system

Abstract

Provide a system. A means for a user to input symptoms, diagnosis results, and treatment methods; means for transmitting the input data to a server; A means for the server to search for similar cases in the database; A means of inputting search results into a generative AI model for analysis; means for transmitting the generated second opinion to a user; means for displaying the transmitted results on a user terminal; A system including:

Description

[Technical Field]

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

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

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

[0004] When patients and their families are deciding on treatment methods and policies for serious illnesses, especially cancer, they typically seek a second opinion. However, the time required for new tests and consultations, as well as the effort required to find a medical institution that can provide a second opinion, can be a significant burden. This can lead to missed treatment opportunities and the risk of the disease progressing. The purpose of this invention is to reduce these time and procedural burdens and quickly and effectively provide patients with optimal treatment methods and policies. [Means for solving the problem]

[0005] The present invention solves the above problem by providing a system including: a means for a user to input symptoms, diagnosis results, and treatment methods; a means for transmitting the input data to a server; a means for the server to search for similar cases in a database; a means for inputting the search results into a generative AI model for analysis; a means for transmitting the generated second opinion to the user; and a means for displaying the transmitted results on a user terminal. Furthermore, the means for searching for similar cases in the database executes a query based on the user's symptoms and diagnosis results, and by presenting a new treatment method or policy based on the generated second opinion, it is possible to provide patients with information quickly and accurately.

[0006] "User" refers to patients, their families, or healthcare providers who use the system.

[0007] "Symptom" refers to a physical or psychological abnormality experienced by a patient.

[0008] "Diagnostic result" refers to the name of a disease or an assessment of health status obtained as a result of tests or examinations conducted by a medical institution or doctor.

[0009] "Treatment method" refers to the specific treatment measures recommended by a medical institution or doctor to improve a disease or abnormal condition.

[0010] "Terminal" refers to a device such as a computer or smartphone through which a user inputs information and receives results.

[0011] "Server" refers to a centralized computer that receives and processes data sent by users.

[0012] A "database" refers to a collection of information that stores past diagnostic results and treatment cases.

[0013] "Similar cases" refer to past cases in which the symptoms and diagnostic results are similar to the current patient's data.

[0014] "Searching" refers to locating information in a database based on specific criteria.

[0015] A "generative AI model" refers to an artificial intelligence model that uses machine learning techniques to generate new information and recommendations based on input data.

[0016] "Analyzing" refers to the process of breaking down data and extracting information to make sense of it.

[0017] A "second opinion" refers to a second opinion or recommendation based on current diagnostic and treatment methods.

[0018] "Transmit" refers to the transfer of data from one system or device to another.

[0019] "Display" refers to outputting the received information on the terminal screen in a form that can be confirmed by the user.

[0020] A "query" refers to a query command for searching or updating information in a database. [Brief explanation of the drawings]

[0021] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0022] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0024] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0025] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0026] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0027] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0028] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0029] [First embodiment]

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

[0031] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0032] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0033] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0034] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0035] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0036] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0038] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0039] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0042] The present invention is a system that provides a second opinion based on the symptoms, diagnosis, and treatment method input by the user. The program processing of this system is explained in natural language below. Specific examples are also provided for further explanation.

[0043] System Overview

[0044] 1. User Input

[0045] Users use a device (such as a PC or smartphone) to enter their symptoms, diagnosis results, and treatment methods provided by their doctor. By entering the necessary information into a form on the device, the data is organized in an orderly manner.

[0046] 2. Data Transmission

[0047] The entered data is sent from the device to a server, which receives it, stores it in a database, and prepares it for analysis.

[0048] 3. Database Search

[0049] The server searches the database for past diagnostic and treatment cases, identifies cases with similar symptoms and diagnostic results based on the user's input data, and collects information about these cases.

[0050] 4. Analysis of AI models

[0051] Similar cases obtained as search results are input into a generative AI model, which analyzes this data and generates a second opinion to suggest the most appropriate treatment method and policy for the user's symptoms and diagnosis.

[0052] 5. Sending the results

[0053] The generated second opinion is then sent back to the user's device from the server, with important data encrypted to protect the user's privacy.

[0054] 6. Displaying the results

[0055] The terminal displays the second opinion received from the server in an appropriate format, allowing the user to quickly check the second opinion at home or on the go.

[0056] Specific examples

[0057] Suppose a patient has symptoms such as "chest pain and cough," is diagnosed by a doctor with "suspected lung cancer," and is recommended chemotherapy as a treatment. The patient enters this information using a device. The device then sends the information to a server, which then searches for similar cases in its database. For example, if a past case with similar symptoms and diagnosis is found, the generative AI model can use this to generate a second opinion stating that "radiation therapy is likely to be effective." This result is then sent from the server to the patient's device and displayed to the patient on the device.

[0058] As described above, the present invention is a system that helps users quickly obtain a second opinion from home or on the go and consider the most appropriate treatment method, significantly reducing the time burden and procedural difficulties.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The user enters their symptoms, diagnosis, and the proposed treatment into the terminal. Specifically, the user details symptoms such as "chest pain, cough" in the terminal form, and also enters the doctor's diagnosis of "suspected lung cancer" and the treatment recommended, "chemotherapy."

[0062] Step 2:

[0063] The terminal sends the data entered by the user to the server. Specifically, it converts the data into packets in JSON format and sends them to the server using a secure communication protocol (e.g., HTTPS).

[0064] Step 3:

[0065] The server analyzes the received data and generates a query to the database. Specifically, it executes an SQL query to search for similar past diagnoses and treatment cases based on the user's symptoms and diagnosis.

[0066] Step 4:

[0067] The server retrieves similar cases from the database, analyzes the records returned as a result of the query, and lists the cases with the highest similarity.

[0068] Step 5:

[0069] The server inputs the acquired similar cases into the generative AI model for analysis. Specifically, it converts the data of the similar cases into an input format for the AI ​​model and applies it to the model to generate new treatment methods and policies.

[0070] Step 6:

[0071] The server then sends the generated second opinion to the user's device. Specifically, it encodes the treatment method and policy data obtained from the AI ​​model into JSON format and sends it back to the device using a secure communication protocol.

[0072] Step 7:

[0073] The user's device analyzes the received second opinion and displays it in an appropriate format. Specifically, it parses the received JSON format data and displays it on the screen as text and graphics that are easy for the user to understand. The user can review this and consider treatment methods and policies.

[0074] Through the above processing steps, the user can quickly obtain a second opinion and obtain information for selecting the most appropriate treatment method or policy.

[0075] Example 1

[0076] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0077] In modern medical practice, it is important to gather opinions from multiple medical professionals to assess whether a patient's diagnosis or proposed treatment is accurate and optimal. However, obtaining a second opinion often requires significant time and cost. Furthermore, if a doctor with the appropriate expertise is not available nearby, patients have difficulty obtaining such an option. This raises concerns that patients may not receive optimal medical care and miss opportunities to maximize the effectiveness of treatment. Furthermore, there is a lack of methods for quickly obtaining a second opinion while protecting privacy.

[0078] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0079] In this invention, the server includes means for a user to input symptoms, diagnosis results, and treatment methods, means for transmitting the input data to the server, means for the server to search for similar cases in a database, means for inputting the search results to a generative AI model for analysis, means for transmitting the generated second opinion to the user, means for displaying the transmitted results on a user terminal, means for providing the input data to the generative AI model as a prompt sentence, and means for encrypting and transmitting the generated second opinion, thereby enabling a user to quickly and safely obtain a second opinion and consider the optimal treatment method.

[0080] "User" refers to an individual who utilizes the system to input their symptoms, diagnosis, and treatment options and receive a second opinion.

[0081] "Server" refers to a computer system that receives data sent by a user, searches a database to input it into a generative AI model, and sends the generated second opinion to the user.

[0082] "Terminal" refers to the device used by the user to input symptoms, diagnosis results, and treatment methods and transmit them to the server. Examples include computers and smartphones.

[0083] A "database" refers to a collection of information that includes past diagnostic results and treatment cases.

[0084] "Generative AI model" refers to artificial intelligence technology that generates second opinions based on data entered by the user.

[0085] A "prompt" refers to a formalized instruction to be input to a generative AI model.

[0086] "Encoding" refers to the process of converting data into a particular format to make it easier to transmit or store.

[0087] "Encryption" refers to converting data format using a specific algorithm to prevent the data from being read by third parties.

[0088] "Second opinion" refers to an alternative diagnosis or treatment suggested by a generative AI model based on data entered by the user.

[0089] The present invention is a system in which a user inputs their own symptoms and diagnosis results, and a generative AI model provides a second opinion based on that data. This system is mainly composed of a terminal, a server, and a generative AI model.

[0090] User Input

[0091] Users use devices such as computers or smartphones to enter their symptoms, diagnosis results, and treatment options provided by their doctors. By entering information into dedicated web forms or applications displayed on the device, data is organized in an orderly manner. For example, a user can enter "chest pain, cough" in the "symptoms" field, "suspected lung cancer" in the "diagnosis" field, and "chemotherapy" in the "treatment" field.

[0092] Data transmission

[0093] The entered data is sent from the terminal to the server. The terminal encodes the data entered in the form and sends it to the server using an HTTP POST request. The server receives this data and stores it in a database.

[0094] Database search

[0095] The server searches the database for past diagnoses and treatment cases. Based on the data entered by the user, it issues queries to identify similar cases. For example, it searches for cases with similar symptoms and diagnoses and collects this information.

[0096] AI model analysis

[0097] The server inputs similar cases obtained as search results into a generative AI model. The generative AI model is pre-trained and suggests the optimal treatment method based on the user's symptoms and diagnosis results. The server generates a prompt sentence to analyze the data with the generative AI model. The following is an example of a prompt sentence:

[0098] User symptoms: Chest pain, cough.

[0099] Diagnosis: suspected lung cancer.

[0100] Suggested treatment: chemotherapy.

[0101] Please suggest other possible treatment options based on similar past cases.

[0102] The generative AI model generates a second opinion based on this prompt and offers new treatment methods and strategies.

[0103] Sending the results

[0104] The generated second opinion is sent from the server to the user's device. The server encrypts the generated data to protect the user's privacy. The encrypted data is then sent back to the device via HTTP or HTTPS.

[0105] Displaying the results

[0106] The terminal displays the second opinion received from the server in an appropriate format. The terminal decrypts the received data and formats it in a format that is easy for the user to understand. For example, the terminal may display the results in a text view or a dialog box so that the user can quickly review them.

[0107] This system allows users to easily obtain a second opinion from home or on the go and consider the best treatment method, significantly reducing the time burden and procedural difficulties and increasing the chances of receiving more effective treatment.

[0108] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0109] Step 1:

[0110] The user uses the device to input their symptoms, diagnosis, and treatment options provided by the doctor. Specifically, the user enters data for each item into a dedicated web form or application displayed on the device. For example, the user might enter "chest pain, cough" in the "symptoms" field, "suspected lung cancer" in the "diagnosis" field, and "chemotherapy" in the "treatment" field. This input data becomes the input for the next step.

[0111] Step 2:

[0112] The terminal sends the input data to the server. The terminal encodes the input data into JSON or XML format and sends it to the server using an HTTP POST request. The sent data is entered for server execution. This process passes the user input data to the server.

[0113] Step 3:

[0114] The server stores the received data in a database. The server uses a database management system to store the input data in an appropriate format. As a result, the data of symptoms, diagnosis results, and treatment methods entered by the user are stored in the database.

[0115] Step 4:

[0116] The server searches the database for past diagnosis results and treatment cases. It issues a query based on the user's input data. For example, it searches for cases containing "chest pain," "cough," "suspected lung cancer," and "chemotherapy." The server executes this query and extracts data on similar cases. This similar case data becomes the input for the next step.

[0117] Step 5:

[0118] The server generates a prompt to input the extracted similar case data into the generative AI model. Specifically, the server uses a text processing library to construct the prompt. For example, it generates a prompt such as, "User's symptoms: chest pain, cough. Diagnosis: suspected lung cancer. Proposed treatment: chemotherapy. Please suggest other possible optimal treatment methods based on similar past cases." This prompt becomes the input for the generative AI model.

[0119] Step 6:

[0120] The server inputs a prompt sentence into the generative AI model and performs analysis. The server loads a pre-trained generative AI model and inputs a prompt sentence. The generative AI model performs analysis and generates a second opinion. The analysis result becomes the input for the next step.

[0121] Step 7:

[0122] The server sends the generated second opinion to the user's device. Specifically, the server encrypts the generated data and sends it to the device via HTTP or HTTPS. Encryption is a necessary process to protect the user's privacy.

[0123] Step 8:

[0124] The terminal displays the second opinion received from the server in an appropriate format. The terminal decrypts the received data and formats it in a format that is easy for the user to understand. For example, the terminal can display the results in a text view or a dialog box so that the user can quickly check them. At this stage, the user can review the second opinion and consider the best treatment method.

[0125] (Application example 1)

[0126] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0127] Currently, it is difficult for patients to obtain a second opinion quickly and accurately in medical settings. In particular, when patients wish to consult directly at a pharmacy or medical institution about diagnostic results or treatment options, they need to be able to receive reliable information immediately. They also need a way to efficiently input patients' symptoms and diagnostic results and suggest appropriate treatment options based on that information. There is a need for a system that can solve these issues and improve convenience and reliability in medical settings.

[0128] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0129] In this invention, the server includes: a means for a user to input symptoms, diagnosis results, and treatment methods; a means for transmitting the input data to the server; a means for the server to search for similar cases in a database; a means for inputting the search results into a generative AI model for analysis; a means for transmitting the generated second opinion to the user; a means for displaying the transmitted results on a user terminal; a means for accepting voice input using a smart device; a means for transmitting the voice-input data to a cloud server; and a means for displaying the generated second opinion on the smart device. This allows patients in medical settings to easily input symptoms using a smart device and obtain analysis results using the cloud server in real time. This enables second opinions to be obtained more quickly and efficiently.

[0130] A "user terminal" is an electronic device used by a user, such as a computer, smartphone, or smart device.

[0131] A "server" is a remote computer system that receives and processes data sent from user devices, performs database searches, and analyzes AI models.

[0132] A "database" is an information system that systematically stores medical information such as past diagnostic results and treatment cases.

[0133] A "generative AI model" is an artificial intelligence model used to generate new treatment methods or recommended policies based on data analysis.

[0134] A "second opinion" refers to an additional diagnosis or treatment suggestion provided by a different medical professional or device than the initial consultation.

[0135] A "smart device" is a pair of smart glasses, a smartphone, or other portable electronic device with advanced functionality.

[0136] "Voice input" is a method in which a user inputs data using voice, and the data is processed as text data using voice recognition technology.

[0137] A "cloud server" is a remote server accessible via the Internet that stores and processes large amounts of data.

[0138] "Real-time" refers to data processing and information provision occurring almost immediately.

[0139] An embodiment of the present invention will be described. The present invention is a system that provides a second opinion based on symptoms, diagnosis results, and treatment methods input by a user. In this system, a user terminal and a server communicate via the Internet, and the server processes medical information and performs analysis using a generative AI model.

[0140] System Configuration

[0141] User terminal

[0142] User terminals include smartphones, PCs, and smart devices. These terminals provide an interface for users to input symptoms, diagnosis results, and treatment methods. In addition, smart devices (such as smart glasses) can collect input data through voice input functions.

[0143] server

[0144] The server receives data sent from the user device, searches the database, and analyzes it using the generative AI model. The server is hosted in a cloud environment (such as AWS (registered trademark) or Azure (registered trademark)) and has the following main functions:

[0145] 1. Data reception and storage: Receives symptoms, diagnosis results, and treatment methods sent from the user device and stores them in a cloud database.

[0146] 2. Database search: Search existing diagnostic and treatment cases in a database to identify similar cases.

[0147] 3. AI analysis: The search results are input into a generative AI model to generate a second opinion that suggests the best treatment method based on the user's symptoms and diagnosis.

