system

Abstract

We provide the system. [Solution] A means of obtaining biological state information entered by the user, A means of converting acquired biological state information into structured data, A means for transmitting structured data over a communication network, A means for receiving and analyzing transmitted structured data, A means of predicting biological abnormalities based on analyzed data, Means for suggesting treatments corresponding to predicted biological abnormalities, 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, 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 modern medical settings, when users receive an initial diagnosis, the time spent visiting the hospital and waiting for examination places a significant burden on them. Additionally, there are challenges in providing prompt and accurate information for appropriate medical treatment. In such a situation, users are anxious about their health status, and furthermore, the efficient utilization of medical resources is hindered. Therefore, there is a need to quickly evaluate the biological state of users and present appropriate action guidelines.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a system that acquires biological state information input by a user, converts it into structured data, and transmits it via a communication network. The transmitted data is received and analyzed on a server, and biological abnormalities are predicted by a generative model. As a result, specific actions corresponding to the predicted abnormalities are presented, enabling the user to obtain quick and appropriate guidance. Therefore, it provides necessary initial information before visiting a medical institution, aiming to improve the efficiency of medical resources and enhance user confidence.

[0006] "Biological status information" refers to information that indicates symptoms and conditions related to the user's health and physical condition.

[0007] "Structured data" refers to data organized according to a specific format or style, making it suitable for analysis and communication.

[0008] A "communication network" is a network system used to transmit or receive data, and includes the Internet and other digital communication methods.

[0009] "Analysis" refers to the process of examining information based on data and deriving logical conclusions.

[0010] A "generative model" is a machine learning model that learns patterns based on vast amounts of historical data and uses that learning to make predictions and suggestions for new data.

[0011] A "biological abnormality" refers to a condition that deviates from a normal state of health, and may include the possibility of illness or poor physical condition.

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

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

[0016] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[0018] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), 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 predicts potential biological abnormalities based on biological state information entered by the user and provides appropriate treatment. This system is implemented through the user's terminal, server, and communication network.

[0035] First, the user uses an input form displayed on the device screen to enter information about their biological condition, such as specific symptoms like "headache," "fever," or "fatigue." The device then organizes the input data as structured data and sends it to the server via the communication network.

[0036] The server internally prepares the received structured data for analysis and uses a generative model to predict biological abnormalities. This generative model is trained on historical medical literature and the latest medical data to provide the best possible diagnosis for the entered symptoms.

[0037] Once the analysis and prediction are complete, the server determines specific actions based on the predicted biological abnormalities. These actions may include recommendations for consultation with a medical professional and suggestions for improvements in daily life. For example, it may suggest advice such as, "You may have the flu, so see a doctor immediately," or "Drink plenty of fluids and get plenty of rest."

[0038] Finally, the server sends response data, including the decided course of action, back to the user's terminal. The terminal displays this response data in its user interface, allowing the user to consciously assess their own health status and decide on their next course of action.

[0039] This configuration allows users to receive a rapid assessment of their health status and take appropriate initial action before visiting a medical institution. This invention contributes to the efficient use of medical resources and enhances user confidence.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user opens a symptom input form on the device screen. Here, they enter detailed information about their symptoms, such as "headache," "fever," and "fatigue." The device receives the entered information and prepares for the next step.

[0043] Step 2:

[0044] The terminal converts the user's entered biological state information into structured data such as JSON format. This conversion prepares the data for transmission over the communication network.

[0045] Step 3:

[0046] The device sends structured data to the server as an HTTP POST request, specifying the appropriate endpoint. After sending, it waits for a response from the server.

[0047] Step 4:

[0048] The server receives structured data sent from the terminal and verifies the data's content and format. Once it confirms there are no problems, it prepares the data to be passed to the analysis engine.

[0049] Step 5:

[0050] The server uses generative models to analyze biological state information. Based on the analysis results, it predicts possible biological abnormalities and provides a diagnosis for the entered symptoms. The latest medical information is utilized in this process.

[0051] Step 6:

[0052] The server determines the appropriate course of action based on the predicted biological abnormalities. This may include recommending consultation with a medical professional, lifestyle changes, or emergency medical intervention.

[0053] Step 7:

[0054] The server compiles the determined action information into response data for the user and sends it to the terminal. This transmission is usually done using JSON format data.

[0055] Step 8:

[0056] The terminal analyzes the response data received from the server and displays it on the user interface. Based on this information, the user can decide on further actions and take the next steps to improve their health management.

[0057] (Example 1)

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

[0059] Rapid and accurate detection of health abnormalities based on biological status and provision of appropriate initial responses are essential to reducing the burden on medical institutions and enabling individual users to manage their health properly. However, conventional methods have made it difficult to appropriately evaluate a user's biological status or effectively propose treatment. Therefore, the present invention aims to provide a system that efficiently analyzes biological status information provided by users and provides meaningful feedback.

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

[0061] In this invention, the server includes means for receiving biological state information input by a user, means for preparing the received information for analysis, and means for predicting biological anomalies using a generative AI model. This enables rapid identification of anomalies based on the state information provided by the user and prompt proposal of initial response measures.

[0062] A "user" refers to an individual who uses the system by inputting biological status information.

[0063] "Biological status information" refers to data and symptoms that users provide regarding their own health status.

[0064] "Data" refers to information that has been structured to make it possible to process a user's biological state within the system.

[0065] "Communication environment" refers to the network infrastructure used to send and receive data between a user's terminal and a server.

[0066] A "server" refers to a computing device that analyzes received data and provides predictions and responses to biological abnormalities.

[0067] "Means of preparing for analysis" refers to the processes and methods used to format received data into a format that can be applied to anomaly prediction.

[0068] A "generative AI model" refers to a learning algorithm used to predict anomalies based on biological state information.

[0069] "Means for predicting biological abnormalities" refers to functions and methods that use generative AI models to identify health-related abnormalities from input information.

[0070] "Response" refers to specific action plans or measures proposed based on predicted biological abnormalities.

[0071] The system of this invention predicts biological abnormalities and provides a rapid response based on the user's biological state information. Specifically, the user first inputs health information using the terminal interface. This input information includes specific symptoms such as "headache," "fever," and "fatigue."

[0072] The terminal organizes the input information as structured data. This data is transmitted to the server via the communication environment. The server in this system is a high-performance computing device that provides an environment for analyzing the received data.

[0073] The server uses a generative AI model to analyze data and predict biological abnormalities. This AI model is trained on a large amount of historical medical information and the latest medical data, and can derive diagnoses that correspond to the user's symptoms with high accuracy. An example of a prompt for the generative AI model would be: "The user has reported fever and cough. Please indicate the most likely diagnosis and recommended actions."

[0074] Once the prediction is complete, the server determines a course of action based on the biological anomaly. This action may include specific advice such as, "You may have the flu; we recommend you seek medical attention immediately."

[0075] Ultimately, the server sends response data, including the decided course of action, back to the terminal. The terminal displays this data on its user interface, allowing the user to gain a deeper understanding of their health status and obtain guidance for taking appropriate action. Such a system enables users to quickly assess their health status and take appropriate initial action.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user enters specific biological condition information, such as headache or fever, through an input form displayed on the device screen. This input information is stored on the device as text data related to individual symptoms. This text data is then used to convert it into a format that can be parsed later.

[0079] Step 2:

[0080] The terminal converts the biological status information obtained from the user into structured data. This is converted into a JSON format that can be stored in a database. In this conversion process, individual symptoms are organized as data fields, and structured data is generated that is sent to the server via the communication environment.

[0081] Step 3:

[0082] The server receives structured data from terminals via the communication environment. The received data is first processed to prepare it for analysis and organized into a dataset that forms the basis for anomaly prediction. This preparation step involves checking the integrity of the data and filtering out unnecessary data.

[0083] Step 4:

[0084] The server predicts biological abnormalities using a generative AI model. The input is prepared structured data, and the AI ​​model applies a learning algorithm based on past data to output the optimal diagnosis. In this process, specific prompt sentences are given to the AI ​​model, and inference is performed to assess the likelihood of an abnormality.

[0085] Step 5:

[0086] The server determines specific actions based on the diagnostic results output by the AI ​​model. The AI ​​model's predictions are used to formulate actions such as recommendations for medical consultations or points to be aware of in daily life. The server compiles these actions as response data.

[0087] Step 6:

[0088] The server sends response data, including the decided action, to the terminal. The data is encrypted during transmission to ensure communication security. This data is then used for display on the user interface.

[0089] Step 7:

[0090] The terminal decodes the response data received from the server and displays it on the user interface. This data allows the user to gain a deeper understanding of their own health status and take necessary actions quickly. This enables the user to obtain information to take specific health-related actions.

[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] In modern society, it is crucial to quickly understand an individual's health status and provide appropriate initial treatment. However, it is difficult for people to immediately visit a medical institution for minor health issues that occur in daily life, and this can lead to excessive consumption of medical resources. Therefore, there is a need for means to effectively monitor individual biological states and promptly encourage appropriate responses when abnormalities occur.

[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 collecting information about the biological state from users who input data, means for converting the collected information into structured data, and means for transmitting the converted structured data via a communication network. This makes it possible to quickly assess the health status of individuals and issue early warnings when abnormalities are predicted.

[0096] "Users who input data" refers to individuals who provide information about their biological state.

[0097] "Information regarding biological status" refers to data related to health and physical condition, specifically information about symptoms such as headaches, fever, and fatigue.

[0098] "Structured data" refers to digital data that is organized according to a specific format or structure.

[0099] A "network for communication" refers to the infrastructure that enables the sending and receiving of information, and includes the Internet network and mobile networks.

[0100] "Analysis" is the process of examining data and finding meaning based on it.

[0101] "Predicting biological abnormalities" refers to predicting health problems based on collected data.

[0102] A "device that proposes countermeasures" is a device that provides specific countermeasures and advice based on predicted anomalies.

[0103] "Early warning" means issuing a warning quickly based on prediction results and prompting appropriate action.

[0104] The system of this invention begins with the user inputting their biological state information via a smartphone or smart glasses. The terminal structures the input information and transmits it to a server via a communication network. The server receives the structured data and performs analysis using a generative AI model. This generative AI model is trained on past medical literature and the latest medical data and has the ability to quickly predict the user's biological abnormalities.

[0105] If the server predicts a biological abnormality, it will formulate specific measures based on that information. These may include recommendations to consult with a health management professional or suggestions for improving daily life. For example, it might advise, "You are at risk of influenza, so we recommend getting enough rest and staying hydrated." Finally, the server sends this information to the terminal and notifies the user via smartphone or smart glasses. This allows the user to recognize health risks early and take appropriate action.

[0106] For example, if a user enters "headache" and "fatigue," the server will determine these to be potential symptoms of influenza and send a notification recommending immediate rest and consultation with a specialist. An example of a prompt message would be, "User's symptom input: fatigue. Predict possible biological abnormalities." This instruction would be sent to the generating AI model in this form. In this way, the present invention functions as a system that contributes to improving the safety and health of individual users and enables appropriate and rapid responses.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The user enters biological status information using the input interface of a smartphone or smart glasses. This information includes specific symptoms (e.g., "headache," "fatigue," etc.). The entered data is structured within the device.

[0110] Step 2:

[0111] The device sends structured data to the server via a communication network. During transmission, the data is properly formatted and encrypted to ensure secure transmission.

[0112] Step 3:

[0113] The server prepares to analyze the structured data received via the communication network. The data is then fed into a generative AI model as input data to predict biological abnormalities.

[0114] Step 4:

[0115] The generative AI model uses received symptom information to predict biological abnormalities. This model is trained on historical medical literature and the latest medical data, and predicts and outputs risk levels and likely diseases from the input data.

[0116] Step 5:

[0117] The server develops appropriate measures based on the biological abnormalities predicted by the generating AI model. This includes recommendations for further medical actions and suggestions for improvements in daily life. For example, if there is a risk of influenza, recommendations for seeing a doctor and advice on rest will be developed.

