occurred, past medical history, and allergy information.

[0042] Step 2:

[0043] The terminal formats the entered information appropriately and sends it to the server using a communication protocol.

[0044] Step 3:

[0045] The server passes the received information to a generating AI model, which then begins the analysis. This process identifies the type and severity of injuries from the input data and makes the best possible diagnosis.

[0046] Step 4:

[0047] The generative AI model generates diagnostic results by comparing them with medical guidelines using an accumulated database and automated learning. These results include information about the extent and cause of the damage.

[0048] Step 5:

[0049] The server creates a treatment plan based on the diagnosis. This plan includes specific first-aid procedures, recommended medications, and the type of medical facility to visit.

[0050] Step 6:

[0051] The server sends the generated diagnostic results and treatment plan to the terminal.

[0052] Step 7:

[0053] The terminal displays information received from the server to the user. Illustrations and video links illustrating first aid procedures are added to support the user in dealing with the situation more easily.

[0054] Step 8:

[0055] If the user requires further assistance, the device offers an online consultation option. The user can request access to a medical professional on the spot.

[0056] Step 9:

[0057] Furthermore, the device will provide information about nearby medical facilities based on the user's location, thereby supporting appropriate follow-up.

[0058] (Example 1)

[0059] 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."

[0060] In today's healthcare environment, providing prompt and appropriate first aid to non-medical professionals facing urgent health conditions is challenging. Furthermore, obtaining necessary medical information and specific instructions can be time-consuming, hindering the ability to respond quickly in situations requiring immediate action.

[0061] 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.

[0062] In this invention, the server includes means for acquiring symptom information from the user, means for analyzing the relevant information using a generative AI model based on large-scale data to generate a health condition diagnosis, and means for formulating an emergency treatment plan based on the generated health condition diagnosis. This enables the user to immediately obtain rapid and accurate emergency treatment information.

[0063] A "user" refers to an individual or entity that uses a system to input information about their own health status and symptoms.

[0064] "Symptom information" refers to detailed data about the user's health condition, including the location and characteristics of injuries, circumstances of occurrence, past medical history, and allergy information.

[0065] "Large-scale data" refers to a collection of information encompassing a large amount of medical-related information and guidelines that AI models use for analysis.

[0066] A "generative AI model" refers to a machine learning model trained on a vast dataset, which is a program or algorithm used to analyze symptom information obtained from users and diagnose their health status.

[0067] "Health status diagnosis" refers to information that provides an evaluation and judgment regarding the user's symptoms, based on the results of analysis by a generative AI model.

[0068] An "emergency response plan" refers to instructions or recommendations, including specific emergency response measures and treatment methods, that are formulated based on the diagnostic results of a generated AI model.

[0069] "Visual means of providing information" refers to functions that display information in the form of text, illustrations, or video links in order to make diagnostic results and first aid plans easier for users to understand.

[0070] "Illustrations and video links" are visual information used to supplement explanations related to first aid plans and health assessments, and are provided in a user-friendly and easy-to-understand format.

[0071] "Telemedicine consultation" refers to a service that connects users with medical professionals online, allowing them to receive real-time health consultations and guidance.

[0072] "Providing medical facility information" refers to a function that provides contact information and locations of appropriate medical institutions based on the user's location.

[0073] An embodiment of the present invention consists of a server operating on the cloud and a terminal operated by the user. The user inputs information about their symptoms via the terminal. This can be easily done by using a text input interface or selection-based items to provide detailed information about symptoms, medical history, and allergy information.

[0074] The terminal transmits the entered information to the server via the internet. This transmission uses a secure communication method employing the SSL / TLS protocol. A generative AI model running on the server analyzes the received symptom information. Specifically, the generative AI model is pre-trained with a large amount of medical data and diagnoses the user's health condition based on the input data.

[0075] Once the diagnosis is complete, the server automatically develops an emergency response plan based on the information obtained from the AI ​​model. This plan includes recommended treatments, relevant medication information, and specific steps to obtain further medical assistance.

[0076] The terminal displays the diagnostic results and emergency response plan received from the server on its interface to visually provide the user with the information. Illustrations and related video links can be attached to aid understanding. Based on this information, the user can take quick and accurate action.

[0077] As a concrete example, consider a scenario where a user has deeply cut their finger. An example prompt might be, "User: I cut my finger while cooking. What should I do?" Based on this information, the generating AI model diagnoses that "there is bleeding, but it has not reached an artery" and proposes a plan such as "wash with running water, apply pressure to stop the bleeding, and go to the emergency room if necessary." This information is displayed clearly to the user on the device.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users input information about their health status and symptoms into the device. Specifically, they use a dedicated input screen to enter details of their symptoms, the location of the injury, the circumstances of its occurrence, their past medical history, allergy information, etc., in text format. This entered information is temporarily stored in the device's database.

[0081] Step 2:

[0082] The terminal sends the information entered by the user to the server. During this process, the data is transmitted securely using encryption technology, thus protecting personal information. The server then prepares the transmitted data for analysis.

[0083] Step 3:

[0084] The server inputs the received data into a generating AI model. The AI ​​model is pre-trained with medical data and processes the input data to diagnose the patient's health condition based on their symptoms. Based on the analysis, diagnostic information is generated, and the level of medical intervention required is determined.

[0085] Step 4:

[0086] Based on the diagnostic information obtained as output from the generated AI model, the server formulates an emergency treatment plan. It automatically generates a plan that includes specific treatment details, necessary medications, and information on recommended medical facilities. This treatment plan data is temporarily stored on the server.

[0087] Step 5:

[0088] The server transmits the formulated first-aid plan and diagnostic information to the terminal. The receiving terminal reads this information and displays it visually to the user using a user interface (UI). This allows the user to understand specific first-aid procedures and immediately put them into action.

[0089] Step 6:

[0090] The information provided by the device includes not only text but also illustrations and video links to assist with first aid. Through this visual information, users can easily understand instructions and provide care themselves. Furthermore, if additional support is needed, they can access online medical consultations and information on nearby medical facilities.

[0091] (Application Example 1)

[0092] 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."

[0093] There is a challenge in quickly and accurately assessing the severity of injuries sustained by security personnel in the field and providing appropriate first aid. Furthermore, mishandling of injuries can worsen the condition, making immediate guidance to the appropriate medical facility essential.

[0094] 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.

[0095] In this invention, the server includes means for receiving information about damage from a user, means for analyzing the relevant damage information using a generative AI model and generating a diagnosis result, means for generating a treatment plan based on the generated diagnosis result, and means for determining the severity of an injury sustained during security activities and providing appropriate first aid. This makes it possible to quickly determine the severity of an injury and receive appropriate treatment and guidance to a medical institution even if a security officer is injured on-site.

[0096] A "user" is someone who uses the system to input injury information and receives diagnostic results and treatment plans.

[0097] "Injury information" refers to detailed information about injuries or physical abnormalities entered by the user, specifically including the affected area, characteristics, circumstances of occurrence, past medical history, and allergy information.

[0098] A "generative AI model" is an artificial intelligence-based model used to analyze input injury information and generate diagnostic results, and is trained on large-scale medical data and guidelines.

[0099] "Diagnosis results" refer to information about the type and severity of damage provided as a result of analysis by a generative AI model.

[0100] A "treatment plan" is a plan developed based on the diagnosis, including methods of first aid, recommended medications, and the type of medical institution to visit.

[0101] "First aid" refers to treatment methods that are recommended to be performed quickly and appropriately in the event of an injury, and are means of preventing the worsening of symptoms.

[0102] "Security activities" refer to on-site work by personnel to protect specific areas or assets, and include cases where those engaged in these activities are injured.

[0103] This system consists of a user-facing information processing device and a cloud server that hosts the generated AI models. The user inputs detailed data on injuries sustained during their activities via the information processing device. Specifically, they record the location and characteristics of the injuries, the circumstances of their occurrence, past medical history, and allergy information. The information processing device then transmits this data to the cloud server.

[0104] The cloud server has the capability to scrutinize and analyze received injury information using a generative AI model. The model is pre-trained with large-scale medical data and guidelines, and identifies the type and severity of injury by comparing it with existing data. After the diagnosis is generated, the system uses that information to build an effective treatment plan. This plan includes first aid methods, recommended medications, and the type of medical facility to visit if necessary.

[0105] The terminal reconstructs the diagnostic results and treatment plan received from the cloud server and presents them visually to the user. This can include detailed illustrations and video links, providing the user with immediately useful instructions. If the user requires additional support, the system allows them to access online consultations or information on nearby medical facilities.

[0106] As a concrete example, let's consider a scenario where a security officer suddenly sprains their ankle while on-site. The officer quickly records the details of their symptoms on a device, and a cloud server uses a generated AI model to diagnose it as a "minor sprain." Next, a treatment plan is provided, such as "immobilize the ankle, apply ice, and see an orthopedic specialist if the pain persists." This plan is presented as specific instructions from the device.

[0107] Examples of prompt statements for a generative AI model are as follows:

[0108] Based on the details below, diagnose the type and severity of the injury and suggest appropriate first aid measures.

[0109] Injured area: Ankle

[0110] Symptoms: Swelling and pain

[0111] Background: Fell while on patrol

[0112] Past injuries: None

[0113] Allergies: None

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] The user uses a device to enter detailed information about the injury. This data includes the location, characteristics, circumstances of the injury, past medical history, and allergy information. This information is collected as digital data and sent directly to a cloud server.

[0117] Step 2:

[0118] The server structures the received damage information as a prompt for the generating AI model. This prompt statement includes all the details about the input damage. Next, this prompt statement is input to the generating AI model to perform a diagnosis of the type and severity of the damage. At this stage, the input raw data is converted into an analyzable data structure.

[0119] Step 3:

[0120] The generating AI model identifies the type and severity of the injury as a diagnostic result. This diagnostic result is generated by comparing it with the medical database maintained by the model. The server receives this result, formats it into a format usable in the next step, and then saves it.

[0121] Step 4:

[0122] The server automatically generates an effective treatment plan based on the generated diagnostic results. This plan includes first aid procedures, recommended medications, and the type of appropriate medical facility. The server then cross-references each diagnostic result and performs further data calculations to select the optimal treatment method.

[0123] Step 5:

[0124] The terminal visualizes and presents diagnostic results and treatment plans received from the server to the user. The display on the terminal may include illustrations and video links to first aid procedures. Based on this information, the user can take immediate action regarding the injury. The terminal optimizes communication efficiency by selecting the most suitable display format for the user and processing the information accordingly.

[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] In the system of the present invention, by integrating an emotion engine that recognizes the user's emotions, it is possible to provide more personalized diagnoses and treatment plans. In this system, a terminal and a server work together to receive and analyze input from the user.

[0127] The user first uses a device to input detailed information about their injury or physical condition. During this input process, an emotion engine infers the user's emotional state from their voice tone and input text, analyzing stress levels, anxiety, and other factors. The device then sends this analysis, along with the resulting information, to a server.

[0128] Based on the information received, the server begins analyzing damage information using a generative AI model. In addition to generating diagnostic results, a treatment plan is created that also takes into account the user's emotional state. Specifically, depending on the analysis results from the emotion engine, relaxation techniques are suggested if stress levels are particularly high. In this way, it becomes possible to support a more holistic view of health.

[0129] The generated diagnostic results and treatment plan are sent to the device and presented to the user. This includes specific first aid measures based on the advice and individual recommendations. If the user requires further assistance, features are also provided to offer online consultations and information on medical facilities, all presented in a user-friendly interface.

[0130] As a concrete example, consider a scenario where a user has cut their finger and is experiencing intense anxiety. The user enters details into the device. An emotion engine analyzes the user's tense voice and keystrokes during input, identifying a high level of anxiety. The server, using a generative AI model, diagnoses the situation as "there is bleeding, but it is not serious" and suggests "apply pressure to stop the bleeding, disinfect the wound, and then rest for 30 minutes." Simultaneously, links to relaxation music and breathing exercises are displayed to support the user in alleviating anxiety.

[0131] Thus, the system of the present invention is designed to provide a comprehensive response that includes not only medical needs but also psychological support for the user.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user enters detailed information about their injury or symptoms through the device. The device then activates an emotion engine to extract emotions from the entered audio or text.

[0135] Step 2:

[0136] The device uses an emotion engine to analyze the user's tone of voice, facial expressions, or input text to identify the user's emotional state. This includes detecting stress levels and anxiety levels.

[0137] Step 3:

[0138] The device sends damage information to the server along with the emotion analysis results. The communication is encrypted and secure.