[0148] 4. Sending the results: The generated second opinion is sent to the user terminal.

[0149] Processing explanation

[0150] After receiving the data sent from the user device, the server uses a search engine such as ElasticSearch® to search for similar cases in the database. The searched cases are input into a generative AI model (e.g., GPT-4®) that processes them using a programming language such as Python. The generative AI model analyzes new treatment methods or strategies and generates them as second opinions. The generated second opinions are sent to the user device in encrypted form and displayed on the screen of the user device or the display of the smart glasses.

[0151] Specific examples

[0152] Let's say a patient visits a pharmacy, reports symptoms such as "chest pain and cough," and is diagnosed with "suspected lung cancer." The suggested treatment is "chemotherapy is recommended." The patient, following the pharmacist's instructions, voice-inputs this information through the smart glasses. The smart glasses send this data to a cloud server, which searches for similar cases in its database. The generative AI model then generates a second opinion that "radiation therapy is likely to be effective," and displays the result in real time on the smart glasses.

[0153] Prompt Sentence Examples

[0154] Below are some example prompts to use when inputting a generative AI model:

[0155] "The patient has symptoms such as 'chest pain and cough' and has been diagnosed with 'suspected lung cancer'. The proposed treatment is 'chemotherapy is recommended'. Based on this information, please provide any other possible treatment options or opinions."

[0156] The present invention thereby enables patients to efficiently and quickly obtain second opinions at medical sites, thereby improving the quality of medical care.

[0157] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0158] Step 1:

[0159] The user inputs symptoms, diagnosis results, and treatment methods. Input is done through voice or text input using a smartphone or smart device (such as smart glasses). Specifically, by utilizing the voice input function of the smart glasses, the user verbally describes their symptoms, which are then converted into text data using voice recognition technology. This input data becomes the input for the next step.

[0160] Step 2:

[0161] The device sends the entered data to a cloud server. This process is securely carried out over the Internet. The cloud server first receives the data and temporarily stores it. This data is stored as text information about symptoms, diagnosis results, and treatment methods.

[0162] Step 3:

[0163] The server performs a database search. Based on the data stored on the cloud server, it uses a search engine such as Elasticsearch to search for similar cases in the database. Specifically, it uses the symptoms and diagnosis results entered by the user as a query to search for related cases with past diagnoses and treatment methods. The search results are used as input data for the next step.

[0164] Step 4:

[0165] The searched similar cases are input into a generative AI model. The cloud server preprocesses the search results using a programming language such as Python and inputs them into a generative AI model (e.g., GPT-4). The AI ​​model then analyzes the search results and generates a second opinion that suggests the optimal treatment method based on the user's symptoms and diagnosis.

[0166] Step 5:

[0167] The generated second opinion is sent to the user terminal. The server encrypts the generated second opinion data and sends it to the user terminal using a secure communication method to protect the user's privacy. This output data is sent as text data of the final second opinion.

[0168] Step 6:

[0169] The results are displayed on the user's device. The received second opinion is displayed on the smart glasses display or smartphone screen. The user can consider additional treatment methods and policies based on the displayed second opinion. Specifically, the results are displayed visually using the smart device's HUD (head-up display) API.

[0170] These steps allow users to quickly and accurately obtain second opinions, improving the quality of diagnosis and treatment in medical settings.

[0171] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0172] The present invention is a system that provides a second opinion by combining an emotion engine based on the symptoms, diagnosis, and treatment methods entered by the user. The program processing of this system is explained in natural language below. Specific examples are also provided for further explanation.

[0173] System Overview

[0174] 1. User Input

[0175] Users input their symptoms, diagnosis results, and treatment options provided by their doctor into the device. The emotion engine then recognizes emotions from the user's input data, and this data is also collected at the same time. For example, if a user enters symptoms such as "chest pain, cough" into the form, and enters a diagnosis of "suspected lung cancer" and a treatment option of "chemotherapy recommended," the emotion engine will recognize emotions such as stress and anxiety from the user's text input.

[0176] 2. Data Transmission

[0177] The device sends the data entered by the user and the emotion data recognized by the emotion engine to the server in JSON format using a secure communication protocol (e.g., HTTPS).

[0178] 3. Database Search

[0179] The server analyzes the received data and generates a query to the database, specifically, to search for similar past diagnoses and treatment cases based on the user's symptoms and diagnosis.

[0180] 4. Analysis of AI models

[0181] The server inputs similar cases obtained as search results into a generative AI model and performs analysis to generate new treatment methods and policies. In addition, recognized emotional data is also provided to the AI ​​model, which adjusts accordingly.

[0182] 5. Sending the results

[0183] The server then sends the generated second opinion back to the user's device. At this time, the second opinion reflects adjustments based on the emotional data. For example, if a user is feeling very anxious, it will highlight treatment options that allow them to consult without rushing.

[0184] 6. Displaying the results

[0185] The device displays the second opinion received from the server in an appropriate format, allowing users to quickly check the second opinion whether they are at home or on the go. Furthermore, the information is presented in a way that takes the user's emotions into consideration, making it easier for them to calmly consider treatment methods and policies.

[0186] Specific examples

[0187] Suppose a patient has symptoms such as "chest pain and cough," is diagnosed by a doctor with "suspected lung cancer," and is recommended chemotherapy as a treatment. When the patient enters this information using a device, the emotion engine recognizes "anxiety" and "stress" from the patient's input. The device sends this information to a server, which searches for similar cases in a database. For example, if a past case with similar symptoms and diagnosis is found, the generative AI model can use this to generate a second opinion that "radiation therapy is likely to be effective." This result is sent from the server to the patient's device, and when it is displayed to the patient on the device, a message to alleviate the patient's anxiety is added.

[0188] As described above, the present invention is a system that allows users to quickly obtain a second opinion from home or on the go, and provides information that takes the user's feelings into consideration, helping them calmly consider the optimal treatment method and policy. This system significantly reduces the time burden and procedural difficulties, and also reduces the psychological burden.

[0189] The processing flow will be explained below.

[0190] Step 1:

[0191] The user enters their symptoms, diagnosis, and proposed treatment into the terminal. Specifically, the user details symptoms such as "chest pain, cough" in the terminal form, and also enters the doctor's diagnosis of "suspected lung cancer" and the treatment recommended, "chemotherapy."

[0192] Step 2:

[0193] The emotion engine recognizes emotions from the user's input data. Specifically, it analyzes the text entered by the user and identifies emotions such as "anxiety" and "stress." This emotion data is also collected along with the user's input data.

[0194] Step 3:

[0195] The device sends the data entered by the user and the emotion data recognized by the emotion engine to the server in JSON format using a secure communication protocol (e.g., HTTPS).

[0196] Step 4:

[0197] The server analyzes the received data and generates a query to the database. Specifically, it creates and executes an SQL query to search for similar past diagnostic results and treatment cases based on the user's symptoms and diagnostic results.

[0198] Step 5:

[0199] The server retrieves similar cases from the database, analyzes the records returned as a result of the query, and lists the cases with the highest similarity.

[0200] Step 6:

[0201] The server inputs the acquired data of similar cases into the generative AI model for analysis. Specifically, it converts the data of similar cases into an input format for the AI ​​model and applies it to the model to generate new treatment methods and policies.

[0202] Step 7:

[0203] The server also provides emotional data from the emotion engine to the AI ​​model, allowing it to adapt the generated second opinion to the user's emotions. For example, if the user is feeling highly anxious, the second opinion will be adjusted to emphasize treatment options that encourage more patient action.

[0204] Step 8:

[0205] The server then sends the generated second opinion to the user's device. Specifically, the server encodes the second opinion in JSON format and sends it to the device using a secure communication protocol.

[0206] Step 9:

[0207] The device analyzes the second opinion received from the server and displays it in an appropriate format. Specifically, it parses the received JSON format data and displays it on the screen as text and graphics that are easy for the user to understand. The user can review this and consider treatment methods and policies.

[0208] Through the above processing steps, users can quickly obtain a second opinion and obtain information to select the most appropriate treatment method and policy. In addition, by using the emotion engine, it is possible to provide information that takes into account the user's psychological state, reducing stress and anxiety during the treatment decision-making process.

[0209] Example 2

[0210] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0211] This invention relates to a system that simultaneously collects and analyzes emotional data when a user inputs their symptoms, diagnosis, and treatment options, and uses this data to provide an optimal second opinion. Conventional systems present diagnosis and treatment options without taking the user's emotional state into consideration, resulting in problems such as inappropriate communication of information or inability to alleviate the user's anxiety. Furthermore, when there are multiple diagnosis and treatment options, comparing and examining each piece of information takes a significant amount of time, placing a significant burden on the user.

[0212] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0213] In this invention, the server includes: means for a user to input symptoms, diagnosis results, and treatment methods; means for analyzing the input data and recognizing emotional data; means for transmitting the analyzed data and emotional data to the server; means for the server to search for similar cases in a database; means for inputting the search results and emotional data into a generative AI model for analysis; means for transmitting the generated second opinion to the user; and means for displaying the transmitted results and adjustments based on the emotions on a user terminal. This allows for the provision of a prompt and appropriate second opinion that takes into consideration the user's emotional state, enabling the user to calmly consider treatment methods and policies.

[0214] "User" means an individual or corporation that uses this system.

[0215] "Symptoms" refer to changes or abnormalities in the user's physical condition.

[0216] "Diagnosis results" refers to medical condition assessments and diagnostic information made by medical professionals based on the user's symptoms.

[0217] "Treatment method" refers to the treatment measures or methods suggested by a medical professional based on the diagnosis results.

[0218] "Emotion data" is data that represents the emotional state recognized from the user's input content.

[0219] A "terminal" is a device (e.g., a PC, smartphone, or tablet) that allows a user to input symptoms, diagnosis results, and treatment methods.

[0220] A "server" is a central processing unit that receives and analyzes data sent by users, manages the database, and executes generative AI models.

[0221] A "database" is an information system that stores and manages past diagnostic results and treatment cases.

[0222] A "query" refers to a search request made to a database.

[0223] A "generative AI model" is an artificial intelligence mechanism that generates new treatment methods and policies based on input data.

[0224] "Second opinion" refers to a separate professional opinion or judgment provided in addition to the diagnosis or treatment method obtained by the user.

[0225] The "JSON format" is a lightweight data exchange format for structuring and representing data.

[0226] "Secure protocol" refers to a communication protocol for securely sending and receiving data, and includes HTTPS.

[0227] "Emotion-based adjustment" refers to modifying and adjusting the generated results to suit the user based on recognized emotional data.

[0228] The present invention provides a system for providing a second opinion based on symptoms, diagnosis results, and treatment methods input by a user, and also combining emotion data. This system includes the following elements:

[0229] The user enters their symptoms, diagnosis, and treatment options provided by the doctor into the device. In the device's form, the user writes down symptoms such as "chest pain, cough," and enters the diagnosis of "suspected lung cancer" and the treatment option of "chemotherapy recommended." In addition, an emotion engine works in the background to recognize emotion data such as "anxiety" and "stress" from the user's input. This emotion data is generated based on the user's text input.

[0230] The device sends the data entered by the user and emotion data to the server. The data is structured in JSON format and transmitted via a secure communication protocol (e.g., HTTPS), which prevents the risk of data eavesdropping or tampering.

[0231] The server analyzes the received data and generates a query for the database. Specifically, it executes a query to search for similar past diagnosis results and treatment cases based on the user's symptoms and diagnosis results. For example, a search using the conditions "chest pain," "cough," and "suspected lung cancer" is performed to retrieve similar past cases.

[0232] The server then inputs the similar cases obtained as search results into a generative AI model (such as GPT-4). The generative AI model then uses this data to perform analysis to generate new treatment methods and policies. Emotional data is also provided to the AI ​​model, and the results are adjusted accordingly. For example, a second opinion is generated that "radiation therapy is likely to be effective," and detailed explanations and success stories are added for users who are feeling stressed.

[0233] The generated second opinion is then sent back to the user's device from the server. At this time, the second opinion reflects adjustments based on the emotional data. For example, if a user is feeling anxious, a message emphasizing treatment options and detailed explanations will be added so that the user can consult calmly.

[0234] The device receives the second opinion from the server and displays it to the user in an appropriate format. Users can quickly check the second opinion at home or on the go. Furthermore, the information is provided in a way that takes emotions into consideration, making it easier to calmly consider treatment methods and policies.

[0235] As a specific example, consider the case where a patient has symptoms such as "chest pain and cough" and is diagnosed by a doctor with "suspected lung cancer" and told that "chemotherapy is recommended." The patient enters this information using a device, and the emotion engine recognizes "anxiety" and "stress" from the input. The device sends this information to a server, which searches for similar cases in a database. For example, if a past case of a patient with similar symptoms is found, the generative AI model may use this to generate a second opinion stating that "radiotherapy is likely to be effective." This result is then sent back to the patient's device, and when displayed to the patient on the device, a message such as "There are many successful cases of radiation therapy, so don't worry" is added.

[0236] An example of a prompt sentence could be in the following format:

[0237] "Generate second opinions based on user-entered symptoms, diagnoses, and treatment options. Include appropriate treatment options and messages, taking into account user sentiment data."

[0238] As described above, the present invention is a system that allows users to quickly obtain a second opinion from home or on the go, and helps them calmly consider the optimal treatment method and policy by providing information that takes the user's emotions into consideration. This system significantly reduces the time burden and procedures, and also reduces the psychological burden.

[0239] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0240] Program processing steps

[0241] Step 1: User enters symptoms, diagnosis, and treatment

[0242] The user enters their symptoms, diagnosis, and treatment method into a dedicated form on the device. The input data includes "chest pain, cough" (symptoms), "suspected lung cancer" (diagnosis), and "chemotherapy recommended" (treatment method), and enters it into the device's form. The device collects this data. Once the input data is ready, the user clicks the "Submit" button.

[0243] Step 2: Analyzing input data and recognizing emotions

[0244] The device analyzes the data entered by the user and recognizes emotional data using an emotion engine. The input data includes symptoms, diagnosis, and treatment methods. The emotion engine uses a text analysis algorithm to extract emotions (e.g., anxiety, stress) contained in the user's input data. The output at this point is symptoms, diagnosis, treatment, and recognized emotional data.

[0245] Step 3: Sending data

[0246] The device sends the analyzed data (symptoms, diagnosis results, treatment methods) and recognized emotion data in JSON format to the server. The HTTPS protocol is used for transmission, ensuring secure data transfer. The input data is the information entered by the user and emotion data, and this information is sent to the server as output.

[0247] Step 4: Receiving and analyzing data

[0248] The server receives data sent from the device. The received data includes symptoms, diagnosis results, treatment methods, and emotion data. The server analyzes this data and generates a query for database search. The received data is the input, and the query for database search is generated as the output.

[0249] Step 5: Database Search

[0250] The server uses the generated query to search for similar cases in the database. The query includes conditions such as "chest pain," "cough," and "suspected lung cancer." The database returns past diagnoses and treatment cases that match these conditions. The search is performed based on the input query, and a list of similar cases is obtained as the output.

[0251] Step 6: Input and analysis into the AI ​​model

[0252] The server inputs similar cases obtained as search results into a generative AI model. The generative AI model performs analysis based on this data to generate new treatment methods and policies. Emotional data is also provided to the AI ​​model, which makes adjustments accordingly. Search result data and emotional data are given as input, and new treatment methods and policies are generated as output.

[0253] Step 7: Generate a second opinion

[0254] The server receives the second opinion generated by the generative AI model and adjusts it based on emotional data. For example, if a second opinion is generated stating that "radiation therapy is likely to be effective," the server adds a message such as "If you feel anxious, we recommend that you seek professional counseling." The generated second opinion is the input, and the adjusted second opinion is the output.

[0255] Step 8: Submit a second opinion

[0256] The server then sends the adjusted second opinion back to the user's device, again using the HTTPS protocol. The adjusted second opinion is given as input and sent to the device as output.