[0118] Step 6:

[0119] The server sends response data, including the formulated measures, to the terminal. The response data is configured to be presented to the user through the user interface.

[0120] Step 7:

[0121] The terminal displays the received response data to the user via a user interface. Based on the information presented, the user can decide on necessary health management actions.

[0122] 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.

[0123] This invention is a system that simultaneously acquires biological state information and emotions from a user and provides appropriate diagnosis and treatment. The system consists of a user terminal, a server, a communication network, and an emotion engine.

[0124] First, the user uses their device to input not only physical symptoms but also the emotions they are feeling. For example, the user is expected to input information such as "stomach ache" or "feeling stressed." The device then organizes this information as structured data and prepares to send it to the server via the communication network.

[0125] The server analyzes the received biological state and emotional information. It uses generative models to make biological predictions, and an emotion engine recognizes the user's emotions, utilizing the results in its analysis. The emotion engine recognizes the user's emotional state based on the analysis and tone of the words entered by the user.

[0126] The server analyzes emotions and biological states to predict biological abnormalities and determine specific actions to take. These actions may include prompt consultation with a medical professional and stress management in daily life. For example, if a user is experiencing stress, the server may suggest breathing exercises or relaxation techniques to help manage stress.

[0127] Finally, the server sends the determined information to the terminal. The terminal displays the received diagnostic results and treatment information on its user interface, making it easy for the user to understand. This allows the user to quickly obtain information about their condition and decide on their next course of action while also considering their mental health.

[0128] In this form, the present invention provides comprehensive medical support that takes into account not only the user's physical health but also their mental health, thereby enhancing the user's sense of security and comfort.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user launches the application on their device and inputs information about their biological state and emotions. During this process, the user enters descriptions such as "stomach ache" as a symptom and "feeling stressed" as an emotion into the form. The device then collects the entered data.

[0132] Step 2:

[0133] The device converts the collected biological state and emotional information into structured data in JSON format. The converted data is then prepared for efficient transmission over the communication network.

[0134] Step 3:

[0135] The device sends structured data to the server in the form of an HTTP POST request. Specify the appropriate endpoint to ensure the data reaches the server reliably. After transmission is complete, wait for a response from the server.

[0136] Step 4:

[0137] The server receives structured data sent from the terminal. It checks the content, syntax, and format of the received data for any problems and prepares it for the analysis process.

[0138] Step 5:

[0139] The server uses a generative model to analyze biological state information. Furthermore, it uses an emotion engine to analyze emotional information. The emotion engine detects "stress" from the user's description and performs analysis that takes this into account.

[0140] Step 6:

[0141] The server predicts the user's biological abnormalities based on the analysis results. At the same time, it also makes decisions on treatment that take emotional information into account. For example, in the case of stomach pain and stress, it recommends "seeing a doctor" along with "relaxation techniques for stress management."

[0142] Step 7:

[0143] The server compiles the final results into a JSON response and sends it to the terminal. This data includes the diagnostic results and recommended actions.

[0144] Step 8:

[0145] The terminal analyzes the received response data and displays it on the user interface. Users can immediately understand the analysis results and recommended actions, and use them to help plan their own health management.

[0146] (Example 2)

[0147] 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".

[0148] Traditionally, health management based on a user's biological state and emotions has generally relied on diagnoses that only consider physical symptoms. There has been a need for technology that simultaneously assesses emotional health and proposes appropriate treatment. A comprehensive approach is necessary because emotional changes can significantly impact physical health. However, systems that accurately analyze emotional information and combine it with actual health status for diagnosis are insufficient, and technology to address this problem is eagerly awaited.

[0149] 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.

[0150] In this invention, the server includes means for acquiring biological and emotional state information input by the user, means for predicting biological abnormalities and emotional states based on the analyzed data, and means for suggesting medical treatment and emotional support based on the predicted results. This makes it possible to take measures that consider not only the user's physical health but also their emotional health.

[0151] A "user" refers to an individual who provides information about their biological and emotional states to the system.

[0152] "Biological status information" refers to objective data regarding the user's physical health status.

[0153] "Emotional state information" refers to data that indicates the user's subjective emotional state.

[0154] "Structured data" refers to data that has been converted into a format suitable for subsequent analysis.

[0155] "Communication medium" refers to the technical elements used to send and receive data, and includes networks.

[0156] A "generative model" refers to a machine learning algorithm used for data analysis and prediction.

[0157] A "sentiment analysis engine" refers to software used to identify and analyze a user's emotional state.

[0158] "Medical treatment" refers to the treatments and countermeasures applied based on the user's physical health condition.

[0159] "Emotional support" refers to advice or methods for improving or maintaining a user's emotional state.

[0160] This invention is a system that analyzes a user's biological and emotional state information to provide appropriate medical treatment and emotional support. The system mainly consists of a terminal, a server, a communication medium, and an emotion analysis engine.

[0161] Users can input biometric and emotional status information at any time using their own devices. The device converts this information into structured data, securely encrypts it, and transmits it to the server via a communication medium. This allows users to easily record and send health information to the server, even from home.

[0162] The server uses specialized software to analyze the received structured data. It leverages generative AI models to perform data analysis that predicts future changes in biological states. This analysis involves referencing historical medical databases and conducting detailed pattern analysis. Meanwhile, the sentiment analysis engine processes emotional information entered by the user, identifying the user's emotional state based on text and tone.

[0163] For example, if a user inputs "I have a headache and I'm stressed," the server receives this information and uses a generative AI model to predict the cause of the headache, while simultaneously evaluating the level of stress using an emotion analysis engine. Based on the analysis results, the server can suggest consulting a medical professional and, at the same time, provide the user with relaxation techniques to alleviate stress.

[0164] A possible prompt message might be, "I currently have a stomach ache and am feeling stressed. What advice do you have for me?" This system aims to provide comprehensive support for the user's physical and mental health, offering prompt and accurate medical and emotional care.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] Users input biological and emotional state information as text using a device. Examples of input include "headache" and "feeling stressed." This input data is converted into structured data by the device. Specifically, the conversion process organizes the input information into a key-value pair and packages it in JSON format.

[0168] Step 2:

[0169] The terminal encrypts the converted JSON-formatted structured data to ensure security before sending it to the server via the communication medium. The encryption process prevents data leakage and tampering. Once transmission is complete, the data is held on the server.

[0170] Step 3:

[0171] After decrypting the received structured data, the server begins data analysis. First, it uses a generative AI model to predict biological conditions. As a specific example of data analysis, it compares the causes of headaches with past medical data and performs a diagnosis of exclusion of abnormalities. The output is the predicted result of biological abnormalities.

[0172] Step 4:

[0173] The server uses an emotion analysis engine to analyze emotional state information. Specifically, it identifies emotions from the text entered by the user, recognizing, for example, "stress." The output obtained from this emotion analysis is the user's emotional state and its severity.

[0174] Step 5:

[0175] The server integrates the analysis results of the generated AI model with the results of the sentiment analysis engine to determine the appropriate course of action. Based on this integrated output, it may generate suggestions that include the need for medical intervention and methods of emotional support.

[0176] Step 6:

[0177] The server encrypts the integrated diagnostic results and recommendations and sends them to the terminal. The terminal decrypts the received data and displays it in a user interface in a format that is easy for the user to understand. This allows the user to confirm the specific actions that have been suggested.

[0178] (Application Example 2)

[0179] 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 device 14 will be referred to as the "terminal."

[0180] In modern society, the impact of emotions such as stress and anxiety, in addition to a user's biological state, on their health is gaining importance. However, there is a lack of systems that provide comprehensive health management that considers these factors simultaneously. Furthermore, while students and working individuals are required to identify and reduce stress, there are limited mechanisms that efficiently suggest solutions. This project aims to solve these problems.

[0181] 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.

[0182] In this invention, the server includes means for acquiring biological state information input by the user, means for recognizing and analyzing the user's emotions, and means for making suggestions to reduce the user's stress based on the recognized emotions. This enables comprehensive support that takes into account not only the user's physical health but also their mental health.

[0183] "Means for acquiring biological state information entered by the user" refers to a system that allows users to input information about their physical condition into an external device.

[0184] "Means of converting to structured data" refers to the process of organizing acquired biological state information into a format that is easy to analyze and transmit.

[0185] "Means of transmission via a communication network" refers to the function of transmitting converted data to a remote server or other device using a communication channel such as the internet.

[0186] "Means for receiving and analyzing transmitted structured data" refers to the process of taking in data obtained through a communication network and understanding and interpreting its contents.

[0187] A "means of predicting biological abnormalities" is a system that uses analyzed data to determine the likelihood of an abnormality occurring in a user's health condition.

[0188] "Means of suggesting treatments corresponding to biological abnormalities" refers to a function that notifies and guides the user on appropriate actions based on predicted abnormalities.

[0189] "Means for recognizing and analyzing user emotions" refers to the process of understanding the user's emotional state and using that information to perform a detailed analysis.

[0190] "Means of suggesting ways to reduce user stress" refers to a system that recommends methods and products for users to relax based on insights gained from emotion analysis.

[0191] The system realizing this invention has a program that acquires biological state information and emotional information using the user's smartphone or wearable device. The user's device provides an interface for inputting biological state (e.g., "stomach ache") and emotion (e.g., "feeling stressed"). The input data is converted into structured data using a morphological analysis library implemented in Python and sent to an AWS® EC2 server via a communication network.

[0192] The server uses a generative model powered by TENSORFLOW® and Google Cloud's natural language processing API to analyze the received data. The generative model analyzes the biological state, while the Google Cloud API is responsible for recognizing and analyzing emotions. Through this combined data analysis, the server predicts the user's biological abnormalities and determines appropriate actions based on those conditions. It also generates specific suggestions for stress reduction (e.g., relaxation techniques and related products) based on the user's emotions.

[0193] Once the analysis is complete, the server sends the results back to the user's terminal. The terminal displays the diagnostic results and stress reduction suggestions through a user interface, making it easy for the user to understand the content.

[0194] For example, if a user inputs that they feel stressed while shopping, the system will suggest relaxation music or relaxation techniques they can try at home, based on weather, location, and past health data. An example of a prompt might be: "Design an app that suggests ways for users who feel stressed while shopping to relax later. Consider what emotions the user might input and propose a mechanism to suggest appropriate responses to them."

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] The user opens the app on their smartphone and enters information about their biological state and emotions (e.g., "headache," "anxiety"). The device temporarily stores the entered information. The entered data is saved on the device in text format.

[0198] Step 2:

[0199] The terminal uses a morphological analysis library implemented in Python to convert user input data into structured data. This process involves analyzing the user input text and extracting specified keywords and phrases. Once the structured data is generated, it is ready to be sent to the server.

[0200] Step 3:

[0201] Structured data is sent to an AWS EC2 server via the internet. The server receives the data and begins analyzing the user's biological state using a generative AI model. The model processes the structured data received as input and outputs predictions about the user's health.

[0202] Step 4:

[0203] Simultaneously, the server uses Google Cloud's natural language processing API to recognize and analyze emotional information. The server analyzes the user's emotional data as input, evaluating tone and emotional intensity. This analysis is then used to quantitatively assess the user's stress level.

[0204] Step 5:

[0205] The server combines predictions of the user's biological state with emotional analysis results to determine the appropriate course of action. It integrates the results of the generative AI model with evaluated emotional data to select the most suitable stress reduction method for the user. This may include specific relaxation techniques or product recommendations.

[0206] Step 6:

[0207] The decided proposal is then transmitted back to the user's terminal via the communication network. The terminal receives it and displays the results through the user interface. The user can review specific action plans and suggestions for stress reduction and choose the next action that best suits their situation.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Second Embodiment]

[0212] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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).

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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".