[0139] Step 4:

[0140] The server inputs the received information into a generating AI model, which analyzes the damage information and generates a diagnosis. This process utilizes a pre-trained medical database.

[0141] Step 5:

[0142] The server creates a treatment plan based on the generated diagnostic results and emotional analysis. Based on the emotional data, additional suggestions for special attention or relaxation techniques may be added.

[0143] Step 6:

[0144] The server sends the completed treatment plan to the terminal. The treatment plan includes specific first-aid procedures and additional emotional support.

[0145] Step 7:

[0146] The device displays the received diagnostic results and treatment plan to the user. This includes visual guides and, if necessary, relaxation music and videos.

[0147] Step 8:

[0148] If the user feels they need further assistance, the device will offer options such as online consultation or information on nearby medical facilities. These options are easily accessible via shortcuts.

[0149] (Example 2)

[0150] 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".

[0151] Current technology lacks the means to simultaneously assess a user's physical and mental health and provide a comprehensive diagnosis and treatment plan. In particular, there is a growing need for personalized medical care that incorporates the user's emotional state.

[0152] 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.

[0153] In this invention, the server includes means for receiving information about damage from the user, means for analyzing the user's emotional state using emotion analysis means, and means for analyzing the relevant damage information using a generative AI model and generating a diagnosis result. This makes it possible to provide personalized diagnoses and treatment plans that take into account not only the user's physical health but also their emotional state.

[0154] A "user" is an entity that uses an information system to provide information about its own health and emotional state.

[0155] "Injury information" refers to detailed data about the physical injuries and symptoms experienced by the user.

[0156] "Emotional analysis methods" are technologies that analyze a user's voice or text to evaluate their emotional state, such as stress levels and anxiety.

[0157] A "generative AI model" is an algorithm that uses artificial intelligence to analyze natural language and other data to generate personalized diagnostic results and treatment plans for the user.

[0158] The "diagnosis result" is an evaluation of the user's health status based on information analyzed by a generative AI model.

[0159] A "treatment plan" is a plan that takes into account the diagnosis and the user's emotional state, and specifically outlines the medical measures and support the user should take.

[0160] "Online consultation" refers to a function that allows users to consult with medical professionals and others about health-related matters via the internet.

[0161] "Medical institution information" refers to detailed data about facilities and specialists that provide the medical services the user needs.

[0162] "Relaxation techniques" are methods and means recommended to reduce a user's stress and anxiety.

[0163] This invention is a system that provides a comprehensive diagnosis and treatment plan that takes into account the user's physical and emotional health status. The system mainly consists of terminals and a server, and it handles user information input, data analysis, diagnosis generation, and treatment plan presentation.

[0164] Users can input detailed information about their injuries and physical condition via the device. Input can be done via voice or text, and voice recognition software and text analysis tools support the processing. The device uses sentiment analysis techniques to analyze the user's input data, analyzing their emotional state, particularly their stress and anxiety levels, from their voice tone and text content.

[0165] The data obtained through emotion analysis is transmitted to a server using communication methods. The server uses a generative AI model to analyze the user's injury information and emotional state and generate a diagnosis. The generative AI model uses, for example, an AI algorithm based on natural language processing technology. Based on the diagnosis, a treatment plan tailored to the user's emotional state is generated, and relaxation techniques and additional medical support are suggested.

[0166] For example, if a user has cut their finger and is feeling anxious, they might type "I cut my finger. I'm very anxious" into their device. Emotional analysis identifies a high level of anxiety, and the server generates a treatment plan that includes "applying pressure to stop the bleeding, disinfecting the wound, and resting for 30 minutes." In addition, links to relaxation music and deep breathing exercises are provided to support the user's psychological well-being.

[0167] An example of a prompt message to the generating AI model would be: "The user has cut their finger and is feeling anxious. Generate an appropriate diagnosis and treatment plan, and suggest emotional support."

[0168] These features enable the system to provide users with personalized diagnoses and treatment plans, and to address their medical and psychological needs.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user inputs information about the injury using the device. For example, they might provide data such as "I cut my finger, I'm worried" via voice input. This input data is formatted internally within the device as pre-processed data for sentiment analysis.

[0172] Step 2:

[0173] The device uses emotion analysis techniques to analyze the user's emotional state from input voice or text data. Specifically, it analyzes voice tone and language patterns to assess stress and anxiety levels. Input is raw voice or text, and output is data that quantifies the emotional state.

[0174] Step 3:

[0175] The terminal structures the analyzed emotional state data and damage information and sends it to the server. This data is used as input for further analysis on the server side.

[0176] Step 4:

[0177] The server activates a generative AI model based on the received data and generates a prompt message. For example, the prompt message might be, "The user has cut their finger and is feeling anxious. Generate an appropriate diagnosis and treatment plan, and suggest emotional support." This prompt message is then provided as input data to the generative AI model.

[0178] Step 5:

[0179] The generating AI model analyzes symptoms and emotional state based on the prompt text and generates a diagnosis. This result includes specific medical advice such as "apply pressure to stop bleeding, disinfect, and rest for 30 minutes." The output is the diagnosis and details of the treatment plan.

[0180] Step 6:

[0181] The server sends the generated diagnostic results and treatment plan to the terminal. This includes links to necessary medical procedures and relaxation techniques. On the terminal, it is formatted in a way that is easily understandable to the user.

[0182] Step 7:

[0183] The device displays the diagnosis results and treatment plan to the user. This allows the user to obtain specific first-aid methods and recommended ways to provide emotional support.

[0184] (Application Example 2)

[0185] 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".

[0186] Currently, many information systems only provide limited diagnoses and treatment plans based on physiological information and subjective input from users. However, there is a lack of comprehensive support that takes into account the user's emotional state and stress levels. As a result, there is a challenge in that appropriate support is not provided, and the user's overall health is not adequately supported, especially when high stress and anxiety affect injury recovery.

[0187] 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.

[0188] In this invention, the server includes means for receiving information about damage from the user, means for analyzing the relevant damage information using a generative AI model and generating a diagnosis result, means for analyzing the user's emotional state and detecting stress levels and anxiety based on that information, and means for providing a treatment plan and mental support means based on the generated diagnosis result and emotional state. This enables comprehensive support that takes into account not only the user's physical health but also their emotional and mental health.

[0189] "Injury information" refers to details about physical injuries or disabilities experienced by the user, including data such as symptoms, circumstances of occurrence, and severity.

[0190] A "generative AI model" is an artificial intelligence technology that processes input data to generate solutions or recommendations for specific problems.

[0191] "User emotional state" refers to information that represents the user's psychological condition, including psychological responses such as stress levels and anxiety.

[0192] "Methods for detecting stress levels and anxiety" refer to technologies that analyze a user's emotional responses and obtain the results as quantitative data.

[0193] A "treatment plan" is a plan that outlines specific steps and methods for addressing a user's injury or health problem based on the diagnosis.

[0194] "Mental support measures" refer to content and methods provided to promote the user's psychological stability and relaxation.

[0195] In embodiments of the present invention, a system is provided that enables the analysis of injury information and the provision of a treatment plan that takes into account the user's emotional state. This system analyzes user input, supplements the information using an emotion engine, and utilizes a generative AI model.

[0196] First, the device provides an interface for the user to input information about the injury. This interface allows the user to input details about their symptoms and the circumstances under which the injury occurred. At that time, an emotion analysis module built into the device infers the emotional state from the user's voice tone and input speed, and evaluates their stress level and anxiety.

[0197] Next, the device sends the analysis results and the entered damage information to the server. The server activates a generating AI model based on the received data and begins processing the data. Tools such as Apple's Face ID and Google's Emotion API are used for emotion analysis. This generates a treatment plan tailored to the user's psychological state, along with the diagnostic results.

[0198] The generated treatment plan may include specific first-aid instructions and relaxation content to promote the user's emotional well-being. This plan is then presented to the user again via the device, and an interface providing optional access to online consultations and information on medical institutions may also be displayed.

[0199] As a concrete example, consider a situation where a user experiences stress at work. By using smart glasses to monitor stress levels, an alert is immediately sent to management if high stress is detected, and relaxation methods are also presented to the user. In this way, it is possible to support the user's psychological well-being.

[0200] An example of a prompt for a generative AI model is, "Please list relaxation methods that can be recommended for users with high levels of anxiety." This allows for the provision of specific support tailored to each user's individual situation.

[0201] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0202] Step 1:

[0203] The terminal provides the user with an interface to input injury information. The user enters details of their injury and symptoms through this interface. Once input is complete, the terminal acquires voice tone and input speed data and sends it to an emotion analysis module. The input here consists of the user's text input and voice data, and the output is an analysis result indicating the emotional state.

[0204] Step 2:

[0205] The device uses an emotion analysis module to evaluate the user's emotional state (stress level and anxiety) in real time. The emotion analysis module processes the input data and calculates a stress index. This output includes quantified stress level and anxiety data. Specifically, it performs voice tone analysis and input speed variation pattern analysis.

[0206] Step 3:

[0207] The terminal sends the emotion analysis results and the input damage information to the server. The input here is the output data from steps 1 and 2, which the server receives. This data includes the user's symptom details and emotional state. The server takes in the received data and prepares to start the generating AI model.

[0208] Step 4:

[0209] The server utilizes a generative AI model based on the received data to generate a diagnosis and treatment plan that considers both injury and emotional state. The input consists of injury information and emotional data received from the terminal. The generative AI model analyzes this data to calculate a diagnosis, appropriate first aid, and recommendations for mental support. The output includes prescriptions, rest instructions, and content suggestions for stress reduction. For example, the AI ​​model might diagnose a "cut with minor bleeding" and suggest "compression to stop the bleeding and 30 minutes of rest."

[0210] Step 5:

[0211] The server sends the generated diagnostic results and treatment plan back to the terminal and presents them to the user. The input is the data generated in step 4, and the output is the diagnostic results and treatment plan displayed on the user's screen. This allows the user to confirm necessary treatments and relaxation methods and obtain the information to decide on their next course of action.

[0212] Throughout this entire process, the prompt example from the generated AI model is, "Please list relaxation methods that can be recommended for users with high levels of anxiety," which enables comprehensive support for the user's psychological well-being.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] The system of this invention has a configuration in which a user's terminal and a server located in the cloud work in cooperation. The user inputs detailed information about injuries and symptoms via the terminal. This information is diverse, including the location, characteristics, circumstances of the injury, past medical history, and allergy information. The terminal transmits this information to the server.

[0230] The server utilizes advanced generative AI models to analyze the information it receives. Specifically, it performs diagnostic processing to identify the type and severity of damage based on the input information. The model is trained on large amounts of medical data and guidelines, giving it the ability to make rapid and accurate diagnoses. After the diagnosis is generated, the server uses it to build an effective treatment plan. The treatment plan includes first aid methods, recommended medications, and the type of medical facility to visit.

[0231] The terminal receives diagnostic results and treatment plans from the server and presents them visually to the user. This can include illustrations and video links demonstrating first aid procedures. If the user desires further assistance, a support system is available that allows them to consult with medical professionals online or access information on nearby medical facilities.

[0232] As a concrete example, consider a scenario where a user deeply cuts their finger while cooking. The user inputs details of the situation into the terminal. The server uses a generated AI model to determine how serious the wound is. For example, it might diagnose "heavy bleeding but not reaching an artery" and then create a treatment plan stating, "Wash the wound with running water and apply pressure to stop the bleeding, and go to the hospital if necessary." Along with this information, the terminal presents the user with a visual guide showing the specific method of applying pressure to stop the bleeding.

[0233] Thus, this system aims to support non-specialists in providing quick and appropriate responses even in complex medical situations.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user uses a device to enter information about their injury. This includes details such as the location and condition of the injury, the circumstances under which it occurred, past medical history, and allergy information.

[0237] Step 2:

[0238] The terminal formats the entered information appropriately and sends it to the server using a communication protocol.

[0239] Step 3:

[0240] The server passes the received information to a generating AI model, which then begins the analysis. This process identifies the type and severity of injuries from the input data and makes the best possible diagnosis.

[0241] Step 4:

[0242] The generative AI model generates diagnostic results by comparing them with medical guidelines using an accumulated database and automated learning. These results include information about the extent and cause of the damage.

[0243] Step 5:

[0244] The server creates a treatment plan based on the diagnosis. This plan includes specific first-aid procedures, recommended medications, and the type of medical facility to visit.

[0245] Step 6:

[0246] The server sends the generated diagnostic results and treatment plan to the terminal.