[0257] Step 9: View the results

[0258] The terminal displays the second opinion received from the server to the user in an appropriate format. The user can quickly check this second opinion at home or on the go. The display is done in a way that takes emotions into consideration, for example, by adding a message such as "Don't rush, let's hear other opinions." The second opinion received is the input, and it is displayed to the user as the output.

[0259] (Application example 2)

[0260] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0261] Conventional systems that provide second opinions present diagnosis results and treatment methods without considering the user's feelings, which increases the user's psychological burden. In addition, when users enter medical information in a physical store, a quick and appropriate response is required, but there was no system that could efficiently do this.

[0262] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0263] In this invention, the server includes means for a user to input symptoms, diagnosis results, and treatment methods, means for transmitting the input data and emotion data to the server, means for the server to search for similar cases in a database, means for inputting the search results into a generative AI model for analysis, means for transmitting the generated second opinion to the user, means for displaying the transmitted results on a user terminal, means for adjusting the message based on the emotion data, and means for the user to input information using a tablet terminal or smartphone. This makes it possible to provide second opinions that take the user's emotions into consideration, thereby realizing fast and efficient service provision in physical stores.

[0264] "User" refers to a person who utilizes the system to input symptoms, diagnoses, and treatments.

[0265] "Symptoms" refers to information that indicates the health condition or physical abnormality that the user is experiencing.

[0266] "Diagnosis results" refers to information on evaluations and judgments regarding the user's health condition provided by a doctor or medical institution.

[0267] "Treatment method" refers to the means or measures proposed by a medical institution to improve the user's health condition.

[0268] "Emotion data" refers to information that indicates a subjective emotional state extracted from the user's input.

[0269] "Server" refers to a central system that receives input data from users and processes it, such as by analyzing, searching, or generating.

[0270] "Database" refers to data storage within a system that stores information such as past diagnostic results and treatment cases.

[0271] A "generative AI model" refers to an artificial intelligence algorithm that generates new diagnostic results and treatment methods based on past cases and input data.

[0272] "Tablet device" refers to a portable electronic device that users use to input information such as symptoms, diagnosis, and treatment.

[0273] The term "smartphone" refers to a portable communication terminal device that is used by users to input information, similar to a tablet terminal.

[0274] "Search results" refer to new treatment methods or second opinions analyzed by the generative AI model based on similar cases in the database.

[0275] A "secure communication protocol" refers to a communication method for securely transmitting user input data to a server.

[0276] "User terminal" refers to an electronic device used by a user to receive and display the results of a second opinion.

[0277] The "means for adjusting a message" refers to a method for providing information in a form appropriate for a user based on emotional data.

[0278] This invention is a system that allows users to input their symptoms, diagnosis results, and treatment methods using a portable electronic device such as a tablet or smartphone, and provides second opinions based on that information. This system analyzes emotional data and proposes new treatment methods that take the user's psychological state into consideration.

[0279] Hardware and Software Configuration

[0280] Hardware

[0281] Tablets and smartphones: Handheld electronic devices that allow users to input information.

[0282] Server: A central system that receives user input data and performs analysis and database lookup.

[0283] software

[0284] Emotion engine: An algorithm that analyzes emotional data from user input.

[0285] Database: Data storage where similar cases and treatment cases are kept.

[0286] Generative AI model: An artificial intelligence algorithm that generates new treatment methods based on input data and past cases.

[0287] Communication protocol: A protocol for secure communication, such as HTTPS.

[0288] Program processing

[0289] User Input

[0290] Users use a tablet or smartphone to input their symptoms, diagnosis results, and treatment options. The input data is analyzed in real time by an emotion engine, and emotional data such as "anxiety" and "stress" is collected.

[0291] Sending data to the server

[0292] The device sends the data entered by the user and the emotion data together in JSON format to the server. This communication is performed using the HTTPS protocol, ensuring data security.

[0293] Database Search and Analysis

[0294] The server performs a database search based on the received data. Specifically, it executes queries based on the user's symptoms and diagnosis results to extract similar past cases.

[0295] Generative AI model for generating treatment methods

[0296] The server inputs the search results obtained from the database into the generative AI model, which analyzes and generates new treatment methods and policies. At this time, emotional data is also provided to the AI ​​model, so it can make adjustments according to the user's psychological state.

[0297] Sending and displaying results

[0298] The generated second opinion is then sent back to the user's device, which then displays the results in an appropriate format. For example, a user who is feeling anxious might be presented with a relaxing message or be given a treatment option to consider without rushing.

[0299] Specific examples

[0300] Consider a case where a patient has symptoms such as "chest pain" and "cough" and is diagnosed with "suspected lung cancer," and is advised to undergo chemotherapy. The patient enters this information using a tablet device. The emotion engine recognizes "anxiety" and "stress" from the patient's input. The device sends this information to a server, which searches for similar cases and uses a generative AI model to generate a second opinion that "radiation therapy is likely to be effective." This result is sent from the server to the patient's device and displayed along with a message to alleviate the patient's anxiety.

[0301] Prompt Sentence Examples

[0302] Symptoms: chest pain, cough

[0303] Diagnosis: Suspected lung cancer

[0304] Suggested treatment: Chemotherapy is recommended

[0305] Emotional data: anxiety, stress

[0306] Generate new treatment options and advice based on previous similar cases and treatment outcomes.

[0307] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0308] Program processing flow

[0309] Step 1:

[0310] Enter user information

[0311] Users use a tablet or smartphone to input their symptoms, diagnosis results, and treatment methods.

[0312] Input: Symptoms, diagnosis, treatment.

[0313] Output: The input data.

[0314] This data is analyzed in real time by an emotion engine to generate the user's emotion data (e.g., anxiety, stress).

[0315] Input: Data entered.

[0316] Output: Emotion data.

[0317] How it works: A user enters their information into the application's input form, which is then analyzed by the emotion engine to generate emotion data.

[0318] Step 2:

[0319] Sending data to the server

[0320] The device sends the data entered by the user and emotion data to the server in JSON format.

[0321] Input: Input data and emotion data (JSON format).

[0322] Output: The data sent to the server.

[0323] How it works: The device uses the HTTPS protocol to send data to the server over a secure connection.

[0324] Step 3:

[0325] Database search

[0326] The server analyzes the received data and generates queries against a database.

[0327] Input: User data and emotion data sent to the server.

[0328] Output: Similar cases retrieved from the database.

[0329] How it works: Based on the symptoms and diagnosis provided by the user, the server queries the database to search for similar past cases.

[0330] Step 4:

[0331] Analysis using generative AI models

[0332] The server inputs similar cases obtained as search results into a generative AI model to generate new treatment methods and policies.

[0333] Input: Similar cases retrieved from the database.

[0334] Output: Generated new treatment methods and second opinions.

[0335] How it works: Using a generative AI model, new treatment methods and policies are generated based on input similar cases and emotional data. For example, a new treatment option such as "radiation therapy is likely to be effective" is generated.

[0336] Step 5:

[0337] Sending the results

[0338] The server transmits the generated second opinion to the user's terminal.

[0339] Input: Generated second opinions and tailored messages.

[0340] Output: Data sent to the user's device.

[0341] How it works: The information generated by the server is sent back to the user's device in JSON format, along with a message based on the emotion data.

[0342] Step 6:

[0343] Displaying the results

[0344] The terminal displays the second opinion received from the server in an appropriate format.

[0345] Input: The second opinion and message sent by the server.

[0346] Output: Information displayed to the user.

[0347] How it works: The user can view the generated second opinion on their device. For anxious users, the message includes tips on how to relax and deal with the situation calmly.

[0348] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0349] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0350] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0351] [Second embodiment]

[0352] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0353] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0354] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0355] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0356] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0357] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0358] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0359] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0360] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0361] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0362] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0363] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0364] The present invention is a system that provides a second opinion based on the symptoms, diagnosis, and treatment method input by the user. The program processing of this system is explained in natural language below. Specific examples are also provided for further explanation.

[0365] System Overview

[0366] 1. User Input

[0367] Users use a device (such as a PC or smartphone) to enter their symptoms, diagnosis results, and treatment methods provided by their doctor. By entering the necessary information into a form on the device, the data is organized in an orderly manner.

[0368] 2. Data Transmission

[0369] The entered data is sent from the device to a server, which receives it, stores it in a database, and prepares it for analysis.

[0370] 3. Database Search

[0371] The server searches the database for past diagnostic and treatment cases, identifies cases with similar symptoms and diagnostic results based on the user's input data, and collects information about these cases.

[0372] 4. Analysis of AI models

[0373] Similar cases obtained as search results are input into a generative AI model, which analyzes this data and generates a second opinion to suggest the most appropriate treatment method and policy for the user's symptoms and diagnosis.

[0374] 5. Sending the results

[0375] The generated second opinion is then sent back to the user's device from the server, with important data encrypted to protect the user's privacy.

[0376] 6. Displaying the results

[0377] The terminal displays the second opinion received from the server in an appropriate format, allowing the user to quickly check the second opinion at home or on the go.

[0378] Specific examples

[0379] Suppose a patient has symptoms such as "chest pain and cough," is diagnosed by a doctor with "suspected lung cancer," and is recommended chemotherapy as a treatment. The patient enters this information using a device. The device then sends the information to a server, which then searches for similar cases in its database. For example, if a past case with similar symptoms and diagnosis is found, the generative AI model can use this to generate a second opinion stating that "radiation therapy is likely to be effective." This result is then sent from the server to the patient's device and displayed to the patient on the device.

[0380] As described above, the present invention is a system that helps users quickly obtain a second opinion from home or on the go and consider the most appropriate treatment method, significantly reducing the time burden and procedural difficulties.

[0381] The processing flow will be explained below.

[0382] Step 1:

[0383] The user enters their symptoms, diagnosis, and the proposed treatment into the terminal. Specifically, the user details symptoms such as "chest pain, cough" in the terminal form, and also enters the doctor's diagnosis of "suspected lung cancer" and the treatment recommended, "chemotherapy."

[0384] Step 2:

[0385] The terminal sends the data entered by the user to the server. Specifically, it converts the data into packets in JSON format and sends them to the server using a secure communication protocol (e.g., HTTPS).

[0386] Step 3:

[0387] The server analyzes the received data and generates a query to the database. Specifically, it executes an SQL query to search for similar past diagnoses and treatment cases based on the user's symptoms and diagnosis.

[0388] Step 4:

[0389] The server retrieves similar cases from the database, analyzes the records returned as a result of the query, and lists the cases with the highest similarity.

[0390] Step 5:

[0391] The server inputs the acquired similar cases into the generative AI model for analysis. Specifically, it converts the data of the similar cases into an input format for the AI ​​model and applies it to the model to generate new treatment methods and policies.

[0392] Step 6:

[0393] The server then sends the generated second opinion to the user's device. Specifically, it encodes the treatment method and policy data obtained from the AI ​​model into JSON format and sends it back to the device using a secure communication protocol.

[0394] Step 7:

[0395] The user's device analyzes the received second opinion and displays it in an appropriate format. Specifically, it parses the received JSON format data and displays it on the screen as text and graphics that are easy for the user to understand. The user can review this and consider treatment methods and policies.

[0396] Through the above processing steps, the user can quickly obtain a second opinion and obtain information for selecting the most appropriate treatment method or policy.

[0397] Example 1

[0398] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0399] In modern medical practice, it is important to gather opinions from multiple medical professionals to assess whether a patient's diagnosis or proposed treatment is accurate and optimal. However, obtaining a second opinion often requires significant time and cost. Furthermore, if a doctor with the appropriate expertise is not available nearby, patients have difficulty obtaining such an option. This raises concerns that patients may not receive optimal medical care and miss opportunities to maximize the effectiveness of treatment. Furthermore, there is a lack of methods for quickly obtaining a second opinion while protecting privacy.

[0400] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0401] In this invention, the server includes means for a user to input symptoms, diagnosis results, and treatment methods, means for transmitting the input data to the server, means for the server to search for similar cases in a database, means for inputting the search results to a generative AI model for analysis, means for transmitting the generated second opinion to the user, means for displaying the transmitted results on a user terminal, means for providing the input data to the generative AI model as a prompt sentence, and means for encrypting and transmitting the generated second opinion, thereby enabling a user to quickly and safely obtain a second opinion and consider the optimal treatment method.

[0402] "User" refers to an individual who utilizes the system to input their symptoms, diagnosis, and treatment options and receive a second opinion.

[0403] "Server" refers to a computer system that receives data sent by a user, searches a database to input it into a generative AI model, and sends the generated second opinion to the user.

[0404] "Terminal" refers to the device used by the user to input symptoms, diagnosis results, and treatment methods and transmit them to the server. Examples include computers and smartphones.

[0405] A "database" refers to a collection of information that includes past diagnostic results and treatment cases.

[0406] "Generative AI model" refers to artificial intelligence technology that generates second opinions based on data entered by the user.

[0407] A "prompt" refers to a formalized instruction to be input to a generative AI model.

[0408] "Encoding" refers to the process of converting data into a particular format to make it easier to transmit or store.

[0409] "Encryption" refers to converting data format using a specific algorithm to prevent the data from being read by third parties.

[0410] "Second opinion" refers to an alternative diagnosis or treatment suggested by a generative AI model based on data entered by the user.

[0411] The present invention is a system in which a user inputs their own symptoms and diagnosis results, and a generative AI model provides a second opinion based on that data. This system is mainly composed of a terminal, a server, and a generative AI model.

[0412] User Input

[0413] Users use devices such as computers or smartphones to enter their symptoms, diagnosis results, and treatment options provided by their doctors. By entering information into dedicated web forms or applications displayed on the device, data is organized in an orderly manner. For example, a user can enter "chest pain, cough" in the "symptoms" field, "suspected lung cancer" in the "diagnosis" field, and "chemotherapy" in the "treatment" field.

[0414] Data transmission

[0415] The entered data is sent from the terminal to the server. The terminal encodes the data entered in the form and sends it to the server using an HTTP POST request. The server receives this data and stores it in a database.

[0416] Database search

[0417] The server searches the database for past diagnoses and treatment cases. Based on the data entered by the user, it issues queries to identify similar cases. For example, it searches for cases with similar symptoms and diagnoses and collects this information.

[0418] AI model analysis

[0419] The server inputs similar cases obtained as search results into a generative AI model. The generative AI model is pre-trained and suggests the optimal treatment method based on the user's symptoms and diagnosis results. The server generates a prompt sentence to analyze the data with the generative AI model. The following is an example of a prompt sentence:

[0420] User symptoms: Chest pain, cough.

[0421] Diagnosis: suspected lung cancer.

[0422] Suggested treatment: chemotherapy.

[0423] Please suggest other possible treatment options based on similar past cases.

[0424] The generative AI model generates a second opinion based on this prompt and offers new treatment methods and strategies.

[0425] Sending the results

[0426] The generated second opinion is sent from the server to the user's device. The server encrypts the generated data to protect the user's privacy. The encrypted data is then sent back to the device via HTTP or HTTPS.

[0427] Displaying the results

[0428] The terminal displays the second opinion received from the server in an appropriate format. The terminal decrypts the received data and formats it in a format that is easy for the user to understand. For example, the terminal may display the results in a text view or a dialog box so that the user can quickly review them.

[0429] This system allows users to easily obtain a second opinion from home or on the go and consider the best treatment method, significantly reducing the time burden and procedural difficulties and increasing the chances of receiving more effective treatment.

[0430] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0431] Step 1:

[0432] The user uses the device to input their symptoms, diagnosis, and treatment options provided by the doctor. Specifically, the user enters data for each item into a dedicated web form or application displayed on the device. For example, the user might enter "chest pain, cough" in the "symptoms" field, "suspected lung cancer" in the "diagnosis" field, and "chemotherapy" in the "treatment" field. This input data becomes the input for the next step.

[0433] Step 2:

[0434] The terminal sends the input data to the server. The terminal encodes the input data into JSON or XML format and sends it to the server using an HTTP POST request. The sent data is entered for server execution. This process passes the user input data to the server.