[0224] The system of this invention predicts potential biological abnormalities based on biological state information entered by the user and provides appropriate treatment. This system is implemented through the user's terminal, server, and communication network.

[0225] First, the user uses an input form displayed on the device screen to enter information about their biological condition, such as specific symptoms like "headache," "fever," or "fatigue." The device then organizes the input data as structured data and sends it to the server via the communication network.

[0226] The server internally prepares the received structured data for analysis and uses a generative model to predict biological abnormalities. This generative model is trained on historical medical literature and the latest medical data to provide the best possible diagnosis for the entered symptoms.

[0227] Once the analysis and prediction are complete, the server determines specific actions based on the predicted biological abnormalities. These actions may include recommendations for consultation with a medical professional and suggestions for improvements in daily life. For example, it may suggest advice such as, "You may have the flu, so see a doctor immediately," or "Drink plenty of fluids and get plenty of rest."

[0228] Finally, the server sends response data, including the decided course of action, back to the user's terminal. The terminal displays this response data in its user interface, allowing the user to consciously assess their own health status and decide on their next course of action.

[0229] This configuration allows users to receive a rapid assessment of their health status and take appropriate initial action before visiting a medical institution. This invention contributes to the efficient use of medical resources and enhances user confidence.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The user opens a symptom input form on the device screen. Here, they enter detailed information about their symptoms, such as "headache," "fever," and "fatigue." The device receives the entered information and prepares for the next step.

[0233] Step 2:

[0234] The terminal converts the user's entered biological state information into structured data such as JSON format. This conversion prepares the data for transmission over the communication network.

[0235] Step 3:

[0236] The device sends structured data to the server as an HTTP POST request, specifying the appropriate endpoint. After sending, it waits for a response from the server.

[0237] Step 4:

[0238] The server receives structured data sent from the terminal and verifies the data's content and format. Once it confirms there are no problems, it prepares the data to be passed to the analysis engine.

[0239] Step 5:

[0240] The server uses generative models to analyze biological state information. Based on the analysis results, it predicts possible biological abnormalities and provides a diagnosis for the entered symptoms. The latest medical information is utilized in this process.

[0241] Step 6:

[0242] The server determines the appropriate course of action based on the predicted biological abnormalities. This may include recommending consultation with a medical professional, lifestyle changes, or emergency medical intervention.

[0243] Step 7:

[0244] The server compiles the determined action information into response data for the user and sends it to the terminal. This transmission is usually done using JSON format data.

[0245] Step 8:

[0246] The terminal analyzes the response data received from the server and displays it on the user interface. Based on this information, the user can decide on further actions and take the next steps to improve their health management.

[0247] (Example 1)

[0248] 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."

[0249] Rapid and accurate detection of health abnormalities based on biological status and provision of appropriate initial responses are essential to reducing the burden on medical institutions and enabling individual users to manage their health properly. However, conventional methods have made it difficult to appropriately evaluate a user's biological status or effectively propose treatment. Therefore, the present invention aims to provide a system that efficiently analyzes biological status information provided by users and provides meaningful feedback.

[0250] 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.

[0251] In this invention, the server includes means for receiving biological state information input by a user, means for preparing the received information for analysis, and means for predicting biological anomalies using a generative AI model. This enables rapid identification of anomalies based on the state information provided by the user and prompt proposal of initial response measures.

[0252] A "user" refers to an individual who uses the system by inputting biological status information.

[0253] "Biological status information" refers to data and symptoms that users provide regarding their own health status.

[0254] "Data" refers to information that has been structured to make it possible to process a user's biological state within the system.

[0255] "Communication environment" refers to the network infrastructure used to send and receive data between a user's terminal and a server.

[0256] A "server" refers to a computing device that analyzes received data and provides predictions and responses to biological abnormalities.

[0257] "Means of preparing for analysis" refers to the processes and methods used to format received data into a format that can be applied to anomaly prediction.

[0258] A "generative AI model" refers to a learning algorithm used to predict anomalies based on biological state information.

[0259] "Means for predicting biological abnormalities" refers to functions and methods that use generative AI models to identify health-related abnormalities from input information.

[0260] "Response" refers to specific action plans or measures proposed based on predicted biological abnormalities.

[0261] The system of this invention predicts biological abnormalities and provides a rapid response based on the user's biological state information. Specifically, the user first inputs health information using the terminal interface. This input information includes specific symptoms such as "headache," "fever," and "fatigue."

[0262] The terminal organizes the input information as structured data. This data is transmitted to the server via the communication environment. The server in this system is a high-performance computing device that provides an environment for analyzing the received data.

[0263] The server uses a generative AI model to analyze data and predict biological abnormalities. This AI model is trained on a large amount of historical medical information and the latest medical data, and can derive diagnoses that correspond to the user's symptoms with high accuracy. An example of a prompt for the generative AI model would be: "The user has reported fever and cough. Please indicate the most likely diagnosis and recommended actions."

[0264] Once the prediction is complete, the server determines a course of action based on the biological anomaly. This action may include specific advice such as, "You may have the flu; we recommend you seek medical attention immediately."

[0265] Ultimately, the server sends response data, including the decided course of action, back to the terminal. The terminal displays this data on its user interface, allowing the user to gain a deeper understanding of their health status and obtain guidance for taking appropriate action. Such a system enables users to quickly assess their health status and take appropriate initial action.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] The user enters specific biological condition information, such as headache or fever, through an input form displayed on the device screen. This input information is stored on the device as text data related to individual symptoms. This text data is then used to convert it into a format that can be parsed later.

[0269] Step 2:

[0270] The terminal converts the biological status information obtained from the user into structured data. This is converted into a JSON format that can be stored in a database. In this conversion process, individual symptoms are organized as data fields, and structured data is generated that is sent to the server via the communication environment.

[0271] Step 3:

[0272] The server receives structured data from terminals via the communication environment. The received data is first processed to prepare it for analysis and organized into a dataset that forms the basis for anomaly prediction. This preparation step involves checking the integrity of the data and filtering out unnecessary data.

[0273] Step 4:

[0274] The server predicts biological abnormalities using a generative AI model. The input is prepared structured data, and the AI ​​model applies a learning algorithm based on past data to output the optimal diagnosis. In this process, specific prompt sentences are given to the AI ​​model, and inference is performed to assess the likelihood of an abnormality.

[0275] Step 5:

[0276] The server determines specific actions based on the diagnostic results output by the AI ​​model. The AI ​​model's predictions are used to formulate actions such as recommendations for medical consultations or points to be aware of in daily life. The server compiles these actions as response data.

[0277] Step 6:

[0278] The server sends response data, including the decided action, to the terminal. The data is encrypted during transmission to ensure communication security. This data is then used for display on the user interface.

[0279] Step 7:

[0280] The terminal decodes the response data received from the server and displays it on the user interface. This data allows the user to gain a deeper understanding of their own health status and take necessary actions quickly. This enables the user to obtain information to take specific health-related actions.

[0281] (Application Example 1)

[0282] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0283] In modern society, it is important to quickly grasp an individual's health status and take appropriate initial measures. However, it is difficult to immediately visit a medical institution for minor health abnormalities that occur in daily life, and there is also a problem of leading to excessive consumption of medical resources. Therefore, there is a need for means to effectively monitor individual biological states and prompt appropriate responses promptly when abnormalities occur.

[0284] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0285] In this invention, the server includes means for collecting information regarding the biological state from a user who inputs data, means for converting the collected information into structured data, and means for transmitting the converted structured data via a communication network. Thereby, it becomes possible to quickly evaluate individual health states and issue an early warning when an abnormality is predicted.

[0286] The "user who inputs data" refers to a person who provides biological state information.

[0287] The "information regarding the biological state" is data related to health and physical condition, and specifically refers to information indicating symptoms such as headache, fever, and fatigue.

[0288] The "structured data" refers to digital data organized according to a specific format and structure.

[0289] The "network for communication" refers to an infrastructure that enables the transmission and reception of information, including the Internet network and mobile networks.

[0290] "Analysis" refers to the process of examining data and finding meaning based on it.

[0291] "Prediction of biological abnormalities" refers to predicting health-related problems from the collected data.

[0292] A "device that proposes countermeasures" is a device that provides specific countermeasures and advice based on predicted anomalies.

[0293] "Early warning" means issuing a warning quickly based on prediction results and prompting appropriate action.

[0294] The system of this invention begins with the user inputting their biological state information via a smartphone or smart glasses. The terminal structures the input information and transmits it to a server via a communication network. The server receives the structured data and performs analysis using a generative AI model. This generative AI model is trained on past medical literature and the latest medical data and has the ability to quickly predict the user's biological abnormalities.

[0295] If the server predicts a biological abnormality, it will formulate specific measures based on that information. These may include recommendations to consult with a health management professional or suggestions for improving daily life. For example, it might advise, "You are at risk of influenza, so we recommend getting enough rest and staying hydrated." Finally, the server sends this information to the terminal and notifies the user via smartphone or smart glasses. This allows the user to recognize health risks early and take appropriate action.

[0296] For example, if a user enters "headache" and "fatigue," the server will determine these to be potential symptoms of influenza and send a notification recommending immediate rest and consultation with a specialist. An example of a prompt message would be, "User's symptom input: fatigue. Predict possible biological abnormalities." This instruction would be sent to the generating AI model in this form. In this way, the present invention functions as a system that contributes to improving the safety and health of individual users and enables appropriate and rapid responses.

[0297] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0298] Step 1:

[0299] The user uses the input interface of a smartphone or smart glasses to input biological state information. This information includes specific symptoms (such as "headache", "fatigue", etc.). The input data is structured within the terminal.

[0300] Step 2:

[0301] The terminal sends the structured data to the server via a communication network. When sending, the data is properly formatted and encrypted to be sent securely.

[0302] Step 3:

[0303] The server prepares to analyze the structured data received through the communication network. The data is input into a generative AI model as input data for predicting biological abnormalities.

[0304] Step 4:

[0305] The generative AI model predicts biological abnormalities using the received symptom information. This model is trained based on past medical literature and the latest medical data, and predicts and outputs the risk level and likely diseases from the input data.

[0306] Step 5:

[0307] The server formulates appropriate measures based on the biological abnormalities predicted by the generative AI model. This includes recommendations for further medical actions and suggestions for improving daily life. For example, if there is a risk of influenza, recommendations for seeing a doctor and advice on rest are formulated.

[0308] Step 6:

[0309] The server sends response data, including the formulated measures, to the terminal. The response data is configured to be presented to the user through the user interface.

[0310] Step 7:

[0311] The terminal displays the received response data to the user via a user interface. Based on the information presented, the user can decide on necessary health management actions.

[0312] 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.

[0313] This invention is a system that simultaneously acquires biological state information and emotions from a user and provides appropriate diagnosis and treatment. The system consists of a user terminal, a server, a communication network, and an emotion engine.

[0314] First, the user uses their device to input not only physical symptoms but also the emotions they are feeling. For example, the user is expected to input information such as "stomach ache" or "feeling stressed." The device then organizes this information as structured data and prepares to send it to the server via the communication network.

[0315] The server analyzes the received biological state and emotional information. It uses generative models to make biological predictions, and an emotion engine recognizes the user's emotions, utilizing the results in its analysis. The emotion engine recognizes the user's emotional state based on the analysis and tone of the words entered by the user.

[0316] The server analyzes emotions and biological states to predict biological abnormalities and determine specific actions to take. These actions may include prompt consultation with a medical professional and stress management in daily life. For example, if a user is experiencing stress, the server may suggest breathing exercises or relaxation techniques to help manage stress.

[0317] Finally, the server sends the determined information to the terminal. The terminal displays the received diagnostic results and treatment information on its user interface, making it easy for the user to understand. This allows the user to quickly obtain information about their condition and decide on their next course of action while also considering their mental health.