[0247] Step 7:

[0248] The terminal displays information received from the server to the user. Illustrations and video links illustrating first aid procedures are added to support the user in dealing with the situation more easily.

[0249] Step 8:

[0250] If the user requires further assistance, the device offers an online consultation option. The user can request access to a medical professional on the spot.

[0251] Step 9:

[0252] Furthermore, the device will provide information about nearby medical facilities based on the user's location, thereby supporting appropriate follow-up.

[0253] (Example 1)

[0254] 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."

[0255] In today's healthcare environment, providing prompt and appropriate first aid to non-medical professionals facing urgent health conditions is challenging. Furthermore, obtaining necessary medical information and specific instructions can be time-consuming, hindering the ability to respond quickly in situations requiring immediate action.

[0256] 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.

[0257] In this invention, the server includes means for acquiring symptom information from the user, means for analyzing the relevant information using a generative AI model based on large-scale data to generate a health condition diagnosis, and means for formulating an emergency treatment plan based on the generated health condition diagnosis. This enables the user to immediately obtain rapid and accurate emergency treatment information.

[0258] A "user" refers to an individual or entity that uses a system to input information about their own health status and symptoms.

[0259] "Symptom information" refers to detailed data about the user's health condition, including the location and characteristics of injuries, circumstances of occurrence, past medical history, and allergy information.

[0260] "Large-scale data" refers to a collection of information encompassing a large amount of medical-related information and guidelines that AI models use for analysis.

[0261] A "generative AI model" refers to a machine learning model trained on a vast dataset, which is a program or algorithm used to analyze symptom information obtained from users and diagnose their health status.

[0262] "Health status diagnosis" refers to information that provides an evaluation and judgment regarding the user's symptoms, based on the results of analysis by a generative AI model.

[0263] An "emergency response plan" refers to instructions or recommendations, including specific emergency response measures and treatment methods, that are formulated based on the diagnostic results of a generated AI model.

[0264] "Visual means of providing information" refers to functions that display information in the form of text, illustrations, or video links in order to make diagnostic results and first aid plans easier for users to understand.

[0265] "Illustrations and video links" are visual information used to supplement explanations related to first aid plans and health assessments, and are provided in a user-friendly and easy-to-understand format.

[0266] "Telemedicine consultation" refers to a service that connects users with medical professionals online, allowing them to receive real-time health consultations and guidance.

[0267] "Providing medical facility information" refers to a function that provides contact information and locations of appropriate medical institutions based on the user's location.

[0268] An embodiment of the present invention consists of a server operating on the cloud and a terminal operated by the user. The user inputs information about their symptoms via the terminal. This can be easily done by using a text input interface or selection-based items to provide detailed information about symptoms, medical history, and allergy information.

[0269] The terminal transmits the entered information to the server via the internet. This transmission uses a secure communication method employing the SSL / TLS protocol. A generative AI model running on the server analyzes the received symptom information. Specifically, the generative AI model is pre-trained with a large amount of medical data and diagnoses the user's health condition based on the input data.

[0270] Once the diagnosis is complete, the server automatically develops an emergency response plan based on the information obtained from the AI ​​model. This plan includes recommended treatments, relevant medication information, and specific steps to obtain further medical assistance.

[0271] The terminal displays the diagnostic results and emergency response plan received from the server on its interface to visually provide the user with the information. Illustrations and related video links can be attached to aid understanding. Based on this information, the user can take quick and accurate action.

[0272] As a concrete example, consider a scenario where a user has deeply cut their finger. An example prompt might be, "User: I cut my finger while cooking. What should I do?" Based on this information, the generating AI model diagnoses that "there is bleeding, but it has not reached an artery" and proposes a plan such as "wash with running water, apply pressure to stop the bleeding, and go to the emergency room if necessary." This information is displayed clearly to the user on the device.

[0273] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0274] Step 1:

[0275] The user inputs information regarding their health status and symptoms into the terminal. Specifically, using a dedicated input screen, details of symptoms, the location of injuries, occurrence status, past medical history, allergy information, etc. are input in text form. These inputted information are temporarily stored in the terminal's database.

[0276] Step 2:

[0277] The terminal sends the information inputted by the user to the server. At this time, since the data is securely sent using encryption technology, personal information is protected. The sent data is prepared for data analysis on the server side.

[0278] Step 3:

[0279] The server inputs the received data into the generated AI model. The AI model is pre-trained with medical data and processes the input data to diagnose the health status based on symptoms. As a result of the analysis, diagnostic information is generated to determine the level of medical response required.

[0280] Step 4:

[0281] Based on the diagnostic information obtained as the output of the generated AI model, the server formulates an emergency response plan. A plan that includes specific treatment details, necessary medications, and information on recommended medical institutions is automatically generated. This treatment plan data is temporarily held on the server side.

[0282] Step 5:

[0283] The server sends the formulated emergency response plan and diagnostic information to the terminal. The receiving terminal reads these information and performs a UI display for visually providing them to the user. Thereby, the user can understand the specific emergency response method and immediately proceed to execution.

[0284] Step 6:

[0285] The information provided by the terminal includes not only text but also illustrations and video links that assist in first aid procedures. Through these visual information, users can easily understand the instructions and perform self-care. Also, if further support is needed, online medical consultations and information on nearby medical institutions can be utilized.

[0286] (Application Example 1)

[0287] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0288] When a person engaged in security activities is injured at the scene, there is an issue that it is difficult to quickly and accurately determine the severity and perform appropriate first aid procedures. Also, if the response at the time of injury is incorrect, the symptoms may worsen, so immediate guidance to an appropriate medical institution is necessary.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following respective means.

[0290] In this invention, the server includes means for receiving information on injuries from the user, means for analyzing the corresponding injury information using a generated AI model and generating a diagnosis result, means for generating a treatment plan based on the generated diagnosis result, and means for determining the severity of an injury during security activities and providing an appropriate first aid method. Thereby, even when a security officer is injured at the scene, it becomes possible to quickly determine the severity of the injury and receive appropriate treatment and guidance to a medical institution.

[0291] A "user" is a person who inputs injury information using the system and receives a diagnosis result and a treatment plan.

[0292] "Information on injuries" is detailed information on injuries and physical abnormalities input by the user, specifically including information on the site, characteristics, occurrence situation, past medical history, and allergy information.

[0293] A "generative AI model" is an artificial intelligence-based model used to analyze input injury information and generate diagnostic results, and is trained on large-scale medical data and guidelines.

[0294] "Diagnosis results" refer to information about the type and severity of damage provided as a result of analysis by a generative AI model.

[0295] A "treatment plan" is a plan developed based on the diagnosis, including methods of first aid, recommended medications, and the type of medical institution to visit.

[0296] "First aid" refers to treatment methods that are recommended to be performed quickly and appropriately in the event of an injury, and are means of preventing the worsening of symptoms.

[0297] "Security activities" refer to on-site work by personnel to protect specific areas or assets, and include cases where those engaged in these activities are injured.

[0298] This system consists of a user-facing information processing device and a cloud server that hosts the generated AI models. The user inputs detailed data on injuries sustained during their activities via the information processing device. Specifically, they record the location and characteristics of the injuries, the circumstances of their occurrence, past medical history, and allergy information. The information processing device then transmits this data to the cloud server.

[0299] The cloud server has the capability to scrutinize and analyze received injury information using a generative AI model. The model is pre-trained with large-scale medical data and guidelines, and identifies the type and severity of injury by comparing it with existing data. After the diagnosis is generated, the system uses that information to build an effective treatment plan. This plan includes first aid methods, recommended medications, and the type of medical facility to visit if necessary.

[0300] The terminal reconstructs the diagnosis results and treatment plans received from the cloud server and visually presents them to the user. This can include detailed illustrations and video links, providing the user with immediate and effective instructions. If the user needs additional support, they can access online consultations or information on nearby medical institutions via the system.

[0301] As a specific example, consider a scenario where a security officer suddenly sprains their foot on-site. At this time, the officer quickly records the details of the symptoms on the terminal, and the cloud server uses the generated AI model to diagnose it as a "mild sprain". Subsequently, a treatment plan such as "fix the foot, apply icing, and consult an orthopedic department if the pain persists" is provided. This plan is presented as specific instructions from the terminal.

[0302] Examples of prompt texts for the generated AI model are as follows.

[0303] Based on the following details, diagnose the type and severity of the injury and propose appropriate first-aid measures.

[0304] Injury site: Ankle

[0305] Symptoms: Swelling and pain

[0306] Background: Fell during patrol

[0307] Previous injuries: None

[0308] Allergies: None

[0309] The flow of specific processing in Application Example 1 will be described using FIG. 12.

[0310] Step 1:

[0311] The user uses a device to enter detailed information about the injury. This data includes the location, characteristics, circumstances of the injury, past medical history, and allergy information. This information is collected as digital data and sent directly to a cloud server.

[0312] Step 2:

[0313] The server structures the received damage information as a prompt for the generating AI model. This prompt statement includes all the details about the input damage. Next, this prompt statement is input to the generating AI model to perform a diagnosis of the type and severity of the damage. At this stage, the input raw data is converted into an analyzable data structure.

[0314] Step 3:

[0315] The generating AI model identifies the type and severity of the injury as a diagnostic result. This diagnostic result is generated by comparing it with the medical database maintained by the model. The server receives this result, formats it into a format usable in the next step, and then saves it.

[0316] Step 4:

[0317] The server automatically generates an effective treatment plan based on the generated diagnostic results. This plan includes first aid procedures, recommended medications, and the type of appropriate medical facility. The server then cross-references each diagnostic result and performs further data calculations to select the optimal treatment method.

[0318] Step 5:

[0319] The terminal visualizes and presents diagnostic results and treatment plans received from the server to the user. The display on the terminal may include illustrations and video links to first aid procedures. Based on this information, the user can take immediate action regarding the injury. The terminal optimizes communication efficiency by selecting the most suitable display format for the user and processing the information accordingly.

[0320] 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.

[0321] In the system of the present invention, by integrating an emotion engine that recognizes the user's emotions, it is possible to provide more personalized diagnoses and treatment plans. In this system, a terminal and a server work together to receive and analyze input from the user.

[0322] The user first uses a device to input detailed information about their injury or physical condition. During this input process, an emotion engine infers the user's emotional state from their voice tone and input text, analyzing stress levels, anxiety, and other factors. The device then sends this analysis, along with the resulting information, to a server.

[0323] Based on the information received, the server begins analyzing damage information using a generative AI model. In addition to generating diagnostic results, a treatment plan is created that also takes into account the user's emotional state. Specifically, depending on the analysis results from the emotion engine, relaxation techniques are suggested if stress levels are particularly high. In this way, it becomes possible to support a more holistic view of health.

[0324] The generated diagnostic results and treatment plan are sent to the device and presented to the user. This includes specific first aid measures based on the advice and individual recommendations. If the user requires further assistance, features are also provided to offer online consultations and information on medical facilities, all presented in a user-friendly interface.

[0325] As a concrete example, consider a scenario where a user has cut their finger and is experiencing intense anxiety. The user enters details into the device. An emotion engine analyzes the user's tense voice and keystrokes during input, identifying a high level of anxiety. The server, using a generative AI model, diagnoses the situation as "there is bleeding, but it is not serious" and suggests "apply pressure to stop the bleeding, disinfect the wound, and then rest for 30 minutes." Simultaneously, links to relaxation music and breathing exercises are displayed to support the user in alleviating anxiety.

[0326] Thus, the system of the present invention is designed to provide a comprehensive response that includes not only medical needs but also psychological support for the user.

[0327] The following describes the processing flow.

[0328] Step 1:

[0329] The user enters detailed information about their injury or symptoms through the device. The device then activates an emotion engine to extract emotions from the entered audio or text.

[0330] Step 2:

[0331] The device uses an emotion engine to analyze the user's tone of voice, facial expressions, or input text to identify the user's emotional state. This includes detecting stress levels and anxiety levels.

[0332] Step 3:

[0333] The device sends damage information to the server along with the emotion analysis results. The communication is encrypted and secure.

[0334] Step 4:

[0335] The server inputs the received information into a generating AI model, which analyzes the damage information and generates a diagnosis. This process utilizes a pre-trained medical database.

[0336] Step 5:

[0337] The server creates a treatment plan based on the generated diagnostic results and emotional analysis. Based on the emotional data, additional suggestions for special attention or relaxation techniques may be added.

[0338] Step 6:

[0339] The server sends the completed treatment plan to the terminal. The treatment plan includes specific first-aid procedures and additional emotional support.