[0435] Step 3:

[0436] The server stores the received data in a database. The server uses a database management system to store the input data in an appropriate format. As a result, the data of symptoms, diagnosis results, and treatment methods entered by the user are stored in the database.

[0437] Step 4:

[0438] The server searches the database for past diagnosis results and treatment cases. It issues a query based on the user's input data. For example, it searches for cases containing "chest pain," "cough," "suspected lung cancer," and "chemotherapy." The server executes this query and extracts data on similar cases. This similar case data becomes the input for the next step.

[0439] Step 5:

[0440] The server generates a prompt to input the extracted similar case data into the generative AI model. Specifically, the server uses a text processing library to construct the prompt. For example, it generates a prompt such as, "User's symptoms: chest pain, cough. Diagnosis: suspected lung cancer. Proposed treatment: chemotherapy. Please suggest other possible optimal treatment methods based on similar past cases." This prompt becomes the input for the generative AI model.

[0441] Step 6:

[0442] The server inputs a prompt sentence into the generative AI model and performs analysis. The server loads a pre-trained generative AI model and inputs a prompt sentence. The generative AI model performs analysis and generates a second opinion. The analysis result becomes the input for the next step.

[0443] Step 7:

[0444] The server sends the generated second opinion to the user's device. Specifically, the server encrypts the generated data and sends it to the device via HTTP or HTTPS. Encryption is a necessary process to protect the user's privacy.

[0445] Step 8:

[0446] The terminal displays the second opinion received from the server in an appropriate format. The terminal decrypts the received data and formats it in a format that is easy for the user to understand. For example, the terminal can display the results in a text view or a dialog box so that the user can quickly check them. At this stage, the user can review the second opinion and consider the best treatment method.

[0447] (Application example 1)

[0448] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0449] Currently, it is difficult for patients to obtain a second opinion quickly and accurately in medical settings. In particular, when patients wish to consult directly at a pharmacy or medical institution about diagnostic results or treatment options, they need to be able to receive reliable information immediately. They also need a way to efficiently input patients' symptoms and diagnostic results and suggest appropriate treatment options based on that information. There is a need for a system that can solve these issues and improve convenience and reliability in medical settings.

[0450] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0451] In this invention, the server includes: a means for a user to input symptoms, diagnosis results, and treatment methods; a means for transmitting the input data to the server; a means for the server to search for similar cases in a database; a means for inputting the search results into a generative AI model for analysis; a means for transmitting the generated second opinion to the user; a means for displaying the transmitted results on a user terminal; a means for accepting voice input using a smart device; a means for transmitting the voice-input data to a cloud server; and a means for displaying the generated second opinion on the smart device. This allows patients in medical settings to easily input symptoms using a smart device and obtain analysis results using the cloud server in real time. This enables second opinions to be obtained more quickly and efficiently.

[0452] A "user terminal" is an electronic device used by a user, such as a computer, smartphone, or smart device.

[0453] A "server" is a remote computer system that receives and processes data sent from user devices, performs database searches, and analyzes AI models.

[0454] A "database" is an information system that systematically stores medical information such as past diagnostic results and treatment cases.

[0455] A "generative AI model" is an artificial intelligence model used to generate new treatment methods or recommended policies based on data analysis.

[0456] A "second opinion" refers to an additional diagnosis or treatment suggestion provided by a different medical professional or device than the initial consultation.

[0457] A "smart device" is a pair of smart glasses, a smartphone, or other portable electronic device with advanced functionality.

[0458] "Voice input" is a method in which a user inputs data using voice, and the data is processed as text data using voice recognition technology.

[0459] A "cloud server" is a remote server accessible via the Internet that stores and processes large amounts of data.

[0460] "Real-time" refers to data processing and information provision occurring almost immediately.

[0461] An embodiment of the present invention will be described. The present invention is a system that provides a second opinion based on symptoms, diagnosis results, and treatment methods input by a user. In this system, a user terminal and a server communicate via the Internet, and the server processes medical information and performs analysis using a generative AI model.

[0462] System Configuration

[0463] User terminal

[0464] User terminals include smartphones, PCs, and smart devices. These terminals provide an interface for users to input symptoms, diagnosis results, and treatment methods. In addition, smart devices (such as smart glasses) can collect input data through voice input functions.

[0465] server

[0466] The server receives data sent from the user device, performs database search and analysis using the generative AI model. The server is hosted in a cloud environment (such as AWS or Azure) and has the following main functions:

[0467] 1. Data reception and storage: Receives symptoms, diagnosis results, and treatment methods sent from the user device and stores them in a cloud database.

[0468] 2. Database search: Search existing diagnostic and treatment cases in a database to identify similar cases.

[0469] 3. AI analysis: The search results are input into a generative AI model to generate a second opinion that suggests the best treatment method based on the user's symptoms and diagnosis.

[0470] 4. Sending the results: The generated second opinion is sent to the user terminal.

[0471] Processing explanation

[0472] After receiving the data sent from the user device, the server uses a search engine such as Elasticsearch to search for similar cases in the database. The searched cases are input into a generative AI model (e.g., GPT-4) that processes them using a programming language such as Python. The generative AI model analyzes new treatment methods or strategies and generates them as second opinions. The generated second opinions are sent to the user device in encrypted form and displayed on the user device screen or the smart glasses display.

[0473] Specific examples

[0474] Let's say a patient visits a pharmacy, reports symptoms such as "chest pain and cough," and is diagnosed with "suspected lung cancer." The suggested treatment is "chemotherapy is recommended." The patient, following the pharmacist's instructions, voice-inputs this information through the smart glasses. The smart glasses send this data to a cloud server, which searches for similar cases in its database. The generative AI model then generates a second opinion that "radiation therapy is likely to be effective," and displays the result in real time on the smart glasses.

[0475] Prompt Sentence Examples

[0476] Below are some example prompts to use when inputting a generative AI model:

[0477] "The patient has symptoms such as 'chest pain and cough' and has been diagnosed with 'suspected lung cancer'. The proposed treatment is 'chemotherapy is recommended'. Based on this information, please provide any other possible treatment options or opinions."

[0478] The present invention thereby enables patients to efficiently and quickly obtain second opinions at medical sites, thereby improving the quality of medical care.

[0479] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0480] Step 1:

[0481] The user inputs symptoms, diagnosis results, and treatment methods. Input is done through voice or text input using a smartphone or smart device (such as smart glasses). Specifically, by utilizing the voice input function of the smart glasses, the user verbally describes their symptoms, which are then converted into text data using voice recognition technology. This input data becomes the input for the next step.

[0482] Step 2:

[0483] The device sends the entered data to a cloud server. This process is securely carried out over the Internet. The cloud server first receives the data and temporarily stores it. This data is stored as text information about symptoms, diagnosis results, and treatment methods.

[0484] Step 3:

[0485] The server performs a database search. Based on the data stored on the cloud server, it uses a search engine such as Elasticsearch to search for similar cases in the database. Specifically, it uses the symptoms and diagnosis results entered by the user as a query to search for related cases with past diagnoses and treatment methods. The search results are used as input data for the next step.

[0486] Step 4:

[0487] The searched similar cases are input into a generative AI model. The cloud server preprocesses the search results using a programming language such as Python and inputs them into a generative AI model (e.g., GPT-4). The AI ​​model then analyzes the search results and generates a second opinion that suggests the optimal treatment method based on the user's symptoms and diagnosis.

[0488] Step 5:

[0489] The generated second opinion is sent to the user terminal. The server encrypts the generated second opinion data and sends it to the user terminal using a secure communication method to protect the user's privacy. This output data is sent as text data of the final second opinion.

[0490] Step 6:

[0491] The results are displayed on the user's device. The received second opinion is displayed on the smart glasses display or smartphone screen. The user can consider additional treatment methods and policies based on the displayed second opinion. Specifically, the results are displayed visually using the smart device's HUD (head-up display) API.

[0492] These steps allow users to quickly and accurately obtain second opinions, improving the quality of diagnosis and treatment in medical settings.

[0493] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0494] The present invention is a system that provides a second opinion by combining an emotion engine based on the symptoms, diagnosis, and treatment methods entered by the user. The program processing of this system is explained in natural language below. Specific examples are also provided for further explanation.

[0495] System Overview

[0496] 1. User Input

[0497] Users input their symptoms, diagnosis results, and treatment options provided by their doctor into the device. The emotion engine then recognizes emotions from the user's input data, and this data is also collected at the same time. For example, if a user enters symptoms such as "chest pain, cough" into the form, and enters a diagnosis of "suspected lung cancer" and a treatment option of "chemotherapy recommended," the emotion engine will recognize emotions such as stress and anxiety from the user's text input.

[0498] 2. Data Transmission

[0499] The device sends the data entered by the user and the emotion data recognized by the emotion engine to the server in JSON format using a secure communication protocol (e.g., HTTPS).

[0500] 3. Database Search

[0501] The server analyzes the received data and generates a query to the database, specifically, to search for similar past diagnoses and treatment cases based on the user's symptoms and diagnosis.

[0502] 4. Analysis of AI models

[0503] The server inputs similar cases obtained as search results into a generative AI model and performs analysis to generate new treatment methods and policies. In addition, recognized emotional data is also provided to the AI ​​model, which adjusts accordingly.

[0504] 5. Sending the results

[0505] The server then sends the generated second opinion back to the user's device. At this time, the second opinion reflects adjustments based on the emotional data. For example, if a user is feeling very anxious, it will highlight treatment options that allow them to consult without rushing.

[0506] 6. Displaying the results

[0507] The device displays the second opinion received from the server in an appropriate format, allowing users to quickly check the second opinion whether they are at home or on the go. Furthermore, the information is presented in a way that takes the user's emotions into consideration, making it easier for them to calmly consider treatment methods and policies.

[0508] Specific examples

[0509] Suppose a patient has symptoms such as "chest pain and cough," is diagnosed by a doctor with "suspected lung cancer," and is recommended chemotherapy as a treatment. When the patient enters this information using a device, the emotion engine recognizes "anxiety" and "stress" from the patient's input. The device sends this information to a server, which searches for similar cases in a database. For example, if a past case with similar symptoms and diagnosis is found, the generative AI model can use this to generate a second opinion that "radiation therapy is likely to be effective." This result is sent from the server to the patient's device, and when it is displayed to the patient on the device, a message to alleviate the patient's anxiety is added.

[0510] As described above, the present invention is a system that allows users to quickly obtain a second opinion from home or on the go, and provides information that takes the user's feelings into consideration, helping them calmly consider the optimal treatment method and policy. This system significantly reduces the time burden and procedural difficulties, and also reduces the psychological burden.

[0511] The processing flow will be explained below.

[0512] Step 1:

[0513] The user enters their symptoms, diagnosis, and proposed treatment into the terminal. Specifically, the user details symptoms such as "chest pain, cough" in the terminal form, and also enters the doctor's diagnosis of "suspected lung cancer" and the treatment recommended, "chemotherapy."

[0514] Step 2:

[0515] The emotion engine recognizes emotions from the user's input data. Specifically, it analyzes the text entered by the user and identifies emotions such as "anxiety" and "stress." This emotion data is also collected along with the user's input data.

[0516] Step 3:

[0517] The device sends the data entered by the user and the emotion data recognized by the emotion engine to the server in JSON format using a secure communication protocol (e.g., HTTPS).

[0518] Step 4:

[0519] The server analyzes the received data and generates a query to the database. Specifically, it creates and executes an SQL query to search for similar past diagnostic results and treatment cases based on the user's symptoms and diagnostic results.

[0520] Step 5:

[0521] The server retrieves similar cases from the database, analyzes the records returned as a result of the query, and lists the cases with the highest similarity.

[0522] Step 6:

[0523] The server inputs the acquired data of similar cases into the generative AI model for analysis. Specifically, it converts the data of similar cases into an input format for the AI ​​model and applies it to the model to generate new treatment methods and policies.

[0524] Step 7:

[0525] The server also provides emotional data from the emotion engine to the AI ​​model, allowing it to adapt the generated second opinion to the user's emotions. For example, if the user is feeling highly anxious, the second opinion will be adjusted to emphasize treatment options that encourage more patient action.

[0526] Step 8:

[0527] The server then sends the generated second opinion to the user's device. Specifically, the server encodes the second opinion in JSON format and sends it to the device using a secure communication protocol.

[0528] Step 9:

[0529] The device analyzes the second opinion received from the server and displays it in an appropriate format. Specifically, it parses the received JSON format data and displays it on the screen as text and graphics that are easy for the user to understand. The user can review this and consider treatment methods and policies.

[0530] Through the above processing steps, users can quickly obtain a second opinion and obtain information to select the most appropriate treatment method and policy. In addition, by using the emotion engine, it is possible to provide information that takes into account the user's psychological state, reducing stress and anxiety during the treatment decision-making process.

[0531] Example 2

[0532] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0533] This invention relates to a system that simultaneously collects and analyzes emotional data when a user inputs their symptoms, diagnosis, and treatment options, and uses this data to provide an optimal second opinion. Conventional systems present diagnosis and treatment options without taking the user's emotional state into consideration, resulting in problems such as inappropriate communication of information or inability to alleviate the user's anxiety. Furthermore, when there are multiple diagnosis and treatment options, comparing and examining each piece of information takes a significant amount of time, placing a significant burden on the user.

[0534] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0535] In this invention, the server includes: means for a user to input symptoms, diagnosis results, and treatment methods; means for analyzing the input data and recognizing emotional data; means for transmitting the analyzed data and emotional data to the server; means for the server to search for similar cases in a database; means for inputting the search results and emotional data into a generative AI model for analysis; means for transmitting the generated second opinion to the user; and means for displaying the transmitted results and adjustments based on the emotions on a user terminal. This allows for the provision of a prompt and appropriate second opinion that takes into consideration the user's emotional state, enabling the user to calmly consider treatment methods and policies.

[0536] "User" means an individual or corporation that uses this system.

[0537] "Symptoms" refer to changes or abnormalities in the user's physical condition.

[0538] "Diagnosis results" refers to medical condition assessments and diagnostic information made by medical professionals based on the user's symptoms.

[0539] "Treatment method" refers to the treatment measures or methods suggested by a medical professional based on the diagnosis results.

[0540] "Emotion data" is data that represents the emotional state recognized from the user's input content.

[0541] A "terminal" is a device (e.g., a PC, smartphone, or tablet) that allows a user to input symptoms, diagnosis results, and treatment methods.

[0542] A "server" is a central processing unit that receives and analyzes data sent by users, manages the database, and executes generative AI models.

[0543] A "database" is an information system that stores and manages past diagnostic results and treatment cases.

[0544] A "query" refers to a search request made to a database.

[0545] A "generative AI model" is an artificial intelligence mechanism that generates new treatment methods and policies based on input data.

[0546] "Second opinion" refers to a separate professional opinion or judgment provided in addition to the diagnosis or treatment method obtained by the user.

[0547] The "JSON format" is a lightweight data exchange format for structuring and representing data.

[0548] "Secure protocol" refers to a communication protocol for securely sending and receiving data, and includes HTTPS.

[0549] "Emotion-based adjustment" refers to modifying and adjusting the generated results to suit the user based on recognized emotional data.

[0550] The present invention provides a system for providing a second opinion based on symptoms, diagnosis results, and treatment methods input by a user, and also combining emotion data. This system includes the following elements:

[0551] The user enters their symptoms, diagnosis, and treatment options provided by the doctor into the device. In the device's form, the user writes down symptoms such as "chest pain, cough," and enters the diagnosis of "suspected lung cancer" and the treatment option of "chemotherapy recommended." In addition, an emotion engine works in the background to recognize emotion data such as "anxiety" and "stress" from the user's input. This emotion data is generated based on the user's text input.

[0552] The device sends the data entered by the user and emotion data to the server. The data is structured in JSON format and transmitted via a secure communication protocol (e.g., HTTPS), which prevents the risk of data eavesdropping or tampering.