[0318] In this form, the present invention provides comprehensive medical support that takes into account not only the user's physical health but also their mental health, thereby enhancing the user's sense of security and comfort.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The user launches the application on their device and inputs information about their biological state and emotions. During this process, the user enters descriptions such as "stomach ache" as a symptom and "feeling stressed" as an emotion into the form. The device then collects the entered data.

[0322] Step 2:

[0323] The device converts the collected biological state and emotional information into structured data in JSON format. The converted data is then prepared for efficient transmission over the communication network.

[0324] Step 3:

[0325] The device sends structured data to the server in the form of an HTTP POST request. Specify the appropriate endpoint to ensure the data reaches the server reliably. After transmission is complete, wait for a response from the server.

[0326] Step 4:

[0327] The server receives structured data sent from the terminal. It checks the content, syntax, and format of the received data for any problems and prepares it for the analysis process.

[0328] Step 5:

[0329] The server uses a generative model to analyze biological state information. Furthermore, it uses an emotion engine to analyze emotional information. The emotion engine detects "stress" from the user's description and performs analysis that takes this into account.

[0330] Step 6:

[0331] The server predicts the user's biological abnormalities based on the analysis results. At the same time, it also makes decisions on treatment that take emotional information into account. For example, in the case of stomach pain and stress, it recommends "seeing a doctor" along with "relaxation techniques for stress management."

[0332] Step 7:

[0333] The server compiles the final results into a JSON response and sends it to the terminal. This data includes the diagnostic results and recommended actions.

[0334] Step 8:

[0335] The terminal analyzes the received response data and displays it on the user interface. Users can immediately understand the analysis results and recommended actions, and use them to help plan their own health management.

[0336] (Example 2)

[0337] 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".

[0338] Traditionally, health management based on a user's biological state and emotions has generally relied on diagnoses that only consider physical symptoms. There has been a need for technology that simultaneously assesses emotional health and proposes appropriate treatment. A comprehensive approach is necessary because emotional changes can significantly impact physical health. However, systems that accurately analyze emotional information and combine it with actual health status for diagnosis are insufficient, and technology to address this problem is eagerly awaited.

[0339] 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.

[0340] In this invention, the server includes means for acquiring biological and emotional state information input by the user, means for predicting biological abnormalities and emotional states based on the analyzed data, and means for suggesting medical treatment and emotional support based on the predicted results. This makes it possible to take measures that consider not only the user's physical health but also their emotional health.

[0341] A "user" refers to an individual who provides information about their biological and emotional states to the system.

[0342] "Biological status information" refers to objective data regarding the user's physical health status.

[0343] "Emotional state information" refers to data that indicates the user's subjective emotional state.

[0344] "Structured data" refers to data that has been converted into a format suitable for subsequent analysis.

[0345] "Communication medium" refers to the technical elements used to send and receive data, and includes networks.

[0346] A "generative model" refers to a machine learning algorithm used for data analysis and prediction.

[0347] A "sentiment analysis engine" refers to software used to identify and analyze a user's emotional state.

[0348] "Medical treatment" refers to the treatments and countermeasures applied based on the user's physical health condition.

[0349] "Emotional support" refers to advice or methods for improving or maintaining a user's emotional state.

[0350] This invention is a system that analyzes a user's biological and emotional state information to provide appropriate medical treatment and emotional support. The system mainly consists of a terminal, a server, a communication medium, and an emotion analysis engine.

[0351] Users can input biometric and emotional status information at any time using their own devices. The device converts this information into structured data, securely encrypts it, and transmits it to the server via a communication medium. This allows users to easily record and send health information to the server, even from home.

[0352] The server uses specialized software to analyze the received structured data. It leverages generative AI models to perform data analysis that predicts future changes in biological states. This analysis involves referencing historical medical databases and conducting detailed pattern analysis. Meanwhile, the sentiment analysis engine processes emotional information entered by the user, identifying the user's emotional state based on text and tone.

[0353] For example, if a user inputs "I have a headache and I'm stressed," the server receives this information and uses a generative AI model to predict the cause of the headache, while simultaneously evaluating the level of stress using an emotion analysis engine. Based on the analysis results, the server can suggest consulting a medical professional and, at the same time, provide the user with relaxation techniques to alleviate stress.

[0354] A possible prompt message might be, "I currently have a stomach ache and am feeling stressed. What advice do you have for me?" This system aims to provide comprehensive support for the user's physical and mental health, offering prompt and accurate medical and emotional care.

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] Users input biological and emotional state information as text using a device. Examples of input include "headache" and "feeling stressed." This input data is converted into structured data by the device. Specifically, the conversion process organizes the input information into a key-value pair and packages it in JSON format.

[0358] Step 2:

[0359] The terminal encrypts the converted JSON-formatted structured data to ensure security before sending it to the server via the communication medium. The encryption process prevents data leakage and tampering. Once transmission is complete, the data is held on the server.

[0360] Step 3:

[0361] After decrypting the received structured data, the server begins data analysis. First, it uses a generative AI model to predict biological conditions. As a specific example of data analysis, it compares the causes of headaches with past medical data and performs a diagnosis of exclusion of abnormalities. The output is the predicted result of biological abnormalities.

[0362] Step 4:

[0363] The server uses an emotion analysis engine to analyze emotional state information. Specifically, it identifies emotions from the text entered by the user, recognizing, for example, "stress." The output obtained from this emotion analysis is the user's emotional state and its severity.

[0364] Step 5:

[0365] The server integrates the analysis results of the generated AI model with the results of the sentiment analysis engine to determine the appropriate course of action. Based on this integrated output, it may generate suggestions that include the need for medical intervention and methods of emotional support.

[0366] Step 6:

[0367] The server encrypts the integrated diagnostic results and recommendations and sends them to the terminal. The terminal decrypts the received data and displays it in a user interface in a format that is easy for the user to understand. This allows the user to confirm the specific actions that have been suggested.

[0368] (Application Example 2)

[0369] 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 as the "terminal".

[0370] In modern society, the impact of emotions such as stress and anxiety, in addition to a user's biological state, on their health is gaining importance. However, there is a lack of systems that provide comprehensive health management that considers these factors simultaneously. Furthermore, while students and working individuals are required to identify and reduce stress, there are limited mechanisms that efficiently suggest solutions. This project aims to solve these problems.

[0371] 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.

[0372] In this invention, the server includes means for acquiring biological state information input by the user, means for recognizing and analyzing the user's emotions, and means for making suggestions to reduce the user's stress based on the recognized emotions. This enables comprehensive support that takes into account not only the user's physical health but also their mental health.

[0373] "Means for acquiring biological state information entered by the user" refers to a system that allows users to input information about their physical condition into an external device.

[0374] "Means of converting to structured data" refers to the process of organizing acquired biological state information into a format that is easy to analyze and transmit.

[0375] "Means of transmission via a communication network" refers to the function of transmitting converted data to a remote server or other device using a communication channel such as the internet.

[0376] "Means for receiving and analyzing transmitted structured data" refers to the process of taking in data obtained through a communication network and understanding and interpreting its contents.

[0377] A "means of predicting biological abnormalities" is a system that uses analyzed data to determine the likelihood of an abnormality occurring in a user's health condition.

[0378] "Means of suggesting treatments corresponding to biological abnormalities" refers to a function that notifies and guides the user on appropriate actions based on predicted abnormalities.

[0379] "Means for recognizing and analyzing user emotions" refers to the process of understanding the user's emotional state and using that information to perform a detailed analysis.

[0380] "Means of suggesting ways to reduce user stress" refers to a system that recommends methods and products for users to relax based on insights gained from emotion analysis.

[0381] The system realizing this invention has a program that acquires biological state information and emotional information using the user's smartphone or wearable device. The user's device provides an interface for inputting biological state (e.g., "stomach ache") and emotion (e.g., "feeling stressed"). The input data is converted into structured data using a morphological analysis library implemented in Python and sent to an AWS EC2 server via a communication network.

[0382] The server uses a TensorFlow generative model and Google Cloud's natural language processing API to analyze the received data. The generative model analyzes the biological state, while the Google Cloud API is responsible for recognizing and analyzing emotions. Through this combined data analysis, the server predicts the user's biological abnormalities and determines appropriate actions based on those conditions. It also generates specific suggestions for stress reduction (e.g., relaxation techniques and related products) based on the user's emotions.

[0383] Once the analysis is complete, the server sends the results back to the user's terminal. The terminal displays the diagnostic results and stress reduction suggestions through a user interface, making it easy for the user to understand the content.

[0384] For example, if a user inputs that they feel stressed while shopping, the system will suggest relaxation music or relaxation techniques they can try at home, based on weather, location, and past health data. An example of a prompt might be: "Design an app that suggests ways for users who feel stressed while shopping to relax later. Consider what emotions the user might input and propose a mechanism to suggest appropriate responses to them."

[0385] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0386] Step 1:

[0387] The user opens the app on their smartphone and enters information about their biological state and emotions (e.g., "headache," "anxiety"). The device temporarily stores the entered information. The entered data is saved on the device in text format.

[0388] Step 2:

[0389] The terminal uses a morphological analysis library implemented in Python to convert user input data into structured data. This process involves analyzing the user input text and extracting specified keywords and phrases. Once the structured data is generated, it is ready to be sent to the server.

[0390] Step 3:

[0391] Structured data is sent to an AWS EC2 server via the internet. The server receives the data and begins analyzing the user's biological state using a generative AI model. The model processes the structured data received as input and outputs predictions about the user's health.

[0392] Step 4:

[0393] Simultaneously, the server uses Google Cloud's natural language processing API to recognize and analyze emotional information. The server analyzes the user's emotional data as input, evaluating tone and emotional intensity. This analysis is then used to quantitatively assess the user's stress level.

[0394] Step 5:

[0395] The server combines predictions of the user's biological state with emotional analysis results to determine the appropriate course of action. It integrates the results of the generative AI model with evaluated emotional data to select the most suitable stress reduction method for the user. This may include specific relaxation techniques or product recommendations.

[0396] Step 6:

[0397] The decided proposal is then transmitted back to the user's terminal via the communication network. The terminal receives it and displays the results through the user interface. The user can review specific action plans and suggestions for stress reduction and choose the next action that best suits their situation.

[0398] 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.

[0399] 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.

[0400] 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.

[0401] [Third Embodiment]

[0402] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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".

[0414] The system of this invention predicts potential biological abnormalities based on biological state information entered by the user and provides appropriate treatment. This system is implemented through the user's terminal, server, and communication network.

[0415] First, the user uses an input form displayed on the device screen to enter information about their biological condition, such as specific symptoms like "headache," "fever," or "fatigue." The device then organizes the input data as structured data and sends it to the server via the communication network.

[0416] The server internally prepares the received structured data for analysis and uses a generative model to predict biological abnormalities. This generative model is trained on historical medical literature and the latest medical data to provide the best possible diagnosis for the entered symptoms.

[0417] Once the analysis and prediction are complete, the server determines specific actions based on the predicted biological abnormalities. These actions may include recommendations for consultation with a medical professional and suggestions for improvements in daily life. For example, it may suggest advice such as, "You may have the flu, so see a doctor immediately," or "Drink plenty of fluids and get plenty of rest."

[0418] Finally, the server sends response data, including the decided course of action, back to the user's terminal. The terminal displays this response data in its user interface, allowing the user to consciously assess their own health status and decide on their next course of action.

[0419] This configuration allows users to receive a rapid assessment of their health status and take appropriate initial action before visiting a medical institution. This invention contributes to the efficient use of medical resources and enhances user confidence.

[0420] The following describes the processing flow.

[0421] Step 1:

[0422] The user opens a symptom input form on the device screen. Here, they enter detailed information about their symptoms, such as "headache," "fever," and "fatigue." The device receives the entered information and prepares for the next step.

[0423] Step 2:

[0424] The terminal converts the user's entered biological state information into structured data such as JSON format. This conversion prepares the data for transmission over the communication network.