[0340] Step 7:

[0341] The device displays the received diagnostic results and treatment plan to the user. This includes visual guides and, if necessary, relaxation music and videos.

[0342] Step 8:

[0343] If the user feels they need further assistance, the device will offer options such as online consultation or information on nearby medical facilities. These options are easily accessible via shortcuts.

[0344] (Example 2)

[0345] 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".

[0346] Current technology lacks the means to simultaneously assess a user's physical and mental health and provide a comprehensive diagnosis and treatment plan. In particular, there is a growing need for personalized medical care that incorporates the user's emotional state.

[0347] 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.

[0348] In this invention, the server includes means for receiving information about damage from the user, means for analyzing the user's emotional state using emotion analysis means, and means for analyzing the relevant damage information using a generative AI model and generating a diagnosis result. This makes it possible to provide personalized diagnoses and treatment plans that take into account not only the user's physical health but also their emotional state.

[0349] A "user" is an entity that uses an information system to provide information about its own health and emotional state.

[0350] "Injury information" refers to detailed data about the physical injuries and symptoms experienced by the user.

[0351] "Emotional analysis methods" are technologies that analyze a user's voice or text to evaluate their emotional state, such as stress levels and anxiety.

[0352] A "generative AI model" is an algorithm that uses artificial intelligence to analyze natural language and other data to generate personalized diagnostic results and treatment plans for the user.

[0353] The "diagnosis result" is an evaluation of the user's health status based on information analyzed by a generative AI model.

[0354] A "treatment plan" is a plan that takes into account the diagnosis and the user's emotional state, and specifically outlines the medical measures and support the user should take.

[0355] "Online consultation" refers to a function that allows users to consult with medical professionals and others about health-related matters via the internet.

[0356] "Medical institution information" refers to detailed data about facilities and specialists that provide the medical services the user needs.

[0357] "Relaxation techniques" are methods and means recommended to reduce a user's stress and anxiety.

[0358] This invention is a system that provides a comprehensive diagnosis and treatment plan that takes into account the user's physical and emotional health status. The system mainly consists of terminals and a server, and it handles user information input, data analysis, diagnosis generation, and treatment plan presentation.

[0359] Users can input detailed information about their injuries and physical condition via the device. Input can be done via voice or text, and voice recognition software and text analysis tools support the processing. The device uses sentiment analysis techniques to analyze the user's input data, analyzing their emotional state, particularly their stress and anxiety levels, from their voice tone and text content.

[0360] The data obtained through emotion analysis is transmitted to a server using communication methods. The server uses a generative AI model to analyze the user's injury information and emotional state and generate a diagnosis. The generative AI model uses, for example, an AI algorithm based on natural language processing technology. Based on the diagnosis, a treatment plan tailored to the user's emotional state is generated, and relaxation techniques and additional medical support are suggested.

[0361] For example, if a user has cut their finger and is feeling anxious, they might type "I cut my finger. I'm very anxious" into their device. Emotional analysis identifies a high level of anxiety, and the server generates a treatment plan that includes "applying pressure to stop the bleeding, disinfecting the wound, and resting for 30 minutes." In addition, links to relaxation music and deep breathing exercises are provided to support the user's psychological well-being.

[0362] An example of a prompt message to the generating AI model would be: "The user has cut their finger and is feeling anxious. Generate an appropriate diagnosis and treatment plan, and suggest emotional support."

[0363] These features enable the system to provide users with personalized diagnoses and treatment plans, and to address their medical and psychological needs.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] The user inputs information about the injury using the device. For example, they might provide data such as "I cut my finger, I'm worried" via voice input. This input data is formatted internally within the device as pre-processed data for sentiment analysis.

[0367] Step 2:

[0368] The device uses emotion analysis techniques to analyze the user's emotional state from input voice or text data. Specifically, it analyzes voice tone and language patterns to assess stress and anxiety levels. Input is raw voice or text, and output is data that quantifies the emotional state.

[0369] Step 3:

[0370] The terminal structures the analyzed emotional state data and damage information and sends it to the server. This data is used as input for further analysis on the server side.

[0371] Step 4:

[0372] The server activates a generative AI model based on the received data and generates a prompt message. For example, the prompt message might be, "The user has cut their finger and is feeling anxious. Generate an appropriate diagnosis and treatment plan, and suggest emotional support." This prompt message is then provided as input data to the generative AI model.

[0373] Step 5:

[0374] The generating AI model analyzes symptoms and emotional state based on the prompt text and generates a diagnosis. This result includes specific medical advice such as "apply pressure to stop bleeding, disinfect, and rest for 30 minutes." The output is the diagnosis and details of the treatment plan.

[0375] Step 6:

[0376] The server sends the generated diagnostic results and treatment plan to the terminal. This includes links to necessary medical procedures and relaxation techniques. On the terminal, it is formatted in a way that is easily understandable to the user.

[0377] Step 7:

[0378] The device displays the diagnosis results and treatment plan to the user. This allows the user to obtain specific first-aid methods and recommended ways to provide emotional support.

[0379] (Application Example 2)

[0380] 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."

[0381] Currently, many information systems only provide limited diagnoses and treatment plans based on physiological information and subjective input from users. However, there is a lack of comprehensive support that takes into account the user's emotional state and stress levels. As a result, there is a challenge in that appropriate support is not provided, and the user's overall health is not adequately supported, especially when high stress and anxiety affect injury recovery.

[0382] 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.

[0383] In this invention, the server includes means for receiving information about damage from the user, means for analyzing the relevant damage information using a generative AI model and generating a diagnosis result, means for analyzing the user's emotional state and detecting stress levels and anxiety based on that information, and means for providing a treatment plan and mental support means based on the generated diagnosis result and emotional state. This enables comprehensive support that takes into account not only the user's physical health but also their emotional and mental health.

[0384] "Injury information" refers to details about physical injuries or disabilities experienced by the user, including data such as symptoms, circumstances of occurrence, and severity.

[0385] A "generative AI model" is an artificial intelligence technology that processes input data to generate solutions or recommendations for specific problems.

[0386] "User emotional state" refers to information that represents the user's psychological condition, including psychological responses such as stress levels and anxiety.

[0387] "Methods for detecting stress levels and anxiety" refer to technologies that analyze a user's emotional responses and obtain the results as quantitative data.

[0388] A "treatment plan" is a plan that outlines specific steps and methods for addressing a user's injury or health problem based on the diagnosis.

[0389] "Mental support measures" refer to content and methods provided to promote the user's psychological stability and relaxation.

[0390] In embodiments of the present invention, a system is provided that enables the analysis of injury information and the provision of a treatment plan that takes into account the user's emotional state. This system analyzes user input, supplements the information using an emotion engine, and utilizes a generative AI model.

[0391] First, the device provides an interface for the user to input information about the injury. This interface allows the user to input details about their symptoms and the circumstances under which the injury occurred. At that time, an emotion analysis module built into the device infers the emotional state from the user's voice tone and input speed, and evaluates their stress level and anxiety.

[0392] Next, the device sends the analysis results and the entered damage information to the server. The server activates a generating AI model based on the received data and begins processing the data. Tools such as Apple's Face ID and Google's Emotion API are used for emotion analysis. This generates a treatment plan tailored to the user's psychological state, along with the diagnostic results.

[0393] The generated treatment plan may include specific first-aid instructions and relaxation content to promote the user's emotional well-being. This plan is then presented to the user again via the device, and an interface providing optional access to online consultations and information on medical institutions may also be displayed.

[0394] As a concrete example, consider a situation where a user experiences stress at work. By using smart glasses to monitor stress levels, an alert is immediately sent to management if high stress is detected, and relaxation methods are also presented to the user. In this way, it is possible to support the user's psychological well-being.

[0395] An example of a prompt for a generative AI model is, "Please list relaxation methods that can be recommended for users with high levels of anxiety." This allows for the provision of specific support tailored to each user's individual situation.

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The terminal provides the user with an interface to input injury information. The user enters details of their injury and symptoms through this interface. Once input is complete, the terminal acquires voice tone and input speed data and sends it to an emotion analysis module. The input here consists of the user's text input and voice data, and the output is an analysis result indicating the emotional state.

[0399] Step 2:

[0400] The device uses an emotion analysis module to evaluate the user's emotional state (stress level and anxiety) in real time. The emotion analysis module processes the input data and calculates a stress index. This output includes quantified stress level and anxiety data. Specifically, it performs voice tone analysis and input speed variation pattern analysis.

[0401] Step 3:

[0402] The terminal sends the emotion analysis results and the input damage information to the server. The input here is the output data from steps 1 and 2, which the server receives. This data includes the user's symptom details and emotional state. The server takes in the received data and prepares to start the generating AI model.

[0403] Step 4:

[0404] The server utilizes a generative AI model based on the received data to generate a diagnosis and treatment plan that considers both injury and emotional state. The input consists of injury information and emotional data received from the terminal. The generative AI model analyzes this data to calculate a diagnosis, appropriate first aid, and recommendations for mental support. The output includes prescriptions, rest instructions, and content suggestions for stress reduction. For example, the AI ​​model might diagnose a "cut with minor bleeding" and suggest "compression to stop the bleeding and 30 minutes of rest."

[0405] Step 5:

[0406] The server sends the generated diagnostic results and treatment plan back to the terminal and presents them to the user. The input is the data generated in step 4, and the output is the diagnostic results and treatment plan displayed on the user's screen. This allows the user to confirm necessary treatments and relaxation methods and obtain the information to decide on their next course of action.

[0407] Throughout this entire process, the prompt example from the generated AI model is, "Please list relaxation methods that can be recommended for users with high levels of anxiety," which enables comprehensive support for the user's psychological well-being.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] [Third Embodiment]

[0412] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0413] 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.

[0414] 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).

[0415] 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.

[0416] 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.

[0417] 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).

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] 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".

[0424] The system of this invention has a configuration in which a user's terminal and a server located in the cloud work in cooperation. The user inputs detailed information about injuries and symptoms via the terminal. This information is diverse, including the location, characteristics, circumstances of the injury, past medical history, and allergy information. The terminal transmits this information to the server.

[0425] The server utilizes advanced generative AI models to analyze the information it receives. Specifically, it performs diagnostic processing to identify the type and severity of damage based on the input information. The model is trained on large amounts of medical data and guidelines, giving it the ability to make rapid and accurate diagnoses. After the diagnosis is generated, the server uses it to build an effective treatment plan. The treatment plan includes first aid methods, recommended medications, and the type of medical facility to visit.

[0426] The terminal receives diagnostic results and treatment plans from the server and presents them visually to the user. This can include illustrations and video links demonstrating first aid procedures. If the user desires further assistance, a support system is available that allows them to consult with medical professionals online or access information on nearby medical facilities.

[0427] As a concrete example, consider a scenario where a user deeply cuts their finger while cooking. The user inputs details of the situation into the terminal. The server uses a generated AI model to determine how serious the wound is. For example, it might diagnose "heavy bleeding but not reaching an artery" and then create a treatment plan stating, "Wash the wound with running water and apply pressure to stop the bleeding, and go to the hospital if necessary." Along with this information, the terminal presents the user with a visual guide showing the specific method of applying pressure to stop the bleeding.

[0428] Thus, this system aims to support non-specialists in providing quick and appropriate responses even in complex medical situations.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] The user uses a device to enter information about their injury. This includes details such as the location and condition of the injury, the circumstances under which it occurred, past medical history, and allergy information.

[0432] Step 2:

[0433] The terminal formats the entered information appropriately and sends it to the server using a communication protocol.

[0434] Step 3:

[0435] The server passes the received information to a generating AI model, which then begins the analysis. This process identifies the type and severity of injuries from the input data and makes the best possible diagnosis.

[0436] Step 4:

[0437] The generative AI model generates diagnostic results by comparing them with medical guidelines using an accumulated database and automated learning. These results include information about the extent and cause of the damage.

[0438] Step 5:

[0439] The server creates a treatment plan based on the diagnosis. This plan includes specific first-aid procedures, recommended medications, and the type of medical facility to visit.

[0440] Step 6:

[0441] The server sends the generated diagnostic results and treatment plan to the terminal.

[0442] Step 7:

[0443] The terminal displays information received from the server to the user. Illustrations and video links illustrating first aid procedures are added to support the user in dealing with the situation more easily.

[0444] Step 8:

[0445] If the user requires further assistance, the device offers an online consultation option. The user can request access to a medical professional on the spot.

[0446] Step 9:

[0447] Furthermore, the device will provide information about nearby medical facilities based on the user's location, thereby supporting appropriate follow-up.

[0448] (Example 1)

[0449] 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."