[0553] The server analyzes the received data and generates a query for the database. Specifically, it executes a query to search for similar past diagnosis results and treatment cases based on the user's symptoms and diagnosis results. For example, a search using the conditions "chest pain," "cough," and "suspected lung cancer" is performed to retrieve similar past cases.

[0554] The server then inputs the similar cases obtained as search results into a generative AI model (such as GPT-4). The generative AI model then uses this data to perform analysis to generate new treatment methods and policies. Emotional data is also provided to the AI ​​model, and the results are adjusted accordingly. For example, a second opinion is generated that "radiation therapy is likely to be effective," and detailed explanations and success stories are added for users who are feeling stressed.

[0555] The generated second opinion is then sent back to the user's device from the server. At this time, the second opinion reflects adjustments based on the emotional data. For example, if a user is feeling anxious, a message emphasizing treatment options and detailed explanations will be added so that the user can consult calmly.

[0556] The device receives the second opinion from the server and displays it to the user in an appropriate format. Users can quickly check the second opinion at home or on the go. Furthermore, the information is provided in a way that takes emotions into consideration, making it easier to calmly consider treatment methods and policies.

[0557] As a specific example, consider the case where a patient has symptoms such as "chest pain and cough" and is diagnosed by a doctor with "suspected lung cancer" and told that "chemotherapy is recommended." The patient enters this information using a device, and the emotion engine recognizes "anxiety" and "stress" from the input. The device sends this information to a server, which searches for similar cases in a database. For example, if a past case of a patient with similar symptoms is found, the generative AI model may use this to generate a second opinion stating that "radiotherapy is likely to be effective." This result is then sent back to the patient's device, and when displayed to the patient on the device, a message such as "There are many successful cases of radiation therapy, so don't worry" is added.

[0558] An example of a prompt sentence could be in the following format:

[0559] "Generate second opinions based on user-entered symptoms, diagnoses, and treatment options. Include appropriate treatment options and messages, taking into account user sentiment data."

[0560] As described above, the present invention is a system that allows users to quickly obtain a second opinion from home or on the go, and helps them calmly consider the optimal treatment method and policy by providing information that takes the user's emotions into consideration. This system significantly reduces the time burden and procedures, and also reduces the psychological burden.

[0561] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0562] Program processing steps

[0563] Step 1: User enters symptoms, diagnosis, and treatment

[0564] The user enters their symptoms, diagnosis, and treatment method into a dedicated form on the device. The input data includes "chest pain, cough" (symptoms), "suspected lung cancer" (diagnosis), and "chemotherapy recommended" (treatment method), and enters it into the device's form. The device collects this data. Once the input data is ready, the user clicks the "Submit" button.

[0565] Step 2: Analyzing input data and recognizing emotions

[0566] The device analyzes the data entered by the user and recognizes emotional data using an emotion engine. The input data includes symptoms, diagnosis, and treatment methods. The emotion engine uses a text analysis algorithm to extract emotions (e.g., anxiety, stress) contained in the user's input data. The output at this point is symptoms, diagnosis, treatment, and recognized emotional data.

[0567] Step 3: Sending data

[0568] The device sends the analyzed data (symptoms, diagnosis results, treatment methods) and recognized emotion data in JSON format to the server. The HTTPS protocol is used for transmission, ensuring secure data transfer. The input data is the information entered by the user and emotion data, and this information is sent to the server as output.

[0569] Step 4: Receiving and analyzing data

[0570] The server receives data sent from the device. The received data includes symptoms, diagnosis results, treatment methods, and emotion data. The server analyzes this data and generates a query for database search. The received data is the input, and the query for database search is generated as the output.

[0571] Step 5: Database Search

[0572] The server uses the generated query to search for similar cases in the database. The query includes conditions such as "chest pain," "cough," and "suspected lung cancer." The database returns past diagnoses and treatment cases that match these conditions. The search is performed based on the input query, and a list of similar cases is obtained as the output.

[0573] Step 6: Input and analysis into the AI ​​model

[0574] The server inputs similar cases obtained as search results into a generative AI model. The generative AI model performs analysis based on this data to generate new treatment methods and policies. Emotional data is also provided to the AI ​​model, which makes adjustments accordingly. Search result data and emotional data are given as input, and new treatment methods and policies are generated as output.

[0575] Step 7: Generate a second opinion

[0576] The server receives the second opinion generated by the generative AI model and adjusts it based on emotional data. For example, if a second opinion is generated stating that "radiation therapy is likely to be effective," the server adds a message such as "If you feel anxious, we recommend that you seek professional counseling." The generated second opinion is the input, and the adjusted second opinion is the output.

[0577] Step 8: Submit a second opinion

[0578] The server then sends the adjusted second opinion back to the user's device, again using the HTTPS protocol. The adjusted second opinion is given as input and sent to the device as output.

[0579] Step 9: View the results

[0580] The terminal displays the second opinion received from the server to the user in an appropriate format. The user can quickly check this second opinion at home or on the go. The display is done in a way that takes emotions into consideration, for example, by adding a message such as "Don't rush, let's hear other opinions." The second opinion received is the input, and it is displayed to the user as the output.

[0581] (Application example 2)

[0582] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0583] Conventional systems that provide second opinions present diagnosis results and treatment methods without considering the user's feelings, which increases the user's psychological burden. In addition, when users enter medical information in a physical store, a quick and appropriate response is required, but there was no system that could efficiently do this.

[0584] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0585] In this invention, the server includes means for a user to input symptoms, diagnosis results, and treatment methods, means for transmitting the input data and emotion data to the server, means for the server to search for similar cases in a database, means for inputting the search results into a generative AI model for analysis, means for transmitting the generated second opinion to the user, means for displaying the transmitted results on a user terminal, means for adjusting the message based on the emotion data, and means for the user to input information using a tablet terminal or smartphone. This makes it possible to provide second opinions that take the user's emotions into consideration, thereby realizing fast and efficient service provision in physical stores.

[0586] "User" refers to a person who utilizes the system to input symptoms, diagnoses, and treatments.

[0587] "Symptoms" refers to information that indicates the health condition or physical abnormality that the user is experiencing.

[0588] "Diagnosis results" refers to information on evaluations and judgments regarding the user's health condition provided by a doctor or medical institution.

[0589] "Treatment method" refers to the means or measures proposed by a medical institution to improve the user's health condition.

[0590] "Emotion data" refers to information that indicates a subjective emotional state extracted from the user's input.

[0591] "Server" refers to a central system that receives input data from users and processes it, such as by analyzing, searching, or generating.

[0592] "Database" refers to data storage within a system that stores information such as past diagnostic results and treatment cases.

[0593] A "generative AI model" refers to an artificial intelligence algorithm that generates new diagnostic results and treatment methods based on past cases and input data.

[0594] "Tablet device" refers to a portable electronic device that users use to input information such as symptoms, diagnosis, and treatment.

[0595] The term "smartphone" refers to a portable communication terminal device that is used by users to input information, similar to a tablet terminal.

[0596] "Search results" refer to new treatment methods or second opinions analyzed by the generative AI model based on similar cases in the database.

[0597] A "secure communication protocol" refers to a communication method for securely transmitting user input data to a server.

[0598] "User terminal" refers to an electronic device used by a user to receive and display the results of a second opinion.

[0599] The "means for adjusting a message" refers to a method for providing information in a form appropriate for a user based on emotional data.

[0600] This invention is a system that allows users to input their symptoms, diagnosis results, and treatment methods using a portable electronic device such as a tablet or smartphone, and provides second opinions based on that information. This system analyzes emotional data and proposes new treatment methods that take the user's psychological state into consideration.

[0601] Hardware and Software Configuration

[0602] Hardware

[0603] Tablets and smartphones: Handheld electronic devices that allow users to input information.

[0604] Server: A central system that receives user input data and performs analysis and database lookup.

[0605] software

[0606] Emotion engine: An algorithm that analyzes emotional data from user input.

[0607] Database: Data storage where similar cases and treatment cases are kept.

[0608] Generative AI model: An artificial intelligence algorithm that generates new treatment methods based on input data and past cases.

[0609] Communication protocol: A protocol for secure communication, such as HTTPS.

[0610] Program processing

[0611] User Input

[0612] Users use a tablet or smartphone to input their symptoms, diagnosis results, and treatment options. The input data is analyzed in real time by an emotion engine, and emotional data such as "anxiety" and "stress" is collected.

[0613] Sending data to the server

[0614] The device sends the data entered by the user and the emotion data together in JSON format to the server. This communication is performed using the HTTPS protocol, ensuring data security.

[0615] Database Search and Analysis

[0616] The server performs a database search based on the received data. Specifically, it executes queries based on the user's symptoms and diagnosis results to extract similar past cases.

[0617] Generative AI model for generating treatment methods

[0618] The server inputs the search results obtained from the database into the generative AI model, which analyzes and generates new treatment methods and policies. At this time, emotional data is also provided to the AI ​​model, so it can make adjustments according to the user's psychological state.

[0619] Sending and displaying results

[0620] The generated second opinion is then sent back to the user's device, which then displays the results in an appropriate format. For example, a user who is feeling anxious might be presented with a relaxing message or be given a treatment option to consider without rushing.

[0621] Specific examples

[0622] Consider a case where a patient has symptoms such as "chest pain" and "cough" and is diagnosed with "suspected lung cancer," and is advised to undergo chemotherapy. The patient enters this information using a tablet device. The emotion engine recognizes "anxiety" and "stress" from the patient's input. The device sends this information to a server, which searches for similar cases and uses a generative AI model to generate a second opinion that "radiation therapy is likely to be effective." This result is sent from the server to the patient's device and displayed along with a message to alleviate the patient's anxiety.

[0623] Prompt Sentence Examples

[0624] Symptoms: chest pain, cough

[0625] Diagnosis: Suspected lung cancer

[0626] Suggested treatment: Chemotherapy is recommended

[0627] Emotional data: anxiety, stress

[0628] Generate new treatment options and advice based on previous similar cases and treatment outcomes.

[0629] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0630] Program processing flow

[0631] Step 1:

[0632] Enter user information

[0633] Users use a tablet or smartphone to input their symptoms, diagnosis results, and treatment methods.

[0634] Input: Symptoms, diagnosis, treatment.

[0635] Output: The input data.

[0636] This data is analyzed in real time by an emotion engine to generate the user's emotion data (e.g., anxiety, stress).

[0637] Input: Data entered.

[0638] Output: Emotion data.

[0639] How it works: A user enters their information into the application's input form, which is then analyzed by the emotion engine to generate emotion data.

[0640] Step 2:

[0641] Sending data to the server

[0642] The device sends the data entered by the user and emotion data to the server in JSON format.

[0643] Input: Input data and emotion data (JSON format).

[0644] Output: The data sent to the server.

[0645] How it works: The device uses the HTTPS protocol to send data to the server over a secure connection.

[0646] Step 3:

[0647] Database search

[0648] The server analyzes the received data and generates queries against a database.

[0649] Input: User data and emotion data sent to the server.

[0650] Output: Similar cases retrieved from the database.

[0651] How it works: Based on the symptoms and diagnosis provided by the user, the server queries the database to search for similar past cases.

[0652] Step 4:

[0653] Analysis using generative AI models

[0654] The server inputs similar cases obtained as search results into a generative AI model to generate new treatment methods and policies.

[0655] Input: Similar cases retrieved from the database.

[0656] Output: Generated new treatment methods and second opinions.

[0657] How it works: Using a generative AI model, new treatment methods and policies are generated based on input similar cases and emotional data. For example, a new treatment option such as "radiation therapy is likely to be effective" is generated.

[0658] Step 5:

[0659] Sending the results

[0660] The server transmits the generated second opinion to the user's terminal.

[0661] Input: Generated second opinions and tailored messages.

[0662] Output: Data sent to the user's device.

[0663] How it works: The information generated by the server is sent back to the user's device in JSON format, along with a message based on the emotion data.

[0664] Step 6:

[0665] Displaying the results

[0666] The terminal displays the second opinion received from the server in an appropriate format.

[0667] Input: The second opinion and message sent by the server.

[0668] Output: Information displayed to the user.

[0669] How it works: The user can view the generated second opinion on their device. For anxious users, the message includes tips on how to relax and deal with the situation calmly.

[0670] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0671] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0672] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0673] [Third embodiment]

[0674] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0675] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0676] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0677] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0678] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0679] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0680] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0681] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0682] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0683] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0684] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0685] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0686] The present invention is a system that provides a second opinion based on the symptoms, diagnosis, and treatment method input by the user. The program processing of this system is explained in natural language below. Specific examples are also provided for further explanation.

[0687] System Overview

[0688] 1. User Input

[0689] Users use a device (such as a PC or smartphone) to enter their symptoms, diagnosis results, and treatment methods provided by their doctor. By entering the necessary information into a form on the device, the data is organized in an orderly manner.

[0690] 2. Data Transmission

[0691] The entered data is sent from the device to a server, which receives it, stores it in a database, and prepares it for analysis.

[0692] 3. Database Search

[0693] The server searches the database for past diagnostic and treatment cases, identifies cases with similar symptoms and diagnostic results based on the user's input data, and collects information about these cases.

[0694] 4. Analysis of AI models

[0695] Similar cases obtained as search results are input into a generative AI model, which analyzes this data and generates a second opinion to suggest the most appropriate treatment method and policy for the user's symptoms and diagnosis.

[0696] 5. Sending the results

[0697] The generated second opinion is then sent back to the user's device from the server, with important data encrypted to protect the user's privacy.

[0698] 6. Displaying the results

[0699] The terminal displays the second opinion received from the server in an appropriate format, allowing the user to quickly check the second opinion at home or on the go.

[0700] Specific examples

[0701] Suppose a patient has symptoms such as "chest pain and cough," is diagnosed by a doctor with "suspected lung cancer," and is recommended chemotherapy as a treatment. The patient enters this information using a device. The device then sends the information to a server, which then searches for similar cases in its database. For example, if a past case with similar symptoms and diagnosis is found, the generative AI model can use this to generate a second opinion stating that "radiation therapy is likely to be effective." This result is then sent from the server to the patient's device and displayed to the patient on the device.

[0702] As described above, the present invention is a system that helps users quickly obtain a second opinion from home or on the go and consider the most appropriate treatment method, significantly reducing the time burden and procedural difficulties.

[0703] The processing flow will be explained below.

[0704] Step 1:

[0705] The user enters their symptoms, diagnosis, and the proposed treatment into the terminal. Specifically, the user details symptoms such as "chest pain, cough" in the terminal form, and also enters the doctor's diagnosis of "suspected lung cancer" and the treatment recommended, "chemotherapy."

[0706] Step 2:

[0707] The terminal sends the data entered by the user to the server. Specifically, it converts the data into packets in JSON format and sends them to the server using a secure communication protocol (e.g., HTTPS).

[0708] Step 3:

[0709] The server analyzes the received data and generates a query to the database. Specifically, it executes an SQL query to search for similar past diagnoses and treatment cases based on the user's symptoms and diagnosis.

[0710] Step 4:

[0711] The server retrieves similar cases from the database, analyzes the records returned as a result of the query, and lists the cases with the highest similarity.

[0712] Step 5:

[0713] The server inputs the acquired similar cases into the generative AI model for analysis. Specifically, it converts the data of the similar cases into an input format for the AI ​​model and applies it to the model to generate new treatment methods and policies.

[0714] Step 6:

[0715] The server then sends the generated second opinion to the user's device. Specifically, it encodes the treatment method and policy data obtained from the AI ​​model into JSON format and sends it back to the device using a secure communication protocol.

[0716] Step 7:

[0717] The user's device analyzes the received second opinion and displays it in an appropriate format. Specifically, it parses the received JSON format data and displays it on the screen as text and graphics that are easy for the user to understand. The user can review this and consider treatment methods and policies.

[0718] Through the above processing steps, the user can quickly obtain a second opinion and obtain information for selecting the most appropriate treatment method or policy.