[0425] Step 3:

[0426] The device sends structured data to the server as an HTTP POST request, specifying the appropriate endpoint. After sending, it waits for a response from the server.

[0427] Step 4:

[0428] The server receives structured data sent from the terminal and verifies the data's content and format. Once it confirms there are no problems, it prepares the data to be passed to the analysis engine.

[0429] Step 5:

[0430] The server uses generative models to analyze biological state information. Based on the analysis results, it predicts possible biological abnormalities and provides a diagnosis for the entered symptoms. The latest medical information is utilized in this process.

[0431] Step 6:

[0432] The server determines the appropriate course of action based on the predicted biological abnormalities. This may include recommending consultation with a medical professional, lifestyle changes, or emergency medical intervention.

[0433] Step 7:

[0434] The server compiles the determined action information into response data for the user and sends it to the terminal. This transmission is usually done using JSON format data.

[0435] Step 8:

[0436] The terminal analyzes the response data received from the server and displays it on the user interface. Based on this information, the user can decide on further actions and take the next steps to improve their health management.

[0437] (Example 1)

[0438] 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."

[0439] Rapid and accurate detection of health abnormalities based on biological status and provision of appropriate initial responses are essential to reducing the burden on medical institutions and enabling individual users to manage their health properly. However, conventional methods have made it difficult to appropriately evaluate a user's biological status or effectively propose treatment. Therefore, the present invention aims to provide a system that efficiently analyzes biological status information provided by users and provides meaningful feedback.

[0440] 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.

[0441] In this invention, the server includes means for receiving biological state information input by a user, means for preparing the received information for analysis, and means for predicting biological anomalies using a generative AI model. This enables rapid identification of anomalies based on the state information provided by the user and prompt proposal of initial response measures.

[0442] A "user" refers to an individual who uses the system by inputting biological status information.

[0443] "Biological status information" refers to data and symptoms that users provide regarding their own health status.

[0444] "Data" refers to information that has been structured to make it possible to process a user's biological state within the system.

[0445] "Communication environment" refers to the network infrastructure used to send and receive data between a user's terminal and a server.

[0446] A "server" refers to a computing device that analyzes received data and provides predictions and responses to biological abnormalities.

[0447] "Means of preparing for analysis" refers to the processes and methods used to format received data into a format that can be applied to anomaly prediction.

[0448] A "generative AI model" refers to a learning algorithm used to predict anomalies based on biological state information.

[0449] "Means for predicting biological abnormalities" refers to functions and methods that use generative AI models to identify health-related abnormalities from input information.

[0450] "Response" refers to specific action plans or measures proposed based on predicted biological abnormalities.

[0451] The system of this invention predicts biological abnormalities and provides a rapid response based on the user's biological state information. Specifically, the user first inputs health information using the terminal interface. This input information includes specific symptoms such as "headache," "fever," and "fatigue."

[0452] The terminal organizes the input information as structured data. This data is transmitted to the server via the communication environment. The server in this system is a high-performance computing device that provides an environment for analyzing the received data.

[0453] The server uses a generative AI model to analyze data and predict biological abnormalities. This AI model is trained on a large amount of historical medical information and the latest medical data, and can derive diagnoses that correspond to the user's symptoms with high accuracy. An example of a prompt for the generative AI model would be: "The user has reported fever and cough. Please indicate the most likely diagnosis and recommended actions."

[0454] Once the prediction is complete, the server determines a course of action based on the biological anomaly. This action may include specific advice such as, "You may have the flu; we recommend you seek medical attention immediately."

[0455] Ultimately, the server sends response data, including the decided course of action, back to the terminal. The terminal displays this data on its user interface, allowing the user to gain a deeper understanding of their health status and obtain guidance for taking appropriate action. Such a system enables users to quickly assess their health status and take appropriate initial action.

[0456] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0457] Step 1:

[0458] The user enters specific biological condition information, such as headache or fever, through an input form displayed on the device screen. This input information is stored on the device as text data related to individual symptoms. This text data is then used to convert it into a format that can be parsed later.

[0459] Step 2:

[0460] The terminal converts the biological status information obtained from the user into structured data. This is converted into a JSON format that can be stored in a database. In this conversion process, individual symptoms are organized as data fields, and structured data is generated that is sent to the server via the communication environment.

[0461] Step 3:

[0462] The server receives structured data from terminals via the communication environment. The received data is first processed for analysis and organized into a dataset that forms the basis for anomaly prediction. This preparation step involves checking the integrity of the data and filtering out unnecessary data.

[0463] Step 4:

[0464] The server predicts biological abnormalities using a generative AI model. The input is prepared structured data, and the AI ​​model applies a learning algorithm based on past data to output the optimal diagnosis. In this process, specific prompt sentences are given to the AI ​​model, and inference is performed to assess the likelihood of an abnormality.

[0465] Step 5:

[0466] The server determines specific actions based on the diagnostic results output by the AI ​​model. The AI ​​model's predictions are used to formulate actions such as recommendations for medical consultations or points to be aware of in daily life. The server compiles these actions as response data.

[0467] Step 6:

[0468] The server sends response data, including the decided action, to the terminal. The data is encrypted during transmission to ensure communication security. This data is then used for display on the user interface.

[0469] Step 7:

[0470] The terminal decodes the response data received from the server and displays it on the user interface. This data allows the user to gain a deeper understanding of their own health status and take necessary actions quickly. This enables the user to obtain information to take specific health-related actions.

[0471] (Application Example 1)

[0472] 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."

[0473] In modern society, it is crucial to quickly understand an individual's health status and provide appropriate initial treatment. However, it is difficult for people to immediately visit a medical institution for minor health issues that occur in daily life, and this can lead to excessive consumption of medical resources. Therefore, there is a need for means to effectively monitor individual biological states and promptly encourage appropriate responses when abnormalities occur.

[0474] 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.

[0475] In this invention, the server includes means for collecting information about the biological state from users who input data, means for converting the collected information into structured data, and means for transmitting the converted structured data via a communication network. This makes it possible to quickly assess the health status of individuals and issue early warnings when abnormalities are predicted.

[0476] "Users who input data" refers to individuals who provide information about their biological state.

[0477] "Information regarding biological status" refers to data related to health and physical condition, specifically information about symptoms such as headaches, fever, and fatigue.

[0478] "Structured data" refers to digital data that is organized according to a specific format or structure.

[0479] A "network for communication" refers to the infrastructure that enables the sending and receiving of information, and includes the Internet network and mobile networks.

[0480] "Analysis" is the process of examining data and finding meaning based on it.

[0481] "Predicting biological abnormalities" refers to predicting health problems based on collected data.

[0482] A "device that proposes countermeasures" is a device that provides specific countermeasures and advice based on predicted anomalies.

[0483] "Early warning" means issuing a warning quickly based on prediction results and prompting appropriate action.

[0484] The system of this invention begins with the user inputting their biological state information via a smartphone or smart glasses. The terminal structures the input information and transmits it to a server via a communication network. The server receives the structured data and performs analysis using a generative AI model. This generative AI model is trained on past medical literature and the latest medical data and has the ability to quickly predict the user's biological abnormalities.

[0485] If the server predicts a biological abnormality, it will formulate specific measures based on that information. These may include recommendations to consult with a health management professional or suggestions for improving daily life. For example, it might advise, "You are at risk of influenza, so we recommend getting enough rest and staying hydrated." Finally, the server sends this information to the terminal and notifies the user via smartphone or smart glasses. This allows the user to recognize health risks early and take appropriate action.

[0486] For example, if a user enters "headache" and "fatigue," the server will determine these to be potential symptoms of influenza and send a notification recommending immediate rest and consultation with a specialist. An example of a prompt message would be, "User's symptom input: fatigue. Predict possible biological abnormalities." This instruction would be sent to the generating AI model in this form. In this way, the present invention functions as a system that contributes to improving the safety and health of individual users and enables appropriate and rapid responses.

[0487] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0488] Step 1:

[0489] The user enters biological status information using the input interface of a smartphone or smart glasses. This information includes specific symptoms (e.g., "headache," "fatigue," etc.). The entered data is structured within the device.

[0490] Step 2:

[0491] The device sends structured data to the server via a communication network. During transmission, the data is properly formatted and encrypted to ensure secure transmission.

[0492] Step 3:

[0493] The server prepares to analyze the structured data received via the communication network. The data is then fed into a generative AI model as input data to predict biological abnormalities.

[0494] Step 4:

[0495] The generative AI model uses received symptom information to predict biological abnormalities. This model is trained on historical medical literature and the latest medical data, and predicts and outputs risk levels and likely diseases from the input data.

[0496] Step 5:

[0497] The server develops appropriate measures based on the biological abnormalities predicted by the generating AI model. This includes recommendations for further medical actions and suggestions for improvements in daily life. For example, if there is a risk of influenza, recommendations for seeing a doctor and advice on rest will be developed.

[0498] Step 6:

[0499] The server sends response data, including the formulated measures, to the terminal. The response data is configured to be presented to the user through the user interface.

[0500] Step 7:

[0501] The terminal displays the received response data to the user via a user interface. Based on the information presented, the user can decide on necessary health management actions.

[0502] 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.

[0503] This invention is a system that simultaneously acquires biological state information and emotions from a user and provides appropriate diagnosis and treatment. The system consists of a user terminal, a server, a communication network, and an emotion engine.

[0504] First, the user uses their device to input not only physical symptoms but also the emotions they are feeling. For example, the user is expected to input information such as "stomach ache" or "feeling stressed." The device then organizes this information as structured data and prepares to send it to the server via the communication network.

[0505] The server analyzes the received biological state and emotional information. It uses generative models to make biological predictions, and an emotion engine recognizes the user's emotions, utilizing the results in its analysis. The emotion engine recognizes the user's emotional state based on the analysis and tone of the words entered by the user.

[0506] The server analyzes emotions and biological states to predict biological abnormalities and determine specific actions to take. These actions may include prompt consultation with a medical professional and stress management in daily life. For example, if a user is experiencing stress, the server may suggest breathing exercises or relaxation techniques to help manage stress.

[0507] Finally, the server sends the determined information to the terminal. The terminal displays the received diagnostic results and treatment information on its user interface, making it easy for the user to understand. This allows the user to quickly obtain information about their condition and decide on their next course of action while also considering their mental health.

[0508] In this form, the present invention provides comprehensive medical support that takes into account not only the user's physical health but also their mental health, thereby enhancing the user's sense of security and comfort.

[0509] The following describes the processing flow.

[0510] Step 1:

[0511] The user launches the application on their device and inputs information about their biological state and emotions. During this process, the user enters descriptions such as "stomach ache" as a symptom and "feeling stressed" as an emotion into the form. The device then collects the entered data.

[0512] Step 2:

[0513] The device converts the collected biological state and emotional information into structured data in JSON format. The converted data is then prepared for efficient transmission over the communication network.

[0514] Step 3:

[0515] The device sends structured data to the server in the form of an HTTP POST request. Specify the appropriate endpoint to ensure the data reaches the server reliably. After transmission is complete, wait for a response from the server.

[0516] Step 4:

[0517] The server receives structured data sent from the terminal. It checks the content, syntax, and format of the received data for any problems and prepares it for the analysis process.

[0518] Step 5:

[0519] The server uses a generative model to analyze biological state information. Furthermore, it uses an emotion engine to analyze emotional information. The emotion engine detects "stress" from the user's description and performs analysis that takes this into account.

[0520] Step 6:

[0521] The server predicts the user's biological abnormalities based on the analysis results. At the same time, it also makes decisions on treatment that take emotional information into account. For example, in the case of stomach pain and stress, it recommends "seeing a doctor" along with "relaxation techniques for stress management."

[0522] Step 7:

[0523] The server compiles the final results into a JSON response and sends it to the terminal. This data includes the diagnostic results and recommended actions.