[0450] In today's healthcare environment, providing prompt and appropriate first aid to non-medical professionals facing urgent health conditions is challenging. Furthermore, obtaining necessary medical information and specific instructions can be time-consuming, hindering the ability to respond quickly in situations requiring immediate action.

[0451] 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.

[0452] In this invention, the server includes means for acquiring symptom information from the user, means for analyzing the relevant information using a generative AI model based on large-scale data to generate a health condition diagnosis, and means for formulating an emergency treatment plan based on the generated health condition diagnosis. This enables the user to immediately obtain rapid and accurate emergency treatment information.

[0453] A "user" refers to an individual or entity that uses a system to input information about their own health status and symptoms.

[0454] "Symptom information" refers to detailed data about the user's health condition, including the location and characteristics of injuries, circumstances of occurrence, past medical history, and allergy information.

[0455] "Large-scale data" refers to a collection of information encompassing a large amount of medical-related information and guidelines that AI models use for analysis.

[0456] A "generative AI model" refers to a machine learning model trained on a vast dataset, which is a program or algorithm used to analyze symptom information obtained from users and diagnose their health status.

[0457] "Health status diagnosis" refers to information that provides an evaluation and judgment regarding the user's symptoms, based on the results of analysis by a generative AI model.

[0458] An "emergency response plan" refers to instructions or recommendations, including specific emergency response measures and treatment methods, that are formulated based on the diagnostic results of a generated AI model.

[0459] "Visual means of providing information" refers to functions that display information in the form of text, illustrations, or video links in order to make diagnostic results and first aid plans easier for users to understand.

[0460] "Illustrations and video links" are visual information used to supplement explanations related to first aid plans and health assessments, and are provided in a user-friendly and easy-to-understand format.

[0461] "Telemedicine consultation" refers to a service that connects users with medical professionals online, allowing them to receive real-time health consultations and guidance.

[0462] "Providing medical facility information" refers to a function that provides contact information and locations of appropriate medical institutions based on the user's location.

[0463] An embodiment of the present invention consists of a server operating on the cloud and a terminal operated by the user. The user inputs information about their symptoms via the terminal. This can be easily done by using a text input interface or selection-based items to provide detailed information about symptoms, medical history, and allergy information.

[0464] The terminal transmits the entered information to the server via the internet. This transmission uses a secure communication method employing the SSL / TLS protocol. A generative AI model running on the server analyzes the received symptom information. Specifically, the generative AI model is pre-trained with a large amount of medical data and diagnoses the user's health condition based on the input data.

[0465] Once the diagnosis is complete, the server automatically develops an emergency response plan based on the information obtained from the AI ​​model. This plan includes recommended treatments, relevant medication information, and specific steps to obtain further medical assistance.

[0466] The terminal displays the diagnostic results and emergency response plan received from the server on its interface to visually provide the user with the information. Illustrations and related video links can be attached to aid understanding. Based on this information, the user can take quick and accurate action.

[0467] As a concrete example, consider a scenario where a user has deeply cut their finger. An example prompt might be, "User: I cut my finger while cooking. What should I do?" Based on this information, the generating AI model diagnoses that "there is bleeding, but it has not reached an artery" and proposes a plan such as "wash with running water, apply pressure to stop the bleeding, and go to the emergency room if necessary." This information is displayed clearly to the user on the device.

[0468] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0469] Step 1:

[0470] Users input information about their health status and symptoms into the device. Specifically, they use a dedicated input screen to enter details of their symptoms, the location of the injury, the circumstances of its occurrence, their past medical history, allergy information, etc., in text format. This entered information is temporarily stored in the device's database.

[0471] Step 2:

[0472] The terminal sends the information entered by the user to the server. During this process, the data is transmitted securely using encryption technology, thus protecting personal information. The server then prepares the transmitted data for analysis.

[0473] Step 3:

[0474] The server inputs the received data into a generating AI model. The AI ​​model is pre-trained with medical data and processes the input data to diagnose the patient's health condition based on their symptoms. Based on the analysis, diagnostic information is generated, and the level of medical intervention required is determined.

[0475] Step 4:

[0476] Based on the diagnostic information obtained as output from the generated AI model, the server formulates an emergency treatment plan. It automatically generates a plan that includes specific treatment details, necessary medications, and information on recommended medical facilities. This treatment plan data is temporarily stored on the server.

[0477] Step 5:

[0478] The server transmits the formulated first-aid plan and diagnostic information to the terminal. The receiving terminal reads this information and displays it visually to the user using a user interface (UI). This allows the user to understand specific first-aid procedures and immediately put them into action.

[0479] Step 6:

[0480] The information provided by the device includes not only text but also illustrations and video links to assist with first aid. Through this visual information, users can easily understand instructions and provide care themselves. Furthermore, if additional support is needed, they can access online medical consultations and information on nearby medical facilities.

[0481] (Application Example 1)

[0482] 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."

[0483] There is a challenge in quickly and accurately assessing the severity of injuries sustained by security personnel in the field and providing appropriate first aid. Furthermore, mishandling of injuries can worsen the condition, making immediate guidance to the appropriate medical facility essential.

[0484] 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.

[0485] In this invention, the server includes means for receiving information about damage from a user, means for analyzing the relevant damage information using a generative AI model and generating a diagnosis result, means for generating a treatment plan based on the generated diagnosis result, and means for determining the severity of an injury sustained during security activities and providing appropriate first aid. This makes it possible to quickly determine the severity of an injury and receive appropriate treatment and guidance to a medical institution even if a security officer is injured on-site.

[0486] A "user" is someone who uses the system to input injury information and receives diagnostic results and treatment plans.

[0487] "Injury information" refers to detailed information about injuries or physical abnormalities entered by the user, specifically including the affected area, characteristics, circumstances of occurrence, past medical history, and allergy information.

[0488] A "generative AI model" is an artificial intelligence-based model used to analyze input injury information and generate diagnostic results, and is trained on large-scale medical data and guidelines.

[0489] "Diagnosis results" refer to information about the type and severity of damage provided as a result of analysis by a generative AI model.

[0490] A "treatment plan" is a plan developed based on the diagnosis, including methods of first aid, recommended medications, and the type of medical institution to visit.

[0491] "First aid" refers to treatment methods that are recommended to be performed quickly and appropriately in the event of an injury, and are means of preventing the worsening of symptoms.

[0492] "Security activities" refer to on-site work by personnel to protect specific areas or assets, and include cases where those engaged in these activities are injured.

[0493] This system consists of a user-facing information processing device and a cloud server that hosts the generated AI models. The user inputs detailed data on injuries sustained during their activities via the information processing device. Specifically, they record the location and characteristics of the injuries, the circumstances of their occurrence, past medical history, and allergy information. The information processing device then transmits this data to the cloud server.

[0494] The cloud server has the capability to scrutinize and analyze received injury information using a generative AI model. The model is pre-trained with large-scale medical data and guidelines, and identifies the type and severity of injury by comparing it with existing data. After the diagnosis is generated, the system uses that information to build an effective treatment plan. This plan includes first aid methods, recommended medications, and the type of medical facility to visit if necessary.

[0495] The terminal reconstructs the diagnostic results and treatment plan received from the cloud server and presents them visually to the user. This can include detailed illustrations and video links, providing the user with immediately useful instructions. If the user requires additional support, the system allows them to access online consultations or information on nearby medical facilities.

[0496] As a concrete example, let's consider a scenario where a security officer suddenly sprains their ankle while on-site. The officer quickly records the details of their symptoms on a device, and a cloud server uses a generated AI model to diagnose it as a "minor sprain." Next, a treatment plan is provided, such as "immobilize the ankle, apply ice, and see an orthopedic specialist if the pain persists." This plan is presented as specific instructions from the device.

[0497] Examples of prompt statements for a generative AI model are as follows:

[0498] Based on the details below, diagnose the type and severity of the injury and suggest appropriate first aid measures.

[0499] Injured area: Ankle

[0500] Symptoms: Swelling and pain

[0501] Background: Fell while on patrol

[0502] Past injuries: None

[0503] Allergies: None

[0504] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0505] Step 1:

[0506] The user uses a device to enter detailed information about the injury. This data includes the location, characteristics, circumstances of the injury, past medical history, and allergy information. This information is collected as digital data and sent directly to a cloud server.

[0507] Step 2:

[0508] The server structures the received damage information as a prompt for the generating AI model. This prompt statement includes all the details about the input damage. Next, this prompt statement is input to the generating AI model to perform a diagnosis of the type and severity of the damage. At this stage, the input raw data is converted into an analyzable data structure.

[0509] Step 3:

[0510] The generating AI model identifies the type and severity of the injury as a diagnostic result. This diagnostic result is generated by comparing it with the medical database maintained by the model. The server receives this result, formats it into a format usable in the next step, and then saves it.

[0511] Step 4:

[0512] The server automatically generates an effective treatment plan based on the generated diagnostic results. This plan includes first aid procedures, recommended medications, and the type of appropriate medical facility. The server then cross-references each diagnostic result and performs further data calculations to select the optimal treatment method.

[0513] Step 5:

[0514] The terminal visualizes and presents diagnostic results and treatment plans received from the server to the user. The display on the terminal may include illustrations and video links to first aid procedures. Based on this information, the user can take immediate action regarding the injury. The terminal optimizes communication efficiency by selecting the most suitable display format for the user and processing the information accordingly.

[0515] 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.

[0516] In the system of the present invention, by integrating an emotion engine that recognizes the user's emotions, it is possible to provide more personalized diagnoses and treatment plans. In this system, a terminal and a server work together to receive and analyze input from the user.

[0517] The user first uses a device to input detailed information about their injury or physical condition. During this input process, an emotion engine infers the user's emotional state from their voice tone and input text, analyzing stress levels, anxiety, and other factors. The device then sends this analysis, along with the resulting information, to a server.

[0518] Based on the information received, the server begins analyzing damage information using a generative AI model. In addition to generating diagnostic results, a treatment plan is created that also takes into account the user's emotional state. Specifically, depending on the analysis results from the emotion engine, relaxation techniques are suggested if stress levels are particularly high. In this way, it becomes possible to support a more holistic view of health.

[0519] The generated diagnostic results and treatment plan are sent to the device and presented to the user. This includes specific first aid measures based on the advice and individual recommendations. If the user requires further assistance, features are also provided to offer online consultations and information on medical facilities, all presented in a user-friendly interface.

[0520] As a concrete example, consider a scenario where a user has cut their finger and is experiencing intense anxiety. The user enters details into the device. An emotion engine analyzes the user's tense voice and keystrokes during input, identifying a high level of anxiety. The server, using a generative AI model, diagnoses the situation as "there is bleeding, but it is not serious" and suggests "apply pressure to stop the bleeding, disinfect the wound, and then rest for 30 minutes." Simultaneously, links to relaxation music and breathing exercises are displayed to support the user in alleviating anxiety.

[0521] Thus, the system of the present invention is designed to provide a comprehensive response that includes not only medical needs but also psychological support for the user.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] The user enters detailed information about their injury or symptoms through the device. The device then activates an emotion engine to extract emotions from the entered audio or text.

[0525] Step 2:

[0526] The device uses an emotion engine to analyze the user's tone of voice, facial expressions, or input text to identify the user's emotional state. This includes detecting stress levels and anxiety levels.

[0527] Step 3:

[0528] The device sends damage information to the server along with the emotion analysis results. The communication is encrypted and secure.

[0529] Step 4:

[0530] The server inputs the received information into a generating AI model, which analyzes the damage information and generates a diagnosis. This process utilizes a pre-trained medical database.

[0531] Step 5:

[0532] The server creates a treatment plan based on the generated diagnostic results and emotional analysis. Based on the emotional data, additional suggestions for special attention or relaxation techniques may be added.

[0533] Step 6:

[0534] The server sends the completed treatment plan to the terminal. The treatment plan includes specific first-aid procedures and additional emotional support.

[0535] Step 7:

[0536] The device displays the received diagnostic results and treatment plan to the user. This includes visual guides and, if necessary, relaxation music and videos.

[0537] Step 8:

[0538] If the user feels they need further assistance, the device will offer options such as online consultation or information on nearby medical facilities. These options are easily accessible via shortcuts.

[0539] (Example 2)

[0540] 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."

[0541] Current technology lacks the means to simultaneously assess a user's physical and mental health and provide a comprehensive diagnosis and treatment plan. In particular, there is a growing need for personalized medical care that incorporates the user's emotional state.

[0542] 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.