[0719] Example 1

[0720] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0721] In modern medical practice, it is important to gather opinions from multiple medical professionals to assess whether a patient's diagnosis or proposed treatment is accurate and optimal. However, obtaining a second opinion often requires significant time and cost. Furthermore, if a doctor with the appropriate expertise is not available nearby, patients have difficulty obtaining such an option. This raises concerns that patients may not receive optimal medical care and miss opportunities to maximize the effectiveness of treatment. Furthermore, there is a lack of methods for quickly obtaining a second opinion while protecting privacy.

[0722] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0723] In this invention, the server includes means for a user to input symptoms, diagnosis results, and treatment methods, means for transmitting the input data to the server, means for the server to search for similar cases in a database, means for inputting the search results to a generative AI model for analysis, means for transmitting the generated second opinion to the user, means for displaying the transmitted results on a user terminal, means for providing the input data to the generative AI model as a prompt sentence, and means for encrypting and transmitting the generated second opinion, thereby enabling a user to quickly and safely obtain a second opinion and consider the optimal treatment method.

[0724] "User" refers to an individual who utilizes the system to input their symptoms, diagnosis, and treatment options and receive a second opinion.

[0725] "Server" refers to a computer system that receives data sent by a user, searches a database to input it into a generative AI model, and sends the generated second opinion to the user.

[0726] "Terminal" refers to the device used by the user to input symptoms, diagnosis results, and treatment methods and transmit them to the server. Examples include computers and smartphones.

[0727] A "database" refers to a collection of information that includes past diagnostic results and treatment cases.

[0728] "Generative AI model" refers to artificial intelligence technology that generates second opinions based on data entered by the user.

[0729] A "prompt" refers to a formalized instruction to be input to a generative AI model.

[0730] "Encoding" refers to the process of converting data into a particular format to make it easier to transmit or store.

[0731] "Encryption" refers to converting data format using a specific algorithm to prevent the data from being read by third parties.

[0732] "Second opinion" refers to an alternative diagnosis or treatment suggested by a generative AI model based on data entered by the user.

[0733] The present invention is a system in which a user inputs their own symptoms and diagnosis results, and a generative AI model provides a second opinion based on that data. This system is mainly composed of a terminal, a server, and a generative AI model.

[0734] User Input

[0735] Users use devices such as computers or smartphones to enter their symptoms, diagnosis results, and treatment options provided by their doctors. By entering information into dedicated web forms or applications displayed on the device, data is organized in an orderly manner. For example, a user can enter "chest pain, cough" in the "symptoms" field, "suspected lung cancer" in the "diagnosis" field, and "chemotherapy" in the "treatment" field.

[0736] Data transmission

[0737] The entered data is sent from the terminal to the server. The terminal encodes the data entered in the form and sends it to the server using an HTTP POST request. The server receives this data and stores it in a database.

[0738] Database search

[0739] The server searches the database for past diagnoses and treatment cases. Based on the data entered by the user, it issues queries to identify similar cases. For example, it searches for cases with similar symptoms and diagnoses and collects this information.

[0740] AI model analysis

[0741] The server inputs similar cases obtained as search results into a generative AI model. The generative AI model is pre-trained and suggests the optimal treatment method based on the user's symptoms and diagnosis results. The server generates a prompt sentence to analyze the data with the generative AI model. The following is an example of a prompt sentence:

[0742] User symptoms: Chest pain, cough.

[0743] Diagnosis: suspected lung cancer.

[0744] Suggested treatment: chemotherapy.

[0745] Please suggest other possible treatment options based on similar past cases.

[0746] The generative AI model generates a second opinion based on this prompt and offers new treatment methods and strategies.

[0747] Sending the results

[0748] The generated second opinion is sent from the server to the user's device. The server encrypts the generated data to protect the user's privacy. The encrypted data is then sent back to the device via HTTP or HTTPS.

[0749] Displaying the results

[0750] The terminal displays the second opinion received from the server in an appropriate format. The terminal decrypts the received data and formats it in a format that is easy for the user to understand. For example, the terminal may display the results in a text view or a dialog box so that the user can quickly review them.

[0751] This system allows users to easily obtain a second opinion from home or on the go and consider the best treatment method, significantly reducing the time burden and procedural difficulties and increasing the chances of receiving more effective treatment.

[0752] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0753] Step 1:

[0754] The user uses the device to input their symptoms, diagnosis, and treatment options provided by the doctor. Specifically, the user enters data for each item into a dedicated web form or application displayed on the device. For example, the user might enter "chest pain, cough" in the "symptoms" field, "suspected lung cancer" in the "diagnosis" field, and "chemotherapy" in the "treatment" field. This input data becomes the input for the next step.

[0755] Step 2:

[0756] The terminal sends the input data to the server. The terminal encodes the input data into JSON or XML format and sends it to the server using an HTTP POST request. The sent data is entered for server execution. This process passes the user input data to the server.

[0757] Step 3:

[0758] The server stores the received data in a database. The server uses a database management system to store the input data in an appropriate format. As a result, the data of symptoms, diagnosis results, and treatment methods entered by the user are stored in the database.

[0759] Step 4:

[0760] The server searches the database for past diagnosis results and treatment cases. It issues a query based on the user's input data. For example, it searches for cases containing "chest pain," "cough," "suspected lung cancer," and "chemotherapy." The server executes this query and extracts data on similar cases. This similar case data becomes the input for the next step.

[0761] Step 5:

[0762] The server generates a prompt to input the extracted similar case data into the generative AI model. Specifically, the server uses a text processing library to construct the prompt. For example, it generates a prompt such as, "User's symptoms: chest pain, cough. Diagnosis: suspected lung cancer. Proposed treatment: chemotherapy. Please suggest other possible optimal treatment methods based on similar past cases." This prompt becomes the input for the generative AI model.

[0763] Step 6:

[0764] The server inputs a prompt sentence into the generative AI model and performs analysis. The server loads a pre-trained generative AI model and inputs a prompt sentence. The generative AI model performs analysis and generates a second opinion. The analysis result becomes the input for the next step.

[0765] Step 7:

[0766] The server sends the generated second opinion to the user's device. Specifically, the server encrypts the generated data and sends it to the device via HTTP or HTTPS. Encryption is a necessary process to protect the user's privacy.

[0767] Step 8:

[0768] The terminal displays the second opinion received from the server in an appropriate format. The terminal decrypts the received data and formats it in a format that is easy for the user to understand. For example, the terminal can display the results in a text view or a dialog box so that the user can quickly check them. At this stage, the user can review the second opinion and consider the best treatment method.

[0769] (Application example 1)

[0770] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0771] Currently, it is difficult for patients to obtain a second opinion quickly and accurately in medical settings. In particular, when patients wish to consult directly at a pharmacy or medical institution about diagnostic results or treatment options, they need to be able to receive reliable information immediately. They also need a way to efficiently input patients' symptoms and diagnostic results and suggest appropriate treatment options based on that information. There is a need for a system that can solve these issues and improve convenience and reliability in medical settings.

[0772] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0773] In this invention, the server includes: a means for a user to input symptoms, diagnosis results, and treatment methods; a means for transmitting the input data to the server; a means for the server to search for similar cases in a database; a means for inputting the search results into a generative AI model for analysis; a means for transmitting the generated second opinion to the user; a means for displaying the transmitted results on a user terminal; a means for accepting voice input using a smart device; a means for transmitting the voice-input data to a cloud server; and a means for displaying the generated second opinion on the smart device. This allows patients in medical settings to easily input symptoms using a smart device and obtain analysis results using the cloud server in real time. This enables second opinions to be obtained more quickly and efficiently.

[0774] A "user terminal" is an electronic device used by a user, such as a computer, smartphone, or smart device.

[0775] A "server" is a remote computer system that receives and processes data sent from user devices, performs database searches, and analyzes AI models.

[0776] A "database" is an information system that systematically stores medical information such as past diagnostic results and treatment cases.

[0777] A "generative AI model" is an artificial intelligence model used to generate new treatment methods or recommended policies based on data analysis.

[0778] A "second opinion" refers to an additional diagnosis or treatment suggestion provided by a different medical professional or device than the initial consultation.

[0779] A "smart device" is a pair of smart glasses, a smartphone, or other portable electronic device with advanced functionality.

[0780] "Voice input" is a method in which a user inputs data using voice, and the data is processed as text data using voice recognition technology.

[0781] A "cloud server" is a remote server accessible via the Internet that stores and processes large amounts of data.

[0782] "Real-time" refers to data processing and information provision occurring almost immediately.

[0783] An embodiment of the present invention will be described. The present invention is a system that provides a second opinion based on symptoms, diagnosis results, and treatment methods input by a user. In this system, a user terminal and a server communicate via the Internet, and the server processes medical information and performs analysis using a generative AI model.

[0784] System Configuration

[0785] User terminal

[0786] User terminals include smartphones, PCs, and smart devices. These terminals provide an interface for users to input symptoms, diagnosis results, and treatment methods. In addition, smart devices (such as smart glasses) can collect input data through voice input functions.

[0787] server

[0788] The server receives data sent from the user device, performs database search and analysis using the generative AI model. The server is hosted in a cloud environment (such as AWS or Azure) and has the following main functions:

[0789] 1. Data reception and storage: Receives symptoms, diagnosis results, and treatment methods sent from the user device and stores them in a cloud database.

[0790] 2. Database search: Search existing diagnostic and treatment cases in a database to identify similar cases.

[0791] 3. AI analysis: The search results are input into a generative AI model to generate a second opinion that suggests the best treatment method based on the user's symptoms and diagnosis.

[0792] 4. Sending the results: The generated second opinion is sent to the user terminal.

[0793] Processing explanation

[0794] After receiving the data sent from the user device, the server uses a search engine such as Elasticsearch to search for similar cases in the database. The searched cases are input into a generative AI model (e.g., GPT-4) that processes them using a programming language such as Python. The generative AI model analyzes new treatment methods or strategies and generates them as second opinions. The generated second opinions are sent to the user device in encrypted form and displayed on the user device screen or the smart glasses display.

[0795] Specific examples

[0796] Let's say a patient visits a pharmacy, reports symptoms such as "chest pain and cough," and is diagnosed with "suspected lung cancer." The suggested treatment is "chemotherapy is recommended." The patient, following the pharmacist's instructions, voice-inputs this information through the smart glasses. The smart glasses send this data to a cloud server, which searches for similar cases in its database. The generative AI model then generates a second opinion that "radiation therapy is likely to be effective," and displays the result in real time on the smart glasses.

[0797] Prompt Sentence Examples

[0798] Below are some example prompts to use when inputting a generative AI model:

[0799] "The patient has symptoms such as 'chest pain and cough' and has been diagnosed with 'suspected lung cancer'. The proposed treatment is 'chemotherapy is recommended'. Based on this information, please provide any other possible treatment options or opinions."

[0800] The present invention thereby enables patients to efficiently and quickly obtain second opinions at medical sites, thereby improving the quality of medical care.

[0801] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0802] Step 1:

[0803] The user inputs symptoms, diagnosis results, and treatment methods. Input is done through voice or text input using a smartphone or smart device (such as smart glasses). Specifically, by utilizing the voice input function of the smart glasses, the user verbally describes their symptoms, which are then converted into text data using voice recognition technology. This input data becomes the input for the next step.

[0804] Step 2:

[0805] The device sends the entered data to a cloud server. This process is securely carried out over the Internet. The cloud server first receives the data and temporarily stores it. This data is stored as text information about symptoms, diagnosis results, and treatment methods.

[0806] Step 3:

[0807] The server performs a database search. Based on the data stored on the cloud server, it uses a search engine such as Elasticsearch to search for similar cases in the database. Specifically, it uses the symptoms and diagnosis results entered by the user as a query to search for related cases with past diagnoses and treatment methods. The search results are used as input data for the next step.

[0808] Step 4:

[0809] The searched similar cases are input into a generative AI model. The cloud server preprocesses the search results using a programming language such as Python and inputs them into a generative AI model (e.g., GPT-4). The AI ​​model then analyzes the search results and generates a second opinion that suggests the optimal treatment method based on the user's symptoms and diagnosis.

[0810] Step 5:

[0811] The generated second opinion is sent to the user terminal. The server encrypts the generated second opinion data and sends it to the user terminal using a secure communication method to protect the user's privacy. This output data is sent as text data of the final second opinion.

[0812] Step 6:

[0813] The results are displayed on the user's device. The received second opinion is displayed on the smart glasses display or smartphone screen. The user can consider additional treatment methods and policies based on the displayed second opinion. Specifically, the results are displayed visually using the smart device's HUD (head-up display) API.

[0814] These steps allow users to quickly and accurately obtain second opinions, improving the quality of diagnosis and treatment in medical settings.

[0815] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0816] The present invention is a system that provides a second opinion by combining an emotion engine based on the symptoms, diagnosis, and treatment methods entered by the user. The program processing of this system is explained in natural language below. Specific examples are also provided for further explanation.

[0817] System Overview

[0818] 1. User Input

[0819] Users input their symptoms, diagnosis results, and treatment options provided by their doctor into the device. The emotion engine then recognizes emotions from the user's input data, and this data is also collected at the same time. For example, if a user enters symptoms such as "chest pain, cough" into the form, and enters a diagnosis of "suspected lung cancer" and a treatment option of "chemotherapy recommended," the emotion engine will recognize emotions such as stress and anxiety from the user's text input.

[0820] 2. Data Transmission

[0821] The device sends the data entered by the user and the emotion data recognized by the emotion engine to the server in JSON format using a secure communication protocol (e.g., HTTPS).

[0822] 3. Database Search

[0823] The server analyzes the received data and generates a query to the database, specifically, to search for similar past diagnoses and treatment cases based on the user's symptoms and diagnosis.

[0824] 4. Analysis of AI models

[0825] The server inputs similar cases obtained as search results into a generative AI model and performs analysis to generate new treatment methods and policies. In addition, recognized emotional data is also provided to the AI ​​model, which adjusts accordingly.

[0826] 5. Sending the results

[0827] The server then sends the generated second opinion back to the user's device. At this time, the second opinion reflects adjustments based on the emotional data. For example, if a user is feeling very anxious, it will highlight treatment options that allow them to consult without rushing.

[0828] 6. Displaying the results

[0829] The device displays the second opinion received from the server in an appropriate format, allowing users to quickly check the second opinion whether they are at home or on the go. Furthermore, the information is presented in a way that takes the user's emotions into consideration, making it easier for them to calmly consider treatment methods and policies.

[0830] Specific examples

[0831] Suppose a patient has symptoms such as "chest pain and cough," is diagnosed by a doctor with "suspected lung cancer," and is recommended chemotherapy as a treatment. When the patient enters this information using a device, the emotion engine recognizes "anxiety" and "stress" from the patient's input. The device sends this information to a server, which searches for similar cases in a database. For example, if a past case with similar symptoms and diagnosis is found, the generative AI model can use this to generate a second opinion that "radiation therapy is likely to be effective." This result is sent from the server to the patient's device, and when it is displayed to the patient on the device, a message to alleviate the patient's anxiety is added.

[0832] As described above, the present invention is a system that allows users to quickly obtain a second opinion from home or on the go, and provides information that takes the user's feelings into consideration, helping them calmly consider the optimal treatment method and policy. This system significantly reduces the time burden and procedural difficulties, and also reduces the psychological burden.

[0833] The processing flow will be explained below.

[0834] Step 1:

[0835] The user enters their symptoms, diagnosis, and proposed treatment into the terminal. Specifically, the user details symptoms such as "chest pain, cough" in the terminal form, and also enters the doctor's diagnosis of "suspected lung cancer" and the treatment recommended, "chemotherapy."

[0836] Step 2:

[0837] The emotion engine recognizes emotions from the user's input data. Specifically, it analyzes the text entered by the user and identifies emotions such as "anxiety" and "stress." This emotion data is also collected along with the user's input data.