[0524] Step 8:

[0525] The terminal analyzes the received response data and displays it on the user interface. Users can immediately understand the analysis results and recommended actions, and use them to help plan their own health management.

[0526] (Example 2)

[0527] 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."

[0528] Traditionally, health management based on a user's biological state and emotions has generally relied on diagnoses that only consider physical symptoms. There has been a need for technology that simultaneously assesses emotional health and proposes appropriate treatment. A comprehensive approach is necessary because emotional changes can significantly impact physical health. However, systems that accurately analyze emotional information and combine it with actual health status for diagnosis are insufficient, and technology to address this problem is eagerly awaited.

[0529] 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.

[0530] In this invention, the server includes means for acquiring biological and emotional state information input by the user, means for predicting biological abnormalities and emotional states based on the analyzed data, and means for suggesting medical treatment and emotional support based on the predicted results. This makes it possible to take measures that consider not only the user's physical health but also their emotional health.

[0531] A "user" refers to an individual who provides information about their biological and emotional states to the system.

[0532] "Biological status information" refers to objective data regarding the user's physical health status.

[0533] "Emotional state information" refers to data that indicates the user's subjective emotional state.

[0534] "Structured data" refers to data that has been converted into a format suitable for subsequent analysis.

[0535] "Communication medium" refers to the technical elements used to send and receive data, and includes networks.

[0536] A "generative model" refers to a machine learning algorithm used for data analysis and prediction.

[0537] A "sentiment analysis engine" refers to software used to identify and analyze a user's emotional state.

[0538] "Medical treatment" refers to the treatments and countermeasures applied based on the user's physical health condition.

[0539] "Emotional support" refers to advice or methods for improving or maintaining a user's emotional state.

[0540] This invention is a system that analyzes a user's biological and emotional state information to provide appropriate medical treatment and emotional support. The system mainly consists of a terminal, a server, a communication medium, and an emotion analysis engine.

[0541] Users can input biometric and emotional status information at any time using their own devices. The device converts this information into structured data, securely encrypts it, and transmits it to the server via a communication medium. This allows users to easily record and send health information to the server, even from home.

[0542] The server uses specialized software to analyze the received structured data. It leverages generative AI models to perform data analysis that predicts future changes in biological states. This analysis involves referencing historical medical databases and conducting detailed pattern analysis. Meanwhile, the sentiment analysis engine processes emotional information entered by the user, identifying the user's emotional state based on text and tone.

[0543] For example, if a user inputs "I have a headache and I'm stressed," the server receives this information and uses a generative AI model to predict the cause of the headache, while simultaneously evaluating the level of stress using an emotion analysis engine. Based on the analysis results, the server can suggest consulting a medical professional and, at the same time, provide the user with relaxation techniques to alleviate stress.

[0544] A possible prompt message might be, "I currently have a stomach ache and am feeling stressed. What advice do you have for me?" This system aims to provide comprehensive support for the user's physical and mental health, offering prompt and accurate medical and emotional care.

[0545] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0546] Step 1:

[0547] Users input biological and emotional state information as text using a device. Examples of input include "headache" and "feeling stressed." This input data is converted into structured data by the device. Specifically, the conversion process organizes the input information into a key-value pair and packages it in JSON format.

[0548] Step 2:

[0549] The terminal encrypts the converted JSON-formatted structured data to ensure security before sending it to the server via the communication medium. The encryption process prevents data leakage and tampering. Once transmission is complete, the data is held on the server.

[0550] Step 3:

[0551] After decrypting the received structured data, the server begins data analysis. First, it uses a generative AI model to predict biological conditions. As a specific example of data analysis, it compares the causes of headaches with past medical data and performs a diagnosis of exclusion of abnormalities. The output is the predicted result of biological abnormalities.

[0552] Step 4:

[0553] The server uses an emotion analysis engine to analyze emotional state information. Specifically, it identifies emotions from the text entered by the user, recognizing, for example, "stress." The output obtained from this emotion analysis is the user's emotional state and its severity.

[0554] Step 5:

[0555] The server integrates the analysis results of the generated AI model with the results of the sentiment analysis engine to determine the appropriate course of action. Based on this integrated output, it may generate suggestions that include the need for medical intervention and methods of emotional support.

[0556] Step 6:

[0557] The server encrypts the integrated diagnostic results and recommendations and sends them to the terminal. The terminal decrypts the received data and displays it in a user interface in a format that is easy for the user to understand. This allows the user to confirm the specific actions that have been suggested.

[0558] (Application Example 2)

[0559] 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."

[0560] In modern society, the impact of emotions such as stress and anxiety, in addition to a user's biological state, on their health is gaining importance. However, there is a lack of systems that provide comprehensive health management that considers these factors simultaneously. Furthermore, while students and working individuals are required to identify and reduce stress, there are limited mechanisms that efficiently suggest solutions. This project aims to solve these problems.

[0561] 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.

[0562] In this invention, the server includes means for acquiring biological state information input by the user, means for recognizing and analyzing the user's emotions, and means for making suggestions to reduce the user's stress based on the recognized emotions. This enables comprehensive support that takes into account not only the user's physical health but also their mental health.

[0563] "Means for acquiring biological state information entered by the user" refers to a system that allows users to input information about their physical condition into an external device.

[0564] "Means of converting to structured data" refers to the process of organizing acquired biological state information into a format that is easy to analyze and transmit.

[0565] "Means of transmission via a communication network" refers to the function of transmitting converted data to a remote server or other device using a communication channel such as the internet.

[0566] "Means for receiving and analyzing transmitted structured data" refers to the process of taking in data obtained through a communication network and understanding and interpreting its contents.

[0567] A "means of predicting biological abnormalities" is a system that uses analyzed data to determine the likelihood of an abnormality occurring in a user's health condition.

[0568] "Means of suggesting treatments corresponding to biological abnormalities" refers to a function that notifies and guides the user on appropriate actions based on predicted abnormalities.

[0569] "Means for recognizing and analyzing user emotions" refers to the process of understanding the user's emotional state and using that information to perform a detailed analysis.

[0570] "Means of suggesting ways to reduce user stress" refers to a system that recommends methods and products for users to relax based on insights gained from emotion analysis.

[0571] The system realizing this invention has a program that acquires biological state information and emotional information using the user's smartphone or wearable device. The user's device provides an interface for inputting biological state (e.g., "stomach ache") and emotion (e.g., "feeling stressed"). The input data is converted into structured data using a morphological analysis library implemented in Python and sent to an AWS EC2 server via a communication network.

[0572] The server uses a TensorFlow generative model and Google Cloud's natural language processing API to analyze the received data. The generative model analyzes the biological state, while the Google Cloud API is responsible for recognizing and analyzing emotions. Through this combined data analysis, the server predicts the user's biological abnormalities and determines appropriate actions based on those conditions. It also generates specific suggestions for stress reduction (e.g., relaxation techniques and related products) based on the user's emotions.

[0573] Once the analysis is complete, the server sends the results back to the user's terminal. The terminal displays the diagnostic results and stress reduction suggestions through a user interface, making it easy for the user to understand the content.

[0574] For example, if a user inputs that they feel stressed while shopping, the system will suggest relaxation music or relaxation techniques they can try at home, based on weather, location, and past health data. An example of a prompt might be: "Design an app that suggests ways for users who feel stressed while shopping to relax later. Consider what emotions the user might input and propose a mechanism to suggest appropriate responses to them."

[0575] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0576] Step 1:

[0577] The user opens the app on their smartphone and enters information about their biological state and emotions (e.g., "headache," "anxiety"). The device temporarily stores the entered information. The entered data is saved on the device in text format.

[0578] Step 2:

[0579] The terminal uses a morphological analysis library implemented in Python to convert user input data into structured data. This process involves analyzing the user input text and extracting specified keywords and phrases. Once the structured data is generated, it is ready to be sent to the server.

[0580] Step 3:

[0581] Structured data is sent to an AWS EC2 server via the internet. The server receives the data and begins analyzing the user's biological state using a generative AI model. The model processes the structured data received as input and outputs predictions about the user's health.

[0582] Step 4:

[0583] Simultaneously, the server uses Google Cloud's natural language processing API to recognize and analyze emotional information. The server analyzes the user's emotional data as input, evaluating tone and emotional intensity. This analysis is then used to quantitatively assess the user's stress level.

[0584] Step 5:

[0585] The server combines predictions of the user's biological state with emotional analysis results to determine the appropriate course of action. It integrates the results of the generative AI model with evaluated emotional data to select the most suitable stress reduction method for the user. This may include specific relaxation techniques or product recommendations.

[0586] Step 6:

[0587] The decided proposal is then transmitted back to the user's terminal via the communication network. The terminal receives it and displays the results through the user interface. The user can review specific action plans and suggestions for stress reduction and choose the next action that best suits their situation.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] [Fourth Embodiment]

[0592] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0593] 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.

[0594] 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).

[0595] 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.

[0596] 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.

[0597] 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).

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] 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".

[0605] The system of this invention predicts potential biological abnormalities based on biological state information entered by the user and provides appropriate treatment. This system is implemented through the user's terminal, server, and communication network.

[0606] First, the user uses an input form displayed on the device screen to enter information about their biological condition, such as specific symptoms like "headache," "fever," or "fatigue." The device then organizes the input data as structured data and sends it to the server via the communication network.

[0607] The server internally prepares the received structured data for analysis and uses a generative model to predict biological abnormalities. This generative model is trained on historical medical literature and the latest medical data to provide the best possible diagnosis for the entered symptoms.

[0608] Once the analysis and prediction are complete, the server determines specific actions based on the predicted biological abnormalities. These actions may include recommendations for consultation with a medical professional and suggestions for improvements in daily life. For example, it may suggest advice such as, "You may have the flu, so see a doctor immediately," or "Drink plenty of fluids and get plenty of rest."

[0609] Finally, the server sends response data, including the decided course of action, back to the user's terminal. The terminal displays this response data in its user interface, allowing the user to consciously assess their own health status and decide on their next course of action.

[0610] This configuration allows users to receive a rapid assessment of their health status and take appropriate initial action before visiting a medical institution. This invention contributes to the efficient use of medical resources and enhances user confidence.

[0611] The following describes the processing flow.

[0612] Step 1:

[0613] The user opens a symptom input form on the device screen. Here, they enter detailed information about their symptoms, such as "headache," "fever," and "fatigue." The device receives the entered information and prepares for the next step.

[0614] Step 2:

[0615] The terminal converts the user's entered biological state information into structured data such as JSON format. This conversion prepares the data for transmission over the communication network.

[0616] Step 3:

[0617] The device sends structured data to the server as an HTTP POST request, specifying the appropriate endpoint. After sending, it waits for a response from the server.

[0618] Step 4:

[0619] The server receives structured data sent from the terminal and verifies the data's content and format. Once it confirms there are no problems, it prepares the data to be passed to the analysis engine.

[0620] Step 5:

[0621] The server uses generative models to analyze biological state information. Based on the analysis results, it predicts possible biological abnormalities and provides a diagnosis for the entered symptoms. The latest medical information is utilized in this process.

[0622] Step 6:

[0623] The server determines the appropriate course of action based on the predicted biological abnormalities. This may include recommending consultation with a medical professional, lifestyle changes, or emergency medical intervention.

[0624] Step 7:

[0625] The server compiles the determined action information into response data for the user and sends it to the terminal. This transmission is usually done using JSON format data.

[0626] Step 8:

[0627] The terminal analyzes the response data received from the server and displays it on the user interface. Based on this information, the user can decide on further actions and take the next steps to improve their health management.

[0628] (Example 1)

[0629] 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".

[0630] Rapid and accurate detection of health abnormalities based on biological status and provision of appropriate initial responses are essential to reducing the burden on medical institutions and enabling individual users to manage their health properly. However, conventional methods have made it difficult to appropriately evaluate a user's biological status or effectively propose treatment. Therefore, the present invention aims to provide a system that efficiently analyzes biological status information provided by users and provides meaningful feedback.