[0543] In this invention, the server includes means for receiving information about damage from the user, means for analyzing the user's emotional state using emotion analysis means, and means for analyzing the relevant damage information using a generative AI model and generating a diagnosis result. This makes it possible to provide personalized diagnoses and treatment plans that take into account not only the user's physical health but also their emotional state.

[0544] A "user" is an entity that uses an information system to provide information about its own health and emotional state.

[0545] "Injury information" refers to detailed data about the physical injuries and symptoms experienced by the user.

[0546] "Emotional analysis methods" are technologies that analyze a user's voice or text to evaluate their emotional state, such as stress levels and anxiety.

[0547] A "generative AI model" is an algorithm that uses artificial intelligence to analyze natural language and other data to generate personalized diagnostic results and treatment plans for the user.

[0548] The "diagnosis result" is an evaluation of the user's health status based on information analyzed by a generative AI model.

[0549] A "treatment plan" is a plan that takes into account the diagnosis and the user's emotional state, and specifically outlines the medical measures and support the user should take.

[0550] "Online consultation" refers to a function that allows users to consult with medical professionals and others about health-related matters via the internet.

[0551] "Medical institution information" refers to detailed data about facilities and specialists that provide the medical services the user needs.

[0552] "Relaxation techniques" are methods and means recommended to reduce a user's stress and anxiety.

[0553] This invention is a system that provides a comprehensive diagnosis and treatment plan that takes into account the user's physical and emotional health status. The system mainly consists of terminals and a server, and it handles user information input, data analysis, diagnosis generation, and treatment plan presentation.

[0554] Users can input detailed information about their injuries and physical condition via the device. Input can be done via voice or text, and voice recognition software and text analysis tools support the processing. The device uses sentiment analysis techniques to analyze the user's input data, analyzing their emotional state, particularly their stress and anxiety levels, from their voice tone and text content.

[0555] The data obtained through emotion analysis is transmitted to a server using communication methods. The server uses a generative AI model to analyze the user's injury information and emotional state and generate a diagnosis. The generative AI model uses, for example, an AI algorithm based on natural language processing technology. Based on the diagnosis, a treatment plan tailored to the user's emotional state is generated, and relaxation techniques and additional medical support are suggested.

[0556] For example, if a user has cut their finger and is feeling anxious, they might type "I cut my finger. I'm very anxious" into their device. Emotional analysis identifies a high level of anxiety, and the server generates a treatment plan that includes "applying pressure to stop the bleeding, disinfecting the wound, and resting for 30 minutes." In addition, links to relaxation music and deep breathing exercises are provided to support the user's psychological well-being.

[0557] An example of a prompt message to the generating AI model would be: "The user has cut their finger and is feeling anxious. Generate an appropriate diagnosis and treatment plan, and suggest emotional support."

[0558] These features enable the system to provide users with personalized diagnoses and treatment plans, and to address their medical and psychological needs.

[0559] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0560] Step 1:

[0561] The user inputs information about the injury using the device. For example, they might provide data such as "I cut my finger, I'm worried" via voice input. This input data is formatted internally within the device as pre-processed data for sentiment analysis.

[0562] Step 2:

[0563] The device uses emotion analysis techniques to analyze the user's emotional state from input voice or text data. Specifically, it analyzes voice tone and language patterns to assess stress and anxiety levels. Input is raw voice or text, and output is data that quantifies the emotional state.

[0564] Step 3:

[0565] The terminal structures the analyzed emotional state data and damage information and sends it to the server. This data is used as input for further analysis on the server side.

[0566] Step 4:

[0567] The server activates a generative AI model based on the received data and generates a prompt message. For example, the prompt message might be, "The user has cut their finger and is feeling anxious. Generate an appropriate diagnosis and treatment plan, and suggest emotional support." This prompt message is then provided as input data to the generative AI model.

[0568] Step 5:

[0569] The generating AI model analyzes symptoms and emotional state based on the prompt text and generates a diagnosis. This result includes specific medical advice such as "apply pressure to stop bleeding, disinfect, and rest for 30 minutes." The output is the diagnosis and details of the treatment plan.

[0570] Step 6:

[0571] The server sends the generated diagnostic results and treatment plan to the terminal. This includes links to necessary medical procedures and relaxation techniques. On the terminal, it is formatted in a way that is easily understandable to the user.

[0572] Step 7:

[0573] The device displays the diagnosis results and treatment plan to the user. This allows the user to obtain specific first-aid methods and recommended ways to provide emotional support.

[0574] (Application Example 2)

[0575] 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."

[0576] Currently, many information systems only provide limited diagnoses and treatment plans based on physiological information and subjective input from users. However, there is a lack of comprehensive support that takes into account the user's emotional state and stress levels. As a result, there is a challenge in that appropriate support is not provided, and the user's overall health is not adequately supported, especially when high stress and anxiety affect injury recovery.

[0577] 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.

[0578] In this invention, the server includes means for receiving information about damage from the user, means for analyzing the relevant damage information using a generative AI model and generating a diagnosis result, means for analyzing the user's emotional state and detecting stress levels and anxiety based on that information, and means for providing a treatment plan and mental support means based on the generated diagnosis result and emotional state. This enables comprehensive support that takes into account not only the user's physical health but also their emotional and mental health.

[0579] "Injury information" refers to details about physical injuries or disabilities experienced by the user, including data such as symptoms, circumstances of occurrence, and severity.

[0580] A "generative AI model" is an artificial intelligence technology that processes input data to generate solutions or recommendations for specific problems.

[0581] "User emotional state" refers to information that represents the user's psychological condition, including psychological responses such as stress levels and anxiety.

[0582] "Methods for detecting stress levels and anxiety" refer to technologies that analyze a user's emotional responses and obtain the results as quantitative data.

[0583] A "treatment plan" is a plan that outlines specific steps and methods for addressing a user's injury or health problem based on the diagnosis.

[0584] "Mental support measures" refer to content and methods provided to promote the user's psychological stability and relaxation.

[0585] In embodiments of the present invention, a system is provided that enables the analysis of injury information and the provision of a treatment plan that takes into account the user's emotional state. This system analyzes user input, supplements the information using an emotion engine, and utilizes a generative AI model.

[0586] First, the device provides an interface for the user to input information about the injury. This interface allows the user to input details about their symptoms and the circumstances under which the injury occurred. At that time, an emotion analysis module built into the device infers the emotional state from the user's voice tone and input speed, and evaluates their stress level and anxiety.

[0587] Next, the device sends the analysis results and the entered damage information to the server. The server activates a generating AI model based on the received data and begins processing the data. Tools such as Apple's Face ID and Google's Emotion API are used for emotion analysis. This generates a treatment plan tailored to the user's psychological state, along with the diagnostic results.

[0588] The generated treatment plan may include specific first-aid instructions and relaxation content to promote the user's emotional well-being. This plan is then presented to the user again via the device, and an interface providing optional access to online consultations and information on medical institutions may also be displayed.

[0589] As a concrete example, consider a situation where a user experiences stress at work. By using smart glasses to monitor stress levels, an alert is immediately sent to management if high stress is detected, and relaxation methods are also presented to the user. In this way, it is possible to support the user's psychological well-being.

[0590] An example of a prompt for a generative AI model is, "Please list relaxation methods that can be recommended for users with high levels of anxiety." This allows for the provision of specific support tailored to each user's individual situation.

[0591] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0592] Step 1:

[0593] The terminal provides the user with an interface to input injury information. The user enters details of their injury and symptoms through this interface. Once input is complete, the terminal acquires voice tone and input speed data and sends it to an emotion analysis module. The input here consists of the user's text input and voice data, and the output is an analysis result indicating the emotional state.

[0594] Step 2:

[0595] The device uses an emotion analysis module to evaluate the user's emotional state (stress level and anxiety) in real time. The emotion analysis module processes the input data and calculates a stress index. This output includes quantified stress level and anxiety data. Specifically, it performs voice tone analysis and input speed variation pattern analysis.

[0596] Step 3:

[0597] The terminal sends the emotion analysis results and the input damage information to the server. The input here is the output data from steps 1 and 2, which the server receives. This data includes the user's symptom details and emotional state. The server takes in the received data and prepares to start the generating AI model.

[0598] Step 4:

[0599] The server utilizes a generative AI model based on the received data to generate a diagnosis and treatment plan that considers both injury and emotional state. The input consists of injury information and emotional data received from the terminal. The generative AI model analyzes this data to calculate a diagnosis, appropriate first aid, and recommendations for mental support. The output includes prescriptions, rest instructions, and content suggestions for stress reduction. For example, the AI ​​model might diagnose a "cut with minor bleeding" and suggest "compression to stop the bleeding and 30 minutes of rest."

[0600] Step 5:

[0601] The server sends the generated diagnostic results and treatment plan back to the terminal and presents them to the user. The input is the data generated in step 4, and the output is the diagnostic results and treatment plan displayed on the user's screen. This allows the user to confirm necessary treatments and relaxation methods and obtain the information to decide on their next course of action.

[0602] Throughout this entire process, the prompt example from the generated AI model is, "Please list relaxation methods that can be recommended for users with high levels of anxiety," which enables comprehensive support for the user's psychological well-being.

[0603] 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.

[0604] 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.

[0605] 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.

[0606] [Fourth Embodiment]

[0607] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0608] 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.

[0609] 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).

[0610] 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.

[0611] 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.

[0612] 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).

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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".

[0620] The system of this invention has a configuration in which a user's terminal and a server located in the cloud work in cooperation. The user inputs detailed information about injuries and symptoms via the terminal. This information is diverse, including the location, characteristics, circumstances of the injury, past medical history, and allergy information. The terminal transmits this information to the server.

[0621] The server utilizes advanced generative AI models to analyze the information it receives. Specifically, it performs diagnostic processing to identify the type and severity of damage based on the input information. The model is trained on large amounts of medical data and guidelines, giving it the ability to make rapid and accurate diagnoses. After the diagnosis is generated, the server uses it to build an effective treatment plan. The treatment plan includes first aid methods, recommended medications, and the type of medical facility to visit.

[0622] The terminal receives diagnostic results and treatment plans from the server and presents them visually to the user. This can include illustrations and video links demonstrating first aid procedures. If the user desires further assistance, a support system is available that allows them to consult with medical professionals online or access information on nearby medical facilities.

[0623] As a concrete example, consider a scenario where a user deeply cuts their finger while cooking. The user inputs details of the situation into the terminal. The server uses a generated AI model to determine how serious the wound is. For example, it might diagnose "heavy bleeding but not reaching an artery" and then create a treatment plan stating, "Wash the wound with running water and apply pressure to stop the bleeding, and go to the hospital if necessary." Along with this information, the terminal presents the user with a visual guide showing the specific method of applying pressure to stop the bleeding.

[0624] Thus, this system aims to support non-specialists in providing quick and appropriate responses even in complex medical situations.

[0625] The following describes the processing flow.

[0626] Step 1:

[0627] The user uses a device to enter information about their injury. This includes details such as the location and condition of the injury, the circumstances under which it occurred, past medical history, and allergy information.

[0628] Step 2:

[0629] The terminal formats the entered information appropriately and sends it to the server using a communication protocol.

[0630] Step 3:

[0631] The server passes the received information to a generating AI model, which then begins the analysis. This process identifies the type and severity of injuries from the input data and makes the best possible diagnosis.

[0632] Step 4:

[0633] The generative AI model generates diagnostic results by comparing them with medical guidelines using an accumulated database and automated learning. These results include information about the extent and cause of the damage.

[0634] Step 5:

[0635] The server creates a treatment plan based on the diagnosis. This plan includes specific first-aid procedures, recommended medications, and the type of medical facility to visit.

[0636] Step 6:

[0637] The server sends the generated diagnostic results and treatment plan to the terminal.

[0638] Step 7:

[0639] The terminal displays information received from the server to the user. Illustrations and video links illustrating first aid procedures are added to support the user in dealing with the situation more easily.

[0640] Step 8:

[0641] If the user requires further assistance, the device offers an online consultation option. The user can request access to a medical professional on the spot.

[0642] Step 9:

[0643] Furthermore, the device will provide information about nearby medical facilities based on the user's location, thereby supporting appropriate follow-up.

[0644] (Example 1)

[0645] 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".

[0646] In today's healthcare environment, providing prompt and appropriate first aid to non-medical professionals facing urgent health conditions is challenging. Furthermore, obtaining necessary medical information and specific instructions can be time-consuming, hindering the ability to respond quickly in situations requiring immediate action.

[0647] 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.