[0838] Step 3:

[0839] The device sends the data entered by the user and the emotion data recognized by the emotion engine to the server in JSON format using a secure communication protocol (e.g., HTTPS).

[0840] Step 4:

[0841] The server analyzes the received data and generates a query to the database. Specifically, it creates and executes an SQL query to search for similar past diagnostic results and treatment cases based on the user's symptoms and diagnostic results.

[0842] Step 5:

[0843] The server retrieves similar cases from the database, analyzes the records returned as a result of the query, and lists the cases with the highest similarity.

[0844] Step 6:

[0845] The server inputs the acquired data of similar cases into the generative AI model for analysis. Specifically, it converts the data of similar cases into an input format for the AI ​​model and applies it to the model to generate new treatment methods and policies.

[0846] Step 7:

[0847] The server also provides emotional data from the emotion engine to the AI ​​model, allowing it to adapt the generated second opinion to the user's emotions. For example, if the user is feeling highly anxious, the second opinion will be adjusted to emphasize treatment options that encourage more patient action.

[0848] Step 8:

[0849] The server then sends the generated second opinion to the user's device. Specifically, the server encodes the second opinion in JSON format and sends it to the device using a secure communication protocol.

[0850] Step 9:

[0851] The device analyzes the second opinion received from the server and displays it in an appropriate format. Specifically, it parses the received JSON format data and displays it on the screen as text and graphics that are easy for the user to understand. The user can review this and consider treatment methods and policies.

[0852] Through the above processing steps, users can quickly obtain a second opinion and obtain information to select the most appropriate treatment method and policy. In addition, by using the emotion engine, it is possible to provide information that takes into account the user's psychological state, reducing stress and anxiety during the treatment decision-making process.

[0853] Example 2

[0854] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0855] This invention relates to a system that simultaneously collects and analyzes emotional data when a user inputs their symptoms, diagnosis, and treatment options, and uses this data to provide an optimal second opinion. Conventional systems present diagnosis and treatment options without taking the user's emotional state into consideration, resulting in problems such as inappropriate communication of information or inability to alleviate the user's anxiety. Furthermore, when there are multiple diagnosis and treatment options, comparing and examining each piece of information takes a significant amount of time, placing a significant burden on the user.

[0856] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0857] In this invention, the server includes: means for a user to input symptoms, diagnosis results, and treatment methods; means for analyzing the input data and recognizing emotional data; means for transmitting the analyzed data and emotional data to the server; means for the server to search for similar cases in a database; means for inputting the search results and emotional data into a generative AI model for analysis; means for transmitting the generated second opinion to the user; and means for displaying the transmitted results and adjustments based on the emotions on a user terminal. This allows for the provision of a prompt and appropriate second opinion that takes into consideration the user's emotional state, enabling the user to calmly consider treatment methods and policies.

[0858] "User" means an individual or corporation that uses this system.

[0859] "Symptoms" refer to changes or abnormalities in the user's physical condition.

[0860] "Diagnosis results" refers to medical condition assessments and diagnostic information made by medical professionals based on the user's symptoms.

[0861] "Treatment method" refers to the treatment measures or methods suggested by a medical professional based on the diagnosis results.

[0862] "Emotion data" is data that represents the emotional state recognized from the user's input content.

[0863] A "terminal" is a device (e.g., a PC, smartphone, or tablet) that allows a user to input symptoms, diagnosis results, and treatment methods.

[0864] A "server" is a central processing unit that receives and analyzes data sent by users, manages the database, and executes generative AI models.

[0865] A "database" is an information system that stores and manages past diagnostic results and treatment cases.

[0866] A "query" refers to a search request made to a database.

[0867] A "generative AI model" is an artificial intelligence mechanism that generates new treatment methods and policies based on input data.

[0868] "Second opinion" refers to a separate professional opinion or judgment provided in addition to the diagnosis or treatment method obtained by the user.

[0869] The "JSON format" is a lightweight data exchange format for structuring and representing data.

[0870] "Secure protocol" refers to a communication protocol for securely sending and receiving data, and includes HTTPS.

[0871] "Emotion-based adjustment" refers to modifying and adjusting the generated results to suit the user based on recognized emotional data.

[0872] The present invention provides a system for providing a second opinion based on symptoms, diagnosis results, and treatment methods input by a user, and also combining emotion data. This system includes the following elements:

[0873] The user enters their symptoms, diagnosis, and treatment options provided by the doctor into the device. In the device's form, the user writes down symptoms such as "chest pain, cough," and enters the diagnosis of "suspected lung cancer" and the treatment option of "chemotherapy recommended." In addition, an emotion engine works in the background to recognize emotion data such as "anxiety" and "stress" from the user's input. This emotion data is generated based on the user's text input.

[0874] The device sends the data entered by the user and emotion data to the server. The data is structured in JSON format and transmitted via a secure communication protocol (e.g., HTTPS), which prevents the risk of data eavesdropping or tampering.

[0875] The server analyzes the received data and generates a query for the database. Specifically, it executes a query to search for similar past diagnosis results and treatment cases based on the user's symptoms and diagnosis results. For example, a search using the conditions "chest pain," "cough," and "suspected lung cancer" is performed to retrieve similar past cases.

[0876] The server then inputs the similar cases obtained as search results into a generative AI model (such as GPT-4). The generative AI model then uses this data to perform analysis to generate new treatment methods and policies. Emotional data is also provided to the AI ​​model, and the results are adjusted accordingly. For example, a second opinion is generated that "radiation therapy is likely to be effective," and detailed explanations and success stories are added for users who are feeling stressed.

[0877] The generated second opinion is then sent back to the user's device from the server. At this time, the second opinion reflects adjustments based on the emotional data. For example, if a user is feeling anxious, a message emphasizing treatment options and detailed explanations will be added so that the user can consult calmly.

[0878] The device receives the second opinion from the server and displays it to the user in an appropriate format. Users can quickly check the second opinion at home or on the go. Furthermore, the information is provided in a way that takes emotions into consideration, making it easier to calmly consider treatment methods and policies.

[0879] As a specific example, consider the case where a patient has symptoms such as "chest pain and cough" and is diagnosed by a doctor with "suspected lung cancer" and told that "chemotherapy is recommended." The patient enters this information using a device, and the emotion engine recognizes "anxiety" and "stress" from the input. The device sends this information to a server, which searches for similar cases in a database. For example, if a past case of a patient with similar symptoms is found, the generative AI model may use this to generate a second opinion stating that "radiotherapy is likely to be effective." This result is then sent back to the patient's device, and when displayed to the patient on the device, a message such as "There are many successful cases of radiation therapy, so don't worry" is added.

[0880] An example of a prompt sentence could be in the following format:

[0881] "Generate second opinions based on user-entered symptoms, diagnoses, and treatment options. Include appropriate treatment options and messages, taking into account user sentiment data."

[0882] As described above, the present invention is a system that allows users to quickly obtain a second opinion from home or on the go, and helps them calmly consider the optimal treatment method and policy by providing information that takes the user's emotions into consideration. This system significantly reduces the time burden and procedures, and also reduces the psychological burden.

[0883] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0884] Program processing steps

[0885] Step 1: User enters symptoms, diagnosis, and treatment

[0886] The user enters their symptoms, diagnosis, and treatment method into a dedicated form on the device. The input data includes "chest pain, cough" (symptoms), "suspected lung cancer" (diagnosis), and "chemotherapy recommended" (treatment method), and enters it into the device's form. The device collects this data. Once the input data is ready, the user clicks the "Submit" button.

[0887] Step 2: Analyzing input data and recognizing emotions

[0888] The device analyzes the data entered by the user and recognizes emotional data using an emotion engine. The input data includes symptoms, diagnosis, and treatment methods. The emotion engine uses a text analysis algorithm to extract emotions (e.g., anxiety, stress) contained in the user's input data. The output at this point is symptoms, diagnosis, treatment, and recognized emotional data.

[0889] Step 3: Sending data

[0890] The device sends the analyzed data (symptoms, diagnosis results, treatment methods) and recognized emotion data in JSON format to the server. The HTTPS protocol is used for transmission, ensuring secure data transfer. The input data is the information entered by the user and emotion data, and this information is sent to the server as output.

[0891] Step 4: Receiving and analyzing data

[0892] The server receives data sent from the device. The received data includes symptoms, diagnosis results, treatment methods, and emotion data. The server analyzes this data and generates a query for database search. The received data is the input, and the query for database search is generated as the output.

[0893] Step 5: Database Search

[0894] The server uses the generated query to search for similar cases in the database. The query includes conditions such as "chest pain," "cough," and "suspected lung cancer." The database returns past diagnoses and treatment cases that match these conditions. The search is performed based on the input query, and a list of similar cases is obtained as the output.

[0895] Step 6: Input and analysis into the AI ​​model

[0896] The server inputs similar cases obtained as search results into a generative AI model. The generative AI model performs analysis based on this data to generate new treatment methods and policies. Emotional data is also provided to the AI ​​model, which makes adjustments accordingly. Search result data and emotional data are given as input, and new treatment methods and policies are generated as output.

[0897] Step 7: Generate a second opinion

[0898] The server receives the second opinion generated by the generative AI model and adjusts it based on emotional data. For example, if a second opinion is generated stating that "radiation therapy is likely to be effective," the server adds a message such as "If you feel anxious, we recommend that you seek professional counseling." The generated second opinion is the input, and the adjusted second opinion is the output.

[0899] Step 8: Submit a second opinion

[0900] The server then sends the adjusted second opinion back to the user's device, again using the HTTPS protocol. The adjusted second opinion is given as input and sent to the device as output.

[0901] Step 9: View the results

[0902] The terminal displays the second opinion received from the server to the user in an appropriate format. The user can quickly check this second opinion at home or on the go. The display is done in a way that takes emotions into consideration, for example, by adding a message such as "Don't rush, let's hear other opinions." The second opinion received is the input, and it is displayed to the user as the output.

[0903] (Application example 2)

[0904] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0905] Conventional systems that provide second opinions present diagnosis results and treatment methods without considering the user's feelings, which increases the user's psychological burden. In addition, when users enter medical information in a physical store, a quick and appropriate response is required, but there was no system that could efficiently do this.

[0906] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0907] In this invention, the server includes means for a user to input symptoms, diagnosis results, and treatment methods, means for transmitting the input data and emotion data to the server, means for the server to search for similar cases in a database, means for inputting the search results into a generative AI model for analysis, means for transmitting the generated second opinion to the user, means for displaying the transmitted results on a user terminal, means for adjusting the message based on the emotion data, and means for the user to input information using a tablet terminal or smartphone. This makes it possible to provide second opinions that take the user's emotions into consideration, thereby realizing fast and efficient service provision in physical stores.

[0908] "User" refers to a person who utilizes the system to input symptoms, diagnoses, and treatments.

[0909] "Symptoms" refers to information that indicates the health condition or physical abnormality that the user is experiencing.

[0910] "Diagnosis results" refers to information on evaluations and judgments regarding the user's health condition provided by a doctor or medical institution.

[0911] "Treatment method" refers to the means or measures proposed by a medical institution to improve the user's health condition.

[0912] "Emotion data" refers to information that indicates a subjective emotional state extracted from the user's input.

[0913] "Server" refers to a central system that receives input data from users and processes it, such as by analyzing, searching, or generating.

[0914] "Database" refers to data storage within a system that stores information such as past diagnostic results and treatment cases.

[0915] A "generative AI model" refers to an artificial intelligence algorithm that generates new diagnostic results and treatment methods based on past cases and input data.

[0916] "Tablet device" refers to a portable electronic device that users use to input information such as symptoms, diagnosis, and treatment.

[0917] The term "smartphone" refers to a portable communication terminal device that is used by users to input information, similar to a tablet terminal.

[0918] "Search results" refer to new treatment methods or second opinions analyzed by the generative AI model based on similar cases in the database.

[0919] A "secure communication protocol" refers to a communication method for securely transmitting user input data to a server.

[0920] "User terminal" refers to an electronic device used by a user to receive and display the results of a second opinion.

[0921] The "means for adjusting a message" refers to a method for providing information in a form appropriate for a user based on emotional data.

[0922] This invention is a system that allows users to input their symptoms, diagnosis results, and treatment methods using a portable electronic device such as a tablet or smartphone, and provides second opinions based on that information. This system analyzes emotional data and proposes new treatment methods that take the user's psychological state into consideration.

[0923] Hardware and Software Configuration

[0924] Hardware

[0925] Tablets and smartphones: Handheld electronic devices that allow users to input information.

[0926] Server: A central system that receives user input data and performs analysis and database lookup.

[0927] software

[0928] Emotion engine: An algorithm that analyzes emotional data from user input.

[0929] Database: Data storage where similar cases and treatment cases are kept.

[0930] Generative AI model: An artificial intelligence algorithm that generates new treatment methods based on input data and past cases.

[0931] Communication protocol: A protocol for secure communication, such as HTTPS.

[0932] Program processing

[0933] User Input

[0934] Users use a tablet or smartphone to input their symptoms, diagnosis results, and treatment options. The input data is analyzed in real time by an emotion engine, and emotional data such as "anxiety" and "stress" is collected.

[0935] Sending data to the server

[0936] The device sends the data entered by the user and the emotion data together in JSON format to the server. This communication is performed using the HTTPS protocol, ensuring data security.

[0937] Database Search and Analysis

[0938] The server performs a database search based on the received data. Specifically, it executes queries based on the user's symptoms and diagnosis results to extract similar past cases.

[0939] Generative AI model for generating treatment methods

[0940] The server inputs the search results obtained from the database into the generative AI model, which analyzes and generates new treatment methods and policies. At this time, emotional data is also provided to the AI ​​model, so it can make adjustments according to the user's psychological state.

[0941] Sending and displaying results

[0942] The generated second opinion is then sent back to the user's device, which then displays the results in an appropriate format. For example, a user who is feeling anxious might be presented with a relaxing message or be given a treatment option to consider without rushing.

[0943] Specific examples

[0944] Consider a case where a patient has symptoms such as "chest pain" and "cough" and is diagnosed with "suspected lung cancer," and is advised to undergo chemotherapy. The patient enters this information using a tablet device. The emotion engine recognizes "anxiety" and "stress" from the patient's input. The device sends this information to a server, which searches for similar cases and uses a generative AI model to generate a second opinion that "radiation therapy is likely to be effective." This result is sent from the server to the patient's device and displayed along with a message to alleviate the patient's anxiety.

[0945] Prompt Sentence Examples

[0946] Symptoms: chest pain, cough

[0947] Diagnosis: Suspected lung cancer

[0948] Suggested treatment: Chemotherapy is recommended

[0949] Emotional data: anxiety, stress

[0950] Generate new treatment options and advice based on previous similar cases and treatment outcomes.

[0951] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0952] Program processing flow

[0953] Step 1:

[0954] Enter user information

[0955] Users use a tablet or smartphone to input their symptoms, diagnosis results, and treatment methods.

[0956] Input: Symptoms, diagnosis, treatment.

[0957] Output: The input data.

[0958] This data is analyzed in real time by an emotion engine to generate the user's emotion data (e.g., anxiety, stress).

[0959] Input: Data entered.

[0960] Output: Emotion data.

[0961] How it works: A user enters their information into the application's input form, which is then analyzed by the emotion engine to generate emotion data.

[0962] Step 2:

[0963] Sending data to the server

[0964] The device sends the data entered by the user and emotion data to the server in JSON format.

[0965] Input: Input data and emotion data (JSON format).

[0966] Output: The data sent to the server.

[0967] How it works: The device uses the HTTPS protocol to send data to the server over a secure connection.

[0968] Step 3:

[0969] Database search

[0970] The server analyzes the received data and generates queries against a database.

[0971] Input: User data and emotion data sent to the server.

[0972] Output: Similar cases retrieved from the database.

[0973] How it works: Based on the symptoms and diagnosis provided by the user, the server queries the database to search for similar past cases.

[0974] Step 4:

[0975] Analysis using generative AI models

[0976] The server inputs similar cases obtained as search results into a generative AI model to generate new treatment methods and policies.