[0631] 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.

[0632] In this invention, the server includes means for receiving biological state information input by a user, means for preparing the received information for analysis, and means for predicting biological anomalies using a generative AI model. This enables rapid identification of anomalies based on the state information provided by the user and prompt proposal of initial response measures.

[0633] A "user" refers to an individual who uses the system by inputting biological status information.

[0634] "Biological status information" refers to data and symptoms that users provide regarding their own health status.

[0635] "Data" refers to information that has been structured to make it possible to process a user's biological state within the system.

[0636] "Communication environment" refers to the network infrastructure used to send and receive data between a user's terminal and a server.

[0637] A "server" refers to a computing device that analyzes received data and provides predictions and responses to biological abnormalities.

[0638] "Means of preparing for analysis" refers to the processes and methods used to format received data into a format that can be applied to anomaly prediction.

[0639] A "generative AI model" refers to a learning algorithm used to predict anomalies based on biological state information.

[0640] "Means for predicting biological abnormalities" refers to functions and methods that use generative AI models to identify health-related abnormalities from input information.

[0641] "Response" refers to specific action plans or measures proposed based on predicted biological abnormalities.

[0642] The system of this invention predicts biological abnormalities and provides a rapid response based on the user's biological state information. Specifically, the user first inputs health information using the terminal interface. This input information includes specific symptoms such as "headache," "fever," and "fatigue."

[0643] The terminal organizes the input information as structured data. This data is transmitted to the server via the communication environment. The server in this system is a high-performance computing device that provides an environment for analyzing the received data.

[0644] The server uses a generative AI model to analyze data and predict biological abnormalities. This AI model is trained on a large amount of historical medical information and the latest medical data, and can derive diagnoses that correspond to the user's symptoms with high accuracy. An example of a prompt for the generative AI model would be: "The user has reported fever and cough. Please indicate the most likely diagnosis and recommended actions."

[0645] Once the prediction is complete, the server determines a course of action based on the biological anomaly. This action may include specific advice such as, "You may have the flu; we recommend you seek medical attention immediately."

[0646] Ultimately, the server sends response data, including the decided course of action, back to the terminal. The terminal displays this data on its user interface, allowing the user to gain a deeper understanding of their health status and obtain guidance for taking appropriate action. Such a system enables users to quickly assess their health status and take appropriate initial action.

[0647] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0648] Step 1:

[0649] The user enters specific biological condition information, such as headache or fever, through an input form displayed on the device screen. This input information is stored on the device as text data related to individual symptoms. This text data is then used to convert it into a format that can be parsed later.

[0650] Step 2:

[0651] The terminal converts the biological status information obtained from the user into structured data. This is converted into a JSON format that can be stored in a database. In this conversion process, individual symptoms are organized as data fields, and structured data is generated that is sent to the server via the communication environment.

[0652] Step 3:

[0653] The server receives structured data from terminals via the communication environment. The received data is first processed to prepare it for analysis and organized into a dataset that forms the basis for anomaly prediction. This preparation step involves checking the data's integrity and filtering out unnecessary data.

[0654] Step 4:

[0655] The server predicts biological abnormalities using a generative AI model. The input is prepared structured data, and the AI ​​model applies a learning algorithm based on past data to output the optimal diagnosis. In this process, specific prompt sentences are given to the AI ​​model, and inference is performed to assess the likelihood of an abnormality.

[0656] Step 5:

[0657] The server determines specific actions based on the diagnostic results output by the AI ​​model. The AI ​​model's predictions are used to formulate actions such as recommendations for medical consultations or points to be aware of in daily life. The server compiles these actions as response data.

[0658] Step 6:

[0659] The server sends response data, including the decided action, to the terminal. The data is encrypted during transmission to ensure communication security. This data is then used for display on the user interface.

[0660] Step 7:

[0661] The terminal decodes the response data received from the server and displays it on the user interface. This data allows the user to gain a deeper understanding of their own health status and take necessary actions quickly. This enables the user to obtain information to take specific health-related actions.

[0662] (Application Example 1)

[0663] 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".

[0664] In modern society, it is crucial to quickly understand an individual's health status and provide appropriate initial treatment. However, it is difficult for people to immediately visit a medical institution for minor health issues that occur in daily life, and this can lead to excessive consumption of medical resources. Therefore, there is a need for means to effectively monitor individual biological states and promptly encourage appropriate responses when abnormalities occur.

[0665] 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.

[0666] In this invention, the server includes means for collecting information about the biological state from users who input data, means for converting the collected information into structured data, and means for transmitting the converted structured data via a communication network. This makes it possible to quickly assess the health status of individuals and issue early warnings when abnormalities are predicted.

[0667] "Users who input data" refers to individuals who provide information about their biological state.

[0668] "Information regarding biological status" refers to data related to health and physical condition, specifically information about symptoms such as headaches, fever, and fatigue.

[0669] "Structured data" refers to digital data that is organized according to a specific format or structure.

[0670] A "network for communication" refers to the infrastructure that enables the sending and receiving of information, and includes the Internet network and mobile networks.

[0671] "Analysis" is the process of examining data and finding meaning based on it.

[0672] "Predicting biological abnormalities" refers to predicting health problems based on collected data.

[0673] A "device that proposes countermeasures" is a device that provides specific countermeasures and advice based on predicted anomalies.

[0674] "Early warning" means issuing a warning quickly based on prediction results and prompting appropriate action.

[0675] The system of this invention begins with the user inputting their biological state information via a smartphone or smart glasses. The terminal structures the input information and transmits it to a server via a communication network. The server receives the structured data and performs analysis using a generative AI model. This generative AI model is trained on past medical literature and the latest medical data and has the ability to quickly predict the user's biological abnormalities.

[0676] If the server predicts a biological abnormality, it will formulate specific measures based on that information. These may include recommendations to consult with a health management professional or suggestions for improving daily life. For example, it might advise, "You are at risk of influenza, so we recommend getting enough rest and staying hydrated." Finally, the server sends this information to the terminal and notifies the user via smartphone or smart glasses. This allows the user to recognize health risks early and take appropriate action.

[0677] For example, if a user enters "headache" and "fatigue," the server will determine these to be potential symptoms of influenza and send a notification recommending immediate rest and consultation with a specialist. An example of a prompt message would be, "User's symptom input: fatigue. Predict possible biological abnormalities." This instruction would be sent to the generating AI model in this form. In this way, the present invention functions as a system that contributes to improving the safety and health of individual users and enables appropriate and rapid responses.

[0678] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0679] Step 1:

[0680] The user enters biological status information using the input interface of a smartphone or smart glasses. This information includes specific symptoms (e.g., "headache," "fatigue," etc.). The entered data is structured within the device.

[0681] Step 2:

[0682] The device sends structured data to the server via a communication network. During transmission, the data is properly formatted and encrypted to ensure secure transmission.

[0683] Step 3:

[0684] The server prepares to analyze the structured data received via the communication network. The data is then fed into a generative AI model as input data to predict biological abnormalities.

[0685] Step 4:

[0686] The generative AI model uses received symptom information to predict biological abnormalities. This model is trained on historical medical literature and the latest medical data, and predicts and outputs risk levels and likely diseases from the input data.

[0687] Step 5:

[0688] The server develops appropriate measures based on the biological abnormalities predicted by the generating AI model. This includes recommendations for further medical actions and suggestions for improvements in daily life. For example, if there is a risk of influenza, recommendations for seeing a doctor and advice on rest will be developed.

[0689] Step 6:

[0690] The server sends response data, including the formulated measures, to the terminal. The response data is configured to be presented to the user through the user interface.

[0691] Step 7:

[0692] The terminal displays the received response data to the user via a user interface. Based on the information presented, the user can decide on necessary health management actions.

[0693] 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.

[0694] This invention is a system that simultaneously acquires biological state information and emotions from a user and provides appropriate diagnosis and treatment. The system consists of a user terminal, a server, a communication network, and an emotion engine.

[0695] First, the user uses their device to input not only physical symptoms but also the emotions they are feeling. For example, the user is expected to input information such as "stomach ache" or "feeling stressed." The device then organizes this information as structured data and prepares to send it to the server via the communication network.

[0696] The server analyzes the received biological state and emotional information. It uses generative models to make biological predictions, and an emotion engine recognizes the user's emotions, utilizing the results in its analysis. The emotion engine recognizes the user's emotional state based on the analysis and tone of the words entered by the user.

[0697] The server analyzes emotions and biological states to predict biological abnormalities and determine specific actions to take. These actions may include prompt consultation with a medical professional and stress management in daily life. For example, if a user is experiencing stress, the server may suggest breathing exercises or relaxation techniques to help manage stress.

[0698] Finally, the server sends the determined information to the terminal. The terminal displays the received diagnostic results and treatment information on its user interface, making it easy for the user to understand. This allows the user to quickly obtain information about their condition and decide on their next course of action while also considering their mental health.

[0699] In this form, the present invention provides comprehensive medical support that takes into account not only the user's physical health but also their mental health, thereby enhancing the user's sense of security and comfort.

[0700] The following describes the processing flow.

[0701] Step 1:

[0702] The user launches the application on their device and inputs information about their biological state and emotions. During this process, the user enters descriptions such as "stomach ache" as a symptom and "feeling stressed" as an emotion into the form. The device then collects the entered data.

[0703] Step 2:

[0704] The device converts the collected biological state and emotional information into structured data in JSON format. The converted data is then prepared for efficient transmission over the communication network.

[0705] Step 3:

[0706] The device sends structured data to the server in the form of an HTTP POST request. Specify the appropriate endpoint to ensure the data reaches the server reliably. After transmission is complete, wait for a response from the server.

[0707] Step 4:

[0708] The server receives structured data sent from the terminal. It checks the content, syntax, and format of the received data for any problems and prepares it for the analysis process.

[0709] Step 5:

[0710] The server uses a generative model to analyze biological state information. Furthermore, it uses an emotion engine to analyze emotional information. The emotion engine detects "stress" from the user's description and performs analysis that takes this into account.

[0711] Step 6:

[0712] The server predicts the user's biological abnormalities based on the analysis results. At the same time, it also makes decisions on treatment that take emotional information into account. For example, in the case of stomach pain and stress, it recommends "seeing a doctor" along with "relaxation techniques for stress management."

[0713] Step 7:

[0714] The server compiles the final results into a JSON response and sends it to the terminal. This data includes the diagnostic results and recommended actions.

[0715] Step 8:

[0716] The terminal analyzes the received response data and displays it on the user interface. Users can immediately understand the analysis results and recommended actions, and use them to help plan their own health management.

[0717] (Example 2)

[0718] 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".

[0719] Traditionally, health management based on a user's biological state and emotions has generally relied on diagnoses that only consider physical symptoms. There has been a need for technology that simultaneously assesses emotional health and proposes appropriate treatment. A comprehensive approach is necessary because emotional changes can significantly impact physical health. However, systems that accurately analyze emotional information and combine it with actual health status for diagnosis are insufficient, and technology to address this problem is eagerly awaited.

[0720] 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.

[0721] In this invention, the server includes means for acquiring biological and emotional state information input by the user, means for predicting biological abnormalities and emotional states based on the analyzed data, and means for suggesting medical treatment and emotional support based on the predicted results. This makes it possible to take measures that consider not only the user's physical health but also their emotional health.

[0722] A "user" refers to an individual who provides information about their biological and emotional states to the system.

[0723] "Biological status information" refers to objective data regarding the user's physical health status.

[0724] "Emotional state information" refers to data that indicates the user's subjective emotional state.

[0725] "Structured data" refers to data that has been converted into a format suitable for subsequent analysis.

[0726] "Communication medium" refers to the technical elements used to send and receive data, and includes networks.