[0648] In this invention, the server includes means for acquiring symptom information from the user, means for analyzing the relevant information using a generative AI model based on large-scale data to generate a health condition diagnosis, and means for formulating an emergency treatment plan based on the generated health condition diagnosis. This enables the user to immediately obtain rapid and accurate emergency treatment information.

[0649] A "user" refers to an individual or entity that uses a system to input information about their own health status and symptoms.

[0650] "Symptom information" refers to detailed data about the user's health condition, including the location and characteristics of injuries, circumstances of occurrence, past medical history, and allergy information.

[0651] "Large-scale data" refers to a collection of information encompassing a large amount of medical-related information and guidelines that AI models use for analysis.

[0652] A "generative AI model" refers to a machine learning model trained on a vast dataset, which is a program or algorithm used to analyze symptom information obtained from users and diagnose their health status.

[0653] "Health status diagnosis" refers to information that provides an evaluation and judgment regarding the user's symptoms, based on the results of analysis by a generative AI model.

[0654] An "emergency response plan" refers to instructions or recommendations, including specific emergency response measures and treatment methods, that are formulated based on the diagnostic results of a generated AI model.

[0655] "Visual means of providing information" refers to functions that display information in the form of text, illustrations, or video links in order to make diagnostic results and first aid plans easier for users to understand.

[0656] "Illustrations and video links" are visual information used to supplement explanations related to first aid plans and health assessments, and are provided in a user-friendly and easy-to-understand format.

[0657] "Telemedicine consultation" refers to a service that connects users with medical professionals online, allowing them to receive real-time health consultations and guidance.

[0658] "Providing medical facility information" refers to a function that provides contact information and locations of appropriate medical institutions based on the user's location.

[0659] An embodiment of the present invention consists of a server operating on the cloud and a terminal operated by the user. The user inputs information about their symptoms via the terminal. This can be easily done by using a text input interface or selection-based items to provide detailed information about symptoms, medical history, and allergy information.

[0660] The terminal transmits the entered information to the server via the internet. This transmission uses a secure communication method employing the SSL / TLS protocol. A generative AI model running on the server analyzes the received symptom information. Specifically, the generative AI model is pre-trained with a large amount of medical data and diagnoses the user's health condition based on the input data.

[0661] Once the diagnosis is complete, the server automatically develops an emergency response plan based on the information obtained from the AI ​​model. This plan includes recommended treatments, relevant medication information, and specific steps to obtain further medical assistance.

[0662] The terminal displays the diagnostic results and emergency response plan received from the server on its interface to visually provide the user with the information. Illustrations and related video links can be attached to aid understanding. Based on this information, the user can take quick and accurate action.

[0663] As a concrete example, consider a scenario where a user has deeply cut their finger. An example prompt might be, "User: I cut my finger while cooking. What should I do?" Based on this information, the generating AI model diagnoses that "there is bleeding, but it has not reached an artery" and proposes a plan such as "wash with running water, apply pressure to stop the bleeding, and go to the emergency room if necessary." This information is displayed clearly to the user on the device.

[0664] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0665] Step 1:

[0666] Users input information about their health status and symptoms into the device. Specifically, they use a dedicated input screen to enter details of their symptoms, the location of the injury, the circumstances of its occurrence, their past medical history, allergy information, etc., in text format. This entered information is temporarily stored in the device's database.

[0667] Step 2:

[0668] The terminal sends the information entered by the user to the server. During this process, the data is transmitted securely using encryption technology, thus protecting personal information. The server then prepares the transmitted data for analysis.

[0669] Step 3:

[0670] The server inputs the received data into a generating AI model. The AI ​​model is pre-trained with medical data and processes the input data to diagnose the patient's health condition based on their symptoms. Based on the analysis, diagnostic information is generated, and the level of medical intervention required is determined.

[0671] Step 4:

[0672] Based on the diagnostic information obtained as output from the generated AI model, the server formulates an emergency treatment plan. It automatically generates a plan that includes specific treatment details, necessary medications, and information on recommended medical facilities. This treatment plan data is temporarily stored on the server.

[0673] Step 5:

[0674] The server transmits the formulated first-aid plan and diagnostic information to the terminal. The receiving terminal reads this information and displays it visually to the user using a user interface (UI). This allows the user to understand specific first-aid procedures and immediately put them into action.

[0675] Step 6:

[0676] The information provided by the device includes not only text but also illustrations and video links to assist with first aid. Through this visual information, users can easily understand instructions and provide care themselves. Furthermore, if additional support is needed, they can access online medical consultations and information on nearby medical facilities.

[0677] (Application Example 1)

[0678] 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".

[0679] There is a challenge in quickly and accurately assessing the severity of injuries sustained by security personnel in the field and providing appropriate first aid. Furthermore, mishandling of injuries can worsen the condition, making immediate guidance to the appropriate medical facility essential.

[0680] 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.

[0681] In this invention, the server includes means for receiving information about damage from a user, means for analyzing the relevant damage information using a generative AI model and generating a diagnosis result, means for generating a treatment plan based on the generated diagnosis result, and means for determining the severity of an injury sustained during security activities and providing appropriate first aid. This makes it possible to quickly determine the severity of an injury and receive appropriate treatment and guidance to a medical institution even if a security officer is injured on-site.

[0682] A "user" is someone who uses the system to input injury information and receives diagnostic results and treatment plans.

[0683] "Injury information" refers to detailed information about injuries or physical abnormalities entered by the user, specifically including the affected area, characteristics, circumstances of occurrence, past medical history, and allergy information.

[0684] A "generative AI model" is an artificial intelligence-based model used to analyze input injury information and generate diagnostic results, and is trained on large-scale medical data and guidelines.

[0685] "Diagnosis results" refer to information about the type and severity of damage provided as a result of analysis by a generative AI model.

[0686] A "treatment plan" is a plan developed based on the diagnosis, including methods of first aid, recommended medications, and the type of medical institution to visit.

[0687] "First aid" refers to treatment methods that are recommended to be performed quickly and appropriately in the event of an injury, and are means of preventing the worsening of symptoms.

[0688] "Security activities" refer to on-site work by personnel to protect specific areas or assets, and include cases where those engaged in these activities are injured.

[0689] This system consists of a user-facing information processing device and a cloud server that hosts the generated AI models. The user inputs detailed data on injuries sustained during their activities via the information processing device. Specifically, they record the location and characteristics of the injuries, the circumstances of their occurrence, past medical history, and allergy information. The information processing device then transmits this data to the cloud server.

[0690] The cloud server has the capability to scrutinize and analyze received injury information using a generative AI model. The model is pre-trained with large-scale medical data and guidelines, and identifies the type and severity of injury by comparing it with existing data. After the diagnosis is generated, the system uses that information to build an effective treatment plan. This plan includes first aid methods, recommended medications, and the type of medical facility to visit if necessary.

[0691] The terminal reconstructs the diagnostic results and treatment plan received from the cloud server and presents them visually to the user. This can include detailed illustrations and video links, providing the user with immediately useful instructions. If the user requires additional support, the system allows them to access online consultations or information on nearby medical facilities.

[0692] As a concrete example, let's consider a scenario where a security officer suddenly sprains their ankle while on-site. The officer quickly records the details of their symptoms on a device, and a cloud server uses a generated AI model to diagnose it as a "minor sprain." Next, a treatment plan is provided, such as "immobilize the ankle, apply ice, and see an orthopedic specialist if the pain persists." This plan is presented as specific instructions from the device.

[0693] Examples of prompt statements for a generative AI model are as follows:

[0694] Based on the details below, diagnose the type and severity of the injury and suggest appropriate first aid measures.

[0695] Injured area: Ankle

[0696] Symptoms: Swelling and pain

[0697] Background: Fell while on patrol

[0698] Past injuries: None

[0699] Allergies: None

[0700] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0701] Step 1:

[0702] The user uses a device to enter detailed information about the injury. This data includes the location, characteristics, circumstances of the injury, past medical history, and allergy information. This information is collected as digital data and sent directly to a cloud server.

[0703] Step 2:

[0704] The server structures the received damage information as a prompt for the generating AI model. This prompt statement includes all the details about the input damage. Next, this prompt statement is input to the generating AI model to perform a diagnosis of the type and severity of the damage. At this stage, the input raw data is converted into an analyzable data structure.

[0705] Step 3:

[0706] The generating AI model identifies the type and severity of the injury as a diagnostic result. This diagnostic result is generated by comparing it with the medical database maintained by the model. The server receives this result, formats it into a format usable in the next step, and then saves it.

[0707] Step 4:

[0708] The server automatically generates an effective treatment plan based on the generated diagnostic results. This plan includes first aid procedures, recommended medications, and the type of appropriate medical facility. The server then cross-references each diagnostic result and performs further data calculations to select the optimal treatment method.

[0709] Step 5:

[0710] The terminal visualizes and presents diagnostic results and treatment plans received from the server to the user. The display on the terminal may include illustrations and video links to first aid procedures. Based on this information, the user can take immediate action regarding the injury. The terminal optimizes communication efficiency by selecting the most suitable display format for the user and processing the information accordingly.

[0711] 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.

[0712] In the system of the present invention, by integrating an emotion engine that recognizes the user's emotions, it is possible to provide more personalized diagnoses and treatment plans. In this system, a terminal and a server work together to receive and analyze input from the user.

[0713] The user first uses a device to input detailed information about their injury or physical condition. During this input process, an emotion engine infers the user's emotional state from their voice tone and input text, analyzing stress levels, anxiety, and other factors. The device then sends this analysis, along with the resulting information, to a server.

[0714] Based on the information received, the server begins analyzing damage information using a generative AI model. In addition to generating diagnostic results, a treatment plan is created that also takes into account the user's emotional state. Specifically, depending on the analysis results from the emotion engine, relaxation techniques are suggested if stress levels are particularly high. In this way, it becomes possible to support a more holistic view of health.

[0715] The generated diagnostic results and treatment plan are sent to the device and presented to the user. This includes specific first aid measures based on the advice and individual recommendations. If the user requires further assistance, features are also provided to offer online consultations and information on medical facilities, all presented in a user-friendly interface.

[0716] As a concrete example, consider a scenario where a user has cut their finger and is experiencing intense anxiety. The user enters details into the device. An emotion engine analyzes the user's tense voice and keystrokes during input, identifying a high level of anxiety. The server, using a generative AI model, diagnoses the situation as "there is bleeding, but it is not serious" and suggests "apply pressure to stop the bleeding, disinfect the wound, and then rest for 30 minutes." Simultaneously, links to relaxation music and breathing exercises are displayed to support the user in alleviating anxiety.

[0717] Thus, the system of the present invention is designed to provide a comprehensive response that includes not only medical needs but also psychological support for the user.

[0718] The following describes the processing flow.

[0719] Step 1:

[0720] The user enters detailed information about their injury or symptoms through the device. The device then activates an emotion engine to extract emotions from the entered audio or text.

[0721] Step 2:

[0722] The device uses an emotion engine to analyze the user's tone of voice, facial expressions, or input text to identify the user's emotional state. This includes detecting stress levels and anxiety levels.

[0723] Step 3:

[0724] The device sends damage information to the server along with the emotion analysis results. The communication is encrypted and secure.

[0725] Step 4:

[0726] The server inputs the received information into a generating AI model, which analyzes the damage information and generates a diagnosis. This process utilizes a pre-trained medical database.

[0727] Step 5:

[0728] The server creates a treatment plan based on the generated diagnostic results and emotional analysis. Based on the emotional data, additional suggestions for special attention or relaxation techniques may be added.

[0729] Step 6:

[0730] The server sends the completed treatment plan to the terminal. The treatment plan includes specific first-aid procedures and additional emotional support.

[0731] Step 7:

[0732] The device displays the received diagnostic results and treatment plan to the user. This includes visual guides and, if necessary, relaxation music and videos.

[0733] Step 8:

[0734] If the user feels they need further assistance, the device will offer options such as online consultation or information on nearby medical facilities. These options are easily accessible via shortcuts.

[0735] (Example 2)

[0736] 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".

[0737] Current technology lacks the means to simultaneously assess a user's physical and mental health and provide a comprehensive diagnosis and treatment plan. In particular, there is a growing need for personalized medical care that incorporates the user's emotional state.

[0738] 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.

[0739] In this invention, the server includes means for receiving information about damage from the user, means for analyzing the user's emotional state using emotion analysis means, and means for analyzing the relevant damage information using a generative AI model and generating a diagnosis result. This makes it possible to provide personalized diagnoses and treatment plans that take into account not only the user's physical health but also their emotional state.