[0727] A "generative model" refers to a machine learning algorithm used for data analysis and prediction.

[0728] A "sentiment analysis engine" refers to software used to identify and analyze a user's emotional state.

[0729] "Medical treatment" refers to the treatments and countermeasures applied based on the user's physical health condition.

[0730] "Emotional support" refers to advice or methods for improving or maintaining a user's emotional state.

[0731] This invention is a system that analyzes a user's biological and emotional state information to provide appropriate medical treatment and emotional support. The system mainly consists of a terminal, a server, a communication medium, and an emotion analysis engine.

[0732] Users can input biometric and emotional status information at any time using their own devices. The device converts this information into structured data, securely encrypts it, and transmits it to the server via a communication medium. This allows users to easily record and send health information to the server, even from home.

[0733] The server uses specialized software to analyze the received structured data. It leverages generative AI models to perform data analysis that predicts future changes in biological states. This analysis involves referencing historical medical databases and conducting detailed pattern analysis. Meanwhile, the sentiment analysis engine processes emotional information entered by the user, identifying the user's emotional state based on text and tone.

[0734] For example, if a user inputs "I have a headache and I'm stressed," the server receives this information and uses a generative AI model to predict the cause of the headache, while simultaneously evaluating the level of stress using an emotion analysis engine. Based on the analysis results, the server can suggest consulting a medical professional and, at the same time, provide the user with relaxation techniques to alleviate stress.

[0735] A possible prompt message might be, "I currently have a stomach ache and am feeling stressed. What advice do you have for me?" This system aims to provide comprehensive support for the user's physical and mental health, offering prompt and accurate medical and emotional care.

[0736] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0737] Step 1:

[0738] Users input biological and emotional state information as text using a device. Examples of input include "headache" and "feeling stressed." This input data is converted into structured data by the device. Specifically, the conversion process organizes the input information into a key-value pair and packages it in JSON format.

[0739] Step 2:

[0740] The terminal encrypts the converted JSON-formatted structured data to ensure security before sending it to the server via the communication medium. The encryption process prevents data leakage and tampering. Once transmission is complete, the data is held on the server.

[0741] Step 3:

[0742] After decrypting the received structured data, the server begins data analysis. First, it uses a generative AI model to predict biological conditions. As a specific example of data analysis, it compares the causes of headaches with past medical data and performs a diagnosis of exclusion of abnormalities. The output is the predicted result of biological abnormalities.

[0743] Step 4:

[0744] The server uses an emotion analysis engine to analyze emotional state information. Specifically, it identifies emotions from the text entered by the user, recognizing, for example, "stress." The output obtained from this emotion analysis is the user's emotional state and its severity.

[0745] Step 5:

[0746] The server integrates the analysis results of the generated AI model with the results of the sentiment analysis engine to determine the appropriate course of action. Based on this integrated output, it may generate suggestions that include the need for medical intervention and methods of emotional support.

[0747] Step 6:

[0748] The server encrypts the integrated diagnostic results and recommendations and sends them to the terminal. The terminal decrypts the received data and displays it in a user interface in a format that is easy for the user to understand. This allows the user to confirm the specific actions that have been suggested.

[0749] (Application Example 2)

[0750] 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".

[0751] In modern society, the impact of emotions such as stress and anxiety, in addition to a user's biological state, on their health is gaining importance. However, there is a lack of systems that provide comprehensive health management that considers these factors simultaneously. Furthermore, while students and working individuals are required to identify and reduce stress, there are limited mechanisms that efficiently suggest solutions. This project aims to solve these problems.

[0752] 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.

[0753] In this invention, the server includes means for acquiring biological state information input by the user, means for recognizing and analyzing the user's emotions, and means for making suggestions to reduce the user's stress based on the recognized emotions. This enables comprehensive support that takes into account not only the user's physical health but also their mental health.

[0754] "Means for acquiring biological state information entered by the user" refers to a system that allows users to input information about their physical condition into an external device.

[0755] "Means of converting to structured data" refers to the process of organizing acquired biological state information into a format that is easy to analyze and transmit.

[0756] "Means of transmission via a communication network" refers to the function of transmitting converted data to a remote server or other device using a communication channel such as the internet.

[0757] "Means for receiving and analyzing transmitted structured data" refers to the process of taking in data obtained through a communication network and understanding and interpreting its contents.

[0758] A "means of predicting biological abnormalities" is a system that uses analyzed data to determine the likelihood of an abnormality occurring in a user's health condition.

[0759] "Means of suggesting treatments corresponding to biological abnormalities" refers to a function that notifies and guides the user on appropriate actions based on predicted abnormalities.

[0760] "Means for recognizing and analyzing user emotions" refers to the process of understanding the user's emotional state and using that information to perform a detailed analysis.

[0761] "Means of suggesting ways to reduce user stress" refers to a system that recommends methods and products for users to relax based on insights gained from emotion analysis.

[0762] The system realizing this invention has a program that acquires biological state information and emotional information using the user's smartphone or wearable device. The user's device provides an interface for inputting biological state (e.g., "stomach ache") and emotion (e.g., "feeling stressed"). The input data is converted into structured data using a morphological analysis library implemented in Python and sent to an AWS EC2 server via a communication network.

[0763] The server uses a TensorFlow generative model and Google Cloud's natural language processing API to analyze the received data. The generative model analyzes the biological state, while the Google Cloud API is responsible for recognizing and analyzing emotions. Through this combined data analysis, the server predicts the user's biological abnormalities and determines appropriate actions based on those conditions. It also generates specific suggestions for stress reduction (e.g., relaxation techniques and related products) based on the user's emotions.

[0764] Once the analysis is complete, the server sends the results back to the user's terminal. The terminal displays the diagnostic results and stress reduction suggestions through a user interface, making it easy for the user to understand the content.

[0765] For example, if a user inputs that they feel stressed while shopping, the system will suggest relaxation music or relaxation techniques they can try at home, based on weather, location, and past health data. An example of a prompt might be: "Design an app that suggests ways for users who feel stressed while shopping to relax later. Consider what emotions the user might input and propose a mechanism to suggest appropriate responses to them."

[0766] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0767] Step 1:

[0768] The user opens the app on their smartphone and enters information about their biological state and emotions (e.g., "headache," "anxiety"). The device temporarily stores the entered information. The entered data is saved on the device in text format.

[0769] Step 2:

[0770] The terminal uses a morphological analysis library implemented in Python to convert user input data into structured data. This process involves analyzing the user input text and extracting specified keywords and phrases. Once the structured data is generated, it is ready to be sent to the server.

[0771] Step 3:

[0772] Structured data is sent to an AWS EC2 server via the internet. The server receives the data and begins analyzing the user's biological state using a generative AI model. The model processes the structured data received as input and outputs predictions about the user's health.

[0773] Step 4:

[0774] Simultaneously, the server uses Google Cloud's natural language processing API to recognize and analyze emotional information. The server analyzes the user's emotional data as input, evaluating tone and emotional intensity. This analysis is then used to quantitatively assess the user's stress level.

[0775] Step 5:

[0776] The server combines predictions of the user's biological state with emotional analysis results to determine the appropriate course of action. It integrates the results of the generative AI model with evaluated emotional data to select the most suitable stress reduction method for the user. This may include specific relaxation techniques or product recommendations.

[0777] Step 6:

[0778] The decided proposal is then transmitted back to the user's terminal via the communication network. The terminal receives it and displays the results through the user interface. The user can review specific action plans and suggestions for stress reduction and choose the next action that best suits their situation.

[0779] 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.

[0780] 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.

[0781] 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 robot 414.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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."

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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 as being incorporated by reference.

[0800] The following is further disclosed regarding the embodiments described above.

[0801] (Claim 1)

[0802] A means of obtaining biological state information entered by the user,

[0803] A means of converting acquired biological state information into structured data,

[0804] A means for transmitting structured data over a communication network,

[0805] A means for receiving and analyzing transmitted structured data,

[0806] A means of predicting biological abnormalities based on analyzed data,

[0807] Means for suggesting treatments corresponding to predicted biological abnormalities,

[0808] A system that includes this.

[0809] (Claim 2)

[0810] The system according to claim 1, wherein the treatment corresponding to the aforementioned biological abnormality includes consulting with a medical professional and improving lifestyle habits.

[0811] (Claim 3)

[0812] The system according to claim 1, further comprising means for using a generative model for analyzing the aforementioned biological state information.

[0813] "Example 1"

[0814] (Claim 1)

[0815] A means for receiving biological state information entered by the user,

[0816] A means of converting received biological state information into data,

[0817] A means of transmitting the converted data via a communication environment,

[0818] A means of receiving transmitted data and preparing it for analysis,

[0819] A means of predicting biological abnormalities using a generative model based on data prepared for analysis,

[0820] A means of determining and presenting responses to predicted biological abnormalities,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, wherein the response to the aforementioned biological abnormality includes consultation with medical professionals and lifestyle improvement measures.

[0824] (Claim 3)

[0825] The system according to claim 1, further comprising means for using a learning model for analyzing the aforementioned state information.

[0826] "Application Example 1"

[0827] (Claim 1)

[0828] A device that collects information about the biological state from users who input data,

[0829] A device that converts collected information into structured data,

[0830] A device that transmits the converted structured data over a network for communication,

[0831] A device that receives and analyzes transmitted structured data,

[0832] A device for predicting biological abnormalities derived from the analysis,

[0833] A device that proposes measures in response to predicted anomalies,

[0834] A device for issuing early warnings,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, wherein the measures based on the aforementioned biological abnormalities include consulting with a health management specialist and improving the maintenance and management of daily life.

[0838] (Claim 3)

[0839] The system according to claim 1, further comprising a device for applying a generative AI model to the analysis of the aforementioned biological state information.

[0840] "Example 2 of combining an emotion engine"

[0841] (Claim 1)

[0842] A means of acquiring biological and emotional state information entered by the user,

[0843] A means of converting acquired information into structured data,

[0844] A means of transmitting this structured data via a communication medium,

[0845] A means for receiving and analyzing transmitted structured data,

[0846] A means of predicting biological abnormalities and emotional states based on analyzed data,

[0847] Based on the predicted outcome, means of presenting medical treatment and emotional support,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, wherein the actions taken in response to the predicted outcome include consultation with a medical professional and stress management.

[0851] (Claim 3)

[0852] The system according to claim 1, further comprising means for using a generative model or an emotion analysis engine for analyzing the aforementioned information.

[0853] "Application example 2 of combining emotional engines"

[0854] (Claim 1)

[0855] A means of obtaining biological state information entered by the user,

[0856] A means of converting acquired biological state information into structured data,

[0857] A means for transmitting structured data over a communication network,

[0858] A means for receiving and analyzing transmitted structured data,

[0859] A means of predicting biological abnormalities based on analyzed data,

[0860] Means for suggesting treatments corresponding to predicted biological abnormalities,

[0861] A means of recognizing and analyzing user emotions,

[0862] A means of making suggestions to reduce user stress based on recognized emotions,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, wherein the treatment corresponding to the aforementioned biological abnormality includes consultation with a medical professional, improvement of lifestyle, and suggestions for stress reduction.

[0866] (Claim 3)

[0867] The system according to claim 1, further comprising means for using a generative model for analyzing the aforementioned biological state information and emotions. [Explanation of Symbols]

[0868] 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 obtaining biological state information entered by the user, A means of converting acquired biological state information into structured data, A means for transmitting structured data over a communication network, A means for receiving and analyzing transmitted structured data, A means of predicting biological abnormalities based on analyzed data, Means for suggesting treatments corresponding to predicted biological abnormalities, A system that includes this.

2. The system according to claim 1, wherein the treatment corresponding to the aforementioned biological abnormality includes consulting with a medical professional and improving lifestyle habits.

3. The system according to claim 1, further comprising means for using a generative model for analyzing the aforementioned biological state information.

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