[0740] A "user" is an entity that uses an information system to provide information about its own health and emotional state.

[0741] "Injury information" refers to detailed data about the physical injuries and symptoms experienced by the user.

[0742] "Emotional analysis methods" are technologies that analyze a user's voice or text to evaluate their emotional state, such as stress levels and anxiety.

[0743] A "generative AI model" is an algorithm that uses artificial intelligence to analyze natural language and other data to generate personalized diagnostic results and treatment plans for the user.

[0744] The "diagnosis result" is an evaluation of the user's health status based on information analyzed by a generative AI model.

[0745] A "treatment plan" is a plan that takes into account the diagnosis and the user's emotional state, and specifically outlines the medical measures and support the user should take.

[0746] "Online consultation" refers to a function that allows users to consult with medical professionals and others about health-related matters via the internet.

[0747] "Medical institution information" refers to detailed data about facilities and specialists that provide the medical services the user needs.

[0748] "Relaxation techniques" are methods and means recommended to reduce a user's stress and anxiety.

[0749] This invention is a system that provides a comprehensive diagnosis and treatment plan that takes into account the user's physical and emotional health status. The system mainly consists of terminals and a server, and it handles user information input, data analysis, diagnosis generation, and treatment plan presentation.

[0750] Users can input detailed information about their injuries and physical condition via the device. Input can be done via voice or text, and voice recognition software and text analysis tools support the processing. The device uses sentiment analysis techniques to analyze the user's input data, analyzing their emotional state, particularly their stress and anxiety levels, from their voice tone and text content.

[0751] The data obtained through emotion analysis is transmitted to a server using communication methods. The server uses a generative AI model to analyze the user's injury information and emotional state and generate a diagnosis. The generative AI model uses, for example, an AI algorithm based on natural language processing technology. Based on the diagnosis, a treatment plan tailored to the user's emotional state is generated, and relaxation techniques and additional medical support are suggested.

[0752] For example, if a user has cut their finger and is feeling anxious, they might type "I cut my finger. I'm very anxious" into their device. Emotional analysis identifies a high level of anxiety, and the server generates a treatment plan that includes "applying pressure to stop the bleeding, disinfecting the wound, and resting for 30 minutes." In addition, links to relaxation music and deep breathing exercises are provided to support the user's psychological well-being.

[0753] An example of a prompt message to the generating AI model would be: "The user has cut their finger and is feeling anxious. Generate an appropriate diagnosis and treatment plan, and suggest emotional support."

[0754] These features enable the system to provide users with personalized diagnoses and treatment plans, and to address their medical and psychological needs.

[0755] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0756] Step 1:

[0757] The user inputs information about the injury using the device. For example, they might provide data such as "I cut my finger, I'm worried" via voice input. This input data is formatted internally within the device as pre-processed data for sentiment analysis.

[0758] Step 2:

[0759] The device uses emotion analysis techniques to analyze the user's emotional state from input voice or text data. Specifically, it analyzes voice tone and language patterns to assess stress and anxiety levels. Input is raw voice or text, and output is data that quantifies the emotional state.

[0760] Step 3:

[0761] The terminal structures the analyzed emotional state data and damage information and sends it to the server. This data is used as input for further analysis on the server side.

[0762] Step 4:

[0763] The server activates a generative AI model based on the received data and generates a prompt message. For example, the prompt message might be, "The user has cut their finger and is feeling anxious. Generate an appropriate diagnosis and treatment plan, and suggest emotional support." This prompt message is then provided as input data to the generative AI model.

[0764] Step 5:

[0765] The generating AI model analyzes symptoms and emotional state based on the prompt text and generates a diagnosis. This result includes specific medical advice such as "apply pressure to stop bleeding, disinfect, and rest for 30 minutes." The output is the diagnosis and details of the treatment plan.

[0766] Step 6:

[0767] The server sends the generated diagnostic results and treatment plan to the terminal. This includes links to necessary medical procedures and relaxation techniques. On the terminal, it is formatted in a way that is easily understandable to the user.

[0768] Step 7:

[0769] The device displays the diagnosis results and treatment plan to the user. This allows the user to obtain specific first-aid methods and recommended ways to provide emotional support.

[0770] (Application Example 2)

[0771] 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".

[0772] Currently, many information systems only provide limited diagnoses and treatment plans based on physiological information and subjective input from users. However, there is a lack of comprehensive support that takes into account the user's emotional state and stress levels. As a result, there is a challenge in that appropriate support is not provided, and the user's overall health is not adequately supported, especially when high stress and anxiety affect injury recovery.

[0773] 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.

[0774] In this invention, the server includes means for receiving information about damage from the user, means for analyzing the relevant damage information using a generative AI model and generating a diagnosis result, means for analyzing the user's emotional state and detecting stress levels and anxiety based on that information, and means for providing a treatment plan and mental support means based on the generated diagnosis result and emotional state. This enables comprehensive support that takes into account not only the user's physical health but also their emotional and mental health.

[0775] "Injury information" refers to details about physical injuries or disabilities experienced by the user, including data such as symptoms, circumstances of occurrence, and severity.

[0776] A "generative AI model" is an artificial intelligence technology that processes input data to generate solutions or recommendations for specific problems.

[0777] "User emotional state" refers to information that represents the user's psychological condition, including psychological responses such as stress levels and anxiety.

[0778] "Methods for detecting stress levels and anxiety" refer to technologies that analyze a user's emotional responses and obtain the results as quantitative data.

[0779] A "treatment plan" is a plan that outlines specific steps and methods for addressing a user's injury or health problem based on the diagnosis.

[0780] "Mental support measures" refer to content and methods provided to promote the user's psychological stability and relaxation.

[0781] In embodiments of the present invention, a system is provided that enables the analysis of injury information and the provision of a treatment plan that takes into account the user's emotional state. This system analyzes user input, supplements the information using an emotion engine, and utilizes a generative AI model.

[0782] First, the device provides an interface for the user to input information about the injury. This interface allows the user to input details about their symptoms and the circumstances under which the injury occurred. At that time, an emotion analysis module built into the device infers the emotional state from the user's voice tone and input speed, and evaluates their stress level and anxiety.

[0783] Next, the device sends the analysis results and the entered damage information to the server. The server activates a generating AI model based on the received data and begins processing the data. Tools such as Apple's Face ID and Google's Emotion API are used for emotion analysis. This generates a treatment plan tailored to the user's psychological state, along with the diagnostic results.

[0784] The generated treatment plan may include specific first-aid instructions and relaxation content to promote the user's emotional well-being. This plan is then presented to the user again via the device, and an interface providing optional access to online consultations and information on medical institutions may also be displayed.

[0785] As a concrete example, consider a situation where a user experiences stress at work. By using smart glasses to monitor stress levels, an alert is immediately sent to management if high stress is detected, and relaxation methods are also presented to the user. In this way, it is possible to support the user's psychological well-being.

[0786] An example of a prompt for a generative AI model is, "Please list relaxation methods that can be recommended for users with high levels of anxiety." This allows for the provision of specific support tailored to each user's individual situation.

[0787] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0788] Step 1:

[0789] The terminal provides the user with an interface to input injury information. The user enters details of their injury and symptoms through this interface. Once input is complete, the terminal acquires voice tone and input speed data and sends it to an emotion analysis module. The input here consists of the user's text input and voice data, and the output is an analysis result indicating the emotional state.

[0790] Step 2:

[0791] The device uses an emotion analysis module to evaluate the user's emotional state (stress level and anxiety) in real time. The emotion analysis module processes the input data and calculates a stress index. This output includes quantified stress level and anxiety data. Specifically, it performs voice tone analysis and input speed variation pattern analysis.

[0792] Step 3:

[0793] The terminal sends the emotion analysis results and the input damage information to the server. The input here is the output data from steps 1 and 2, which the server receives. This data includes the user's symptom details and emotional state. The server takes in the received data and prepares to start the generating AI model.

[0794] Step 4:

[0795] The server utilizes a generative AI model based on the received data to generate a diagnosis and treatment plan that considers both injury and emotional state. The input consists of injury information and emotional data received from the terminal. The generative AI model analyzes this data to calculate a diagnosis, appropriate first aid, and recommendations for mental support. The output includes prescriptions, rest instructions, and content suggestions for stress reduction. For example, the AI ​​model might diagnose a "cut with minor bleeding" and suggest "compression to stop the bleeding and 30 minutes of rest."

[0796] Step 5:

[0797] The server sends the generated diagnostic results and treatment plan back to the terminal and presents them to the user. The input is the data generated in step 4, and the output is the diagnostic results and treatment plan displayed on the user's screen. This allows the user to confirm necessary treatments and relaxation methods and obtain the information to decide on their next course of action.

[0798] Throughout this entire process, the prompt example from the generated AI model is, "Please list relaxation methods that can be recommended for users with high levels of anxiety," which enables comprehensive support for the user's psychological well-being.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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.

[0807] 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."

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] The following is further disclosed regarding the embodiments described above.

[0821] (Claim 1)

[0822] A means of receiving information about damage from the user,

[0823] A means for analyzing the relevant damage information using a generative AI model and generating a diagnostic result,

[0824] A means for generating a treatment plan based on the generated diagnostic results,

[0825] A means for presenting the aforementioned treatment plan to the user,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the damage information received from the user includes past medical history and allergy information.

[0829] (Claim 3)

[0830] The system according to claim 1, wherein the generated treatment plan includes online consultation or provision of medical institution information.

[0831] "Example 1"

[0832] (Claim 1)

[0833] A means of obtaining symptom information from the user,

[0834] A means of analyzing relevant information using a generative AI model based on large-scale data and generating a health status diagnosis,

[0835] A means of formulating an emergency response plan based on the generated health diagnosis,

[0836] A means for visually providing the user with the aforementioned emergency response plan,

[0837] A method that includes illustrations and video links in a visualized emergency response plan,

[0838] A system that includes this.

[0839] (Claim 2)

[0840] The system according to claim 1, wherein the symptom information received from the user includes history information and allergy information.

[0841] (Claim 3)

[0842] The system according to claim 1, wherein the formulated emergency treatment plan includes remote medical consultation or presentation of medical facility information.

[0843] "Application Example 1"

[0844] (Claim 1)

[0845] A means of receiving information about damage from the user,

[0846] A means for analyzing the relevant damage information using a generative AI model and generating a diagnostic result,

[0847] A means for generating a treatment plan based on the generated diagnostic results,

[0848] A means for presenting the aforementioned treatment plan to the user,

[0849] A means to assess the severity of injuries sustained during security operations and to provide appropriate first aid,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, wherein the damage information received from the user includes past medical history and allergy information.

[0853] (Claim 3)

[0854] The system according to claim 1, wherein the generated treatment plan includes online consultation or provision of medical institution information.

[0855] "Example 2 of combining an emotion engine"

[0856] (Claim 1)

[0857] A means of receiving information about damage from the user,

[0858] A means for analyzing a user's emotional state using emotion analysis tools,

[0859] A means for analyzing the relevant damage information using a generative AI model and generating a diagnostic result,

[0860] A means for generating a treatment plan based on the generated diagnostic results and emotional state,

[0861] A means for presenting the aforementioned treatment plan to the user,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, wherein the damage information received from the user includes past medical history, allergy information, and emotional state information.

[0865] (Claim 3)

[0866] The system according to claim 1, wherein the generated treatment plan includes an online consultation or provision of medical institution information, and further includes a suggestion of relaxation techniques.

[0867] "Application example 2 when combining with an emotional engine"

[0868] (Claim 1)

[0869] A means of receiving information about damage from the user,

[0870] A means for analyzing the relevant damage information using a generative AI model and generating a diagnostic result,

[0871] A method for analyzing the user's emotional state and detecting stress levels and anxiety based on that information,

[0872] A means of providing a treatment plan and mental support measures based on the generated diagnostic results and emotional state,

[0873] A means for presenting the aforementioned treatment plan and mental support means to the user,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, wherein the damage information received from the user includes past medical history and allergy information.

[0877] (Claim 3)

[0878] The system according to claim 1, wherein the generated treatment plan and mental support means include online consultation or provision of medical institution information. [Explanation of Symbols]

[0879] 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 receiving information about damage from the user, A means for analyzing the relevant damage information using a generative AI model and generating a diagnostic result, A means for generating a treatment plan based on the generated diagnostic results, A means for presenting the aforementioned treatment plan to the user, A system that includes this.

2. The system according to claim 1, wherein the damage information received from the user includes past medical history and allergy information.

3. The system according to claim 1, wherein the generated treatment plan includes online consultation or provision of medical institution information.

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