[0977] Input: Similar cases retrieved from the database.

[0978] Output: Generated new treatment methods and second opinions.

[0979] How it works: Using a generative AI model, new treatment methods and policies are generated based on input similar cases and emotional data. For example, a new treatment option such as "radiation therapy is likely to be effective" is generated.

[0980] Step 5:

[0981] Sending the results

[0982] The server transmits the generated second opinion to the user's terminal.

[0983] Input: Generated second opinions and tailored messages.

[0984] Output: Data sent to the user's device.

[0985] How it works: The information generated by the server is sent back to the user's device in JSON format, along with a message based on the emotion data.

[0986] Step 6:

[0987] Displaying the results

[0988] The terminal displays the second opinion received from the server in an appropriate format.

[0989] Input: The second opinion and message sent by the server.

[0990] Output: Information displayed to the user.

[0991] How it works: The user can view the generated second opinion on their device. For anxious users, the message includes tips on how to relax and deal with the situation calmly.

[0992] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0993] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0994] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0995] [Fourth embodiment]

[0996] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0997] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0998] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0999] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1000] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1001] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1002] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1003] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1004] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1005] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1006] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1007] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1008] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1009] The present invention is a system that provides a second opinion based on the symptoms, diagnosis, and treatment method input by the user. The program processing of this system is explained in natural language below. Specific examples are also provided for further explanation.

[1010] System Overview

[1011] 1. User Input

[1012] Users use a device (such as a PC or smartphone) to enter their symptoms, diagnosis results, and treatment methods provided by their doctor. By entering the necessary information into a form on the device, the data is organized in an orderly manner.

[1013] 2. Data Transmission

[1014] The entered data is sent from the device to a server, which receives it, stores it in a database, and prepares it for analysis.

[1015] 3. Database Search

[1016] The server searches the database for past diagnostic and treatment cases, identifies cases with similar symptoms and diagnostic results based on the user's input data, and collects information about these cases.

[1017] 4. Analysis of AI models

[1018] Similar cases obtained as search results are input into a generative AI model, which analyzes this data and generates a second opinion to suggest the most appropriate treatment method and policy for the user's symptoms and diagnosis.

[1019] 5. Sending the results

[1020] The generated second opinion is then sent back to the user's device from the server, with important data encrypted to protect the user's privacy.

[1021] 6. Displaying the results

[1022] The terminal displays the second opinion received from the server in an appropriate format, allowing the user to quickly check the second opinion at home or on the go.

[1023] Specific examples

[1024] Suppose a patient has symptoms such as "chest pain and cough," is diagnosed by a doctor with "suspected lung cancer," and is recommended chemotherapy as a treatment. The patient enters this information using a device. The device then sends the information to a server, which then searches for similar cases in its database. For example, if a past case with similar symptoms and diagnosis is found, the generative AI model can use this to generate a second opinion stating that "radiation therapy is likely to be effective." This result is then sent from the server to the patient's device and displayed to the patient on the device.

[1025] As described above, the present invention is a system that helps users quickly obtain a second opinion from home or on the go and consider the most appropriate treatment method, significantly reducing the time burden and procedural difficulties.

[1026] The processing flow will be explained below.

[1027] Step 1:

[1028] The user enters their symptoms, diagnosis, and the proposed treatment into the terminal. Specifically, the user details symptoms such as "chest pain, cough" in the terminal form, and also enters the doctor's diagnosis of "suspected lung cancer" and the treatment recommended, "chemotherapy."

[1029] Step 2:

[1030] The terminal sends the data entered by the user to the server. Specifically, it converts the data into packets in JSON format and sends them to the server using a secure communication protocol (e.g., HTTPS).

[1031] Step 3:

[1032] The server analyzes the received data and generates a query to the database. Specifically, it executes an SQL query to search for similar past diagnoses and treatment cases based on the user's symptoms and diagnosis.

[1033] Step 4:

[1034] The server retrieves similar cases from the database, analyzes the records returned as a result of the query, and lists the cases with the highest similarity.

[1035] Step 5:

[1036] The server inputs the acquired similar cases into the generative AI model for analysis. Specifically, it converts the data of the similar cases into an input format for the AI ​​model and applies it to the model to generate new treatment methods and policies.

[1037] Step 6:

[1038] The server then sends the generated second opinion to the user's device. Specifically, it encodes the treatment method and policy data obtained from the AI ​​model into JSON format and sends it back to the device using a secure communication protocol.

[1039] Step 7:

[1040] The user's device analyzes the received second opinion and displays it in an appropriate format. Specifically, it parses the received JSON format data and displays it on the screen as text and graphics that are easy for the user to understand. The user can review this and consider treatment methods and policies.

[1041] Through the above processing steps, the user can quickly obtain a second opinion and obtain information for selecting the most appropriate treatment method or policy.

[1042] Example 1

[1043] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1044] In modern medical practice, it is important to gather opinions from multiple medical professionals to assess whether a patient's diagnosis or proposed treatment is accurate and optimal. However, obtaining a second opinion often requires significant time and cost. Furthermore, if a doctor with the appropriate expertise is not available nearby, patients have difficulty obtaining such an option. This raises concerns that patients may not receive optimal medical care and miss opportunities to maximize the effectiveness of treatment. Furthermore, there is a lack of methods for quickly obtaining a second opinion while protecting privacy.

[1045] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1046] In this invention, the server includes means for a user to input symptoms, diagnosis results, and treatment methods, means for transmitting the input data to the server, means for the server to search for similar cases in a database, means for inputting the search results to a generative AI model for analysis, means for transmitting the generated second opinion to the user, means for displaying the transmitted results on a user terminal, means for providing the input data to the generative AI model as a prompt sentence, and means for encrypting and transmitting the generated second opinion, thereby enabling a user to quickly and safely obtain a second opinion and consider the optimal treatment method.

[1047] "User" refers to an individual who utilizes the system to input their symptoms, diagnosis, and treatment options and receive a second opinion.

[1048] "Server" refers to a computer system that receives data sent by a user, searches a database to input it into a generative AI model, and sends the generated second opinion to the user.

[1049] "Terminal" refers to the device used by the user to input symptoms, diagnosis results, and treatment methods and transmit them to the server. Examples include computers and smartphones.

[1050] A "database" refers to a collection of information that includes past diagnostic results and treatment cases.

[1051] "Generative AI model" refers to artificial intelligence technology that generates second opinions based on data entered by the user.

[1052] A "prompt" refers to a formalized instruction to be input to a generative AI model.

[1053] "Encoding" refers to the process of converting data into a particular format to make it easier to transmit or store.

[1054] "Encryption" refers to converting data format using a specific algorithm to prevent the data from being read by third parties.

[1055] "Second opinion" refers to an alternative diagnosis or treatment suggested by a generative AI model based on data entered by the user.

[1056] The present invention is a system in which a user inputs their own symptoms and diagnosis results, and a generative AI model provides a second opinion based on that data. This system is mainly composed of a terminal, a server, and a generative AI model.

[1057] User Input

[1058] Users use devices such as computers or smartphones to enter their symptoms, diagnosis results, and treatment options provided by their doctors. By entering information into dedicated web forms or applications displayed on the device, data is organized in an orderly manner. For example, a user can enter "chest pain, cough" in the "symptoms" field, "suspected lung cancer" in the "diagnosis" field, and "chemotherapy" in the "treatment" field.

[1059] Data transmission

[1060] The entered data is sent from the terminal to the server. The terminal encodes the data entered in the form and sends it to the server using an HTTP POST request. The server receives this data and stores it in a database.

[1061] Database search

[1062] The server searches the database for past diagnoses and treatment cases. Based on the data entered by the user, it issues queries to identify similar cases. For example, it searches for cases with similar symptoms and diagnoses and collects this information.

[1063] AI model analysis

[1064] The server inputs similar cases obtained as search results into a generative AI model. The generative AI model is pre-trained and suggests the optimal treatment method based on the user's symptoms and diagnosis results. The server generates a prompt sentence to analyze the data with the generative AI model. The following is an example of a prompt sentence:

[1065] User symptoms: Chest pain, cough.

[1066] Diagnosis: suspected lung cancer.

[1067] Suggested treatment: chemotherapy.

[1068] Please suggest other possible treatment options based on similar past cases.

[1069] The generative AI model generates a second opinion based on this prompt and offers new treatment methods and strategies.

[1070] Sending the results

[1071] The generated second opinion is sent from the server to the user's device. The server encrypts the generated data to protect the user's privacy. The encrypted data is then sent back to the device via HTTP or HTTPS.

[1072] Displaying the results

[1073] The terminal displays the second opinion received from the server in an appropriate format. The terminal decrypts the received data and formats it in a format that is easy for the user to understand. For example, the terminal may display the results in a text view or a dialog box so that the user can quickly review them.

[1074] This system allows users to easily obtain a second opinion from home or on the go and consider the best treatment method, significantly reducing the time burden and procedural difficulties and increasing the chances of receiving more effective treatment.

[1075] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1076] Step 1:

[1077] The user uses the device to input their symptoms, diagnosis, and treatment options provided by the doctor. Specifically, the user enters data for each item into a dedicated web form or application displayed on the device. For example, the user might enter "chest pain, cough" in the "symptoms" field, "suspected lung cancer" in the "diagnosis" field, and "chemotherapy" in the "treatment" field. This input data becomes the input for the next step.

[1078] Step 2:

[1079] The terminal sends the input data to the server. The terminal encodes the input data into JSON or XML format and sends it to the server using an HTTP POST request. The sent data is entered for server execution. This process passes the user input data to the server.

[1080] Step 3:

[1081] The server stores the received data in a database. The server uses a database management system to store the input data in an appropriate format. As a result, the data of symptoms, diagnosis results, and treatment methods entered by the user are stored in the database.

[1082] Step 4:

[1083] The server searches the database for past diagnosis results and treatment cases. It issues a query based on the user's input data. For example, it searches for cases containing "chest pain," "cough," "suspected lung cancer," and "chemotherapy." The server executes this query and extracts data on similar cases. This similar case data becomes the input for the next step.

[1084] Step 5:

[1085] The server generates a prompt to input the extracted similar case data into the generative AI model. Specifically, the server uses a text processing library to construct the prompt. For example, it generates a prompt such as, "User's symptoms: chest pain, cough. Diagnosis: suspected lung cancer. Proposed treatment: chemotherapy. Please suggest other possible optimal treatment methods based on similar past cases." This prompt becomes the input for the generative AI model.

[1086] Step 6:

[1087] The server inputs a prompt sentence into the generative AI model and performs analysis. The server loads a pre-trained generative AI model and inputs a prompt sentence. The generative AI model performs analysis and generates a second opinion. The analysis result becomes the input for the next step.

[1088] Step 7:

[1089] The server sends the generated second opinion to the user's device. Specifically, the server encrypts the generated data and sends it to the device via HTTP or HTTPS. Encryption is a necessary process to protect the user's privacy.

[1090] Step 8:

[1091] The terminal displays the second opinion received from the server in an appropriate format. The terminal decrypts the received data and formats it in a format that is easy for the user to understand. For example, the terminal can display the results in a text view or a dialog box so that the user can quickly check them. At this stage, the user can review the second opinion and consider the best treatment method.

[1092] (Application example 1)

[1093] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1094] Currently, it is difficult for patients to obtain a second opinion quickly and accurately in medical settings. In particular, when patients wish to consult directly at a pharmacy or medical institution about diagnostic results or treatment options, they need to be able to receive reliable information immediately. They also need a way to efficiently input patients' symptoms and diagnostic results and suggest appropriate treatment options based on that information. There is a need for a system that can solve these issues and improve convenience and reliability in medical settings.

[1095] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1096] In this invention, the server includes: a means for a user to input symptoms, diagnosis results, and treatment methods; a means for transmitting the input data to the server; a means for the server to search for similar cases in a database; a means for inputting the search results into a generative AI model for analysis; a means for transmitting the generated second opinion to the user; a means for displaying the transmitted results on a user terminal; a means for accepting voice input using a smart device; a means for transmitting the voice-input data to a cloud server; and a means for displaying the generated second opinion on the smart device. This allows patients in medical settings to easily input symptoms using a smart device and obtain analysis results using the cloud server in real time. This enables second opinions to be obtained more quickly and efficiently.

[1097] A "user terminal" is an electronic device used by a user, such as a computer, smartphone, or smart device.

[1098] A "server" is a remote computer system that receives and processes data sent from user devices, performs database searches, and analyzes AI models.

[1099] A "database" is an information system that systematically stores medical information such as past diagnostic results and treatment cases.

[1100] A "generative AI model" is an artificial intelligence model used to generate new treatment methods or recommended policies based on data analysis.

[1101] A "second opinion" refers to an additional diagnosis or treatment suggestion provided by a different medical professional or device than the initial consultation.

[1102] A "smart device" is a pair of smart glasses, a smartphone, or other portable electronic device with advanced functionality.

[1103] "Voice input" is a method in which a user inputs data using voice, and the data is processed as text data using voice recognition technology.

[1104] A "cloud server" is a remote server accessible via the Internet that stores and processes large amounts of data.

[1105] "Real-time" refers to data processing and information provision occurring almost immediately.

[1106] An embodiment of the present invention will be described. The present invention is a system that provides a second opinion based on symptoms, diagnosis results, and treatment methods input by a user. In this system, a user terminal and a server communicate via the Internet, and the server processes medical information and performs analysis using a generative AI model.

[1107] System Configuration

[1108] User terminal

[1109] User terminals include smartphones, PCs, and smart devices. These terminals provide an interface for users to input symptoms, diagnosis results, and treatment methods. In addition, smart devices (such as smart glasses) can collect input data through voice input functions.

[1110] server

[1111] The server receives data sent from the user device, performs database search and analysis using the generative AI model. The server is hosted in a cloud environment (such as AWS or Azure) and has the following main functions:

[1112] 1. Data reception and storage: Receives symptoms, diagnosis results, and treatment methods sent from the user device and stores them in a cloud database.

[1113] 2. Database search: Search existing diagnostic and treatment cases in a database to identify similar cases.

[1114] 3. AI analysis: The search results are input into a generative AI model to generate a second opinion that suggests the best treatment method based on the user's symptoms and diagnosis.

[1115] 4. Sending the results: The generated second opinion is sent to the user terminal.

[1116] Processing explanation

[1117] After receiving the data sent from the user device, the server uses a search engine such as Elasticsearch to search for similar cases in the database. The searched cases are input into a generative AI model (e.g., GPT-4) that processes them using a programming language such as Python. The generative AI model analyzes new treatment methods or strategies and generates them as second opinions. The generated second opinions are sent to the user device in encrypted form and displayed on the user device screen or the smart glasses display.

[1118] Specific examples

[1119] Let's say a patient visits a pharmacy, reports symptoms such as "chest pain and cough," and is diagnosed with "suspected lung cancer." The suggested treatment is "chemotherapy is recommended." The patient, following the pharmacist's instructions, voice-inputs this information through the smart glasses. The smart glasses send this data to a cloud server, which searches for similar cases in its database. The generative AI model then generates a second opinion that "radiation therapy is likely to be effective," and displays the result in real time on the smart glasses.

[1120] Prompt Sentence Examples

[1121] Below are some example prompts to use when inputting a generative AI model:

[1122] "The patient has symptoms such as 'chest pain and cough' and has been diagnosed with 'suspected lung cancer'. The proposed treatment is 'chemotherapy is recommended'. Based on this information, please provide any other possible treatment options or opinions."

[1123] The present invention thereby enables patients to efficiently and quickly obtain second opinions at medical sites, thereby improving the quality of medical care.

[1124] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1125] Step 1:

[1126] The user inputs symptoms, diagnosis results, and treatment methods. Input is done through voice or text input using a smartphone or smart device (such as smart glasses). Specifically, by utilizing the voice input function of the smart glasses, the user verbally describes their symptoms, which are then converted into text data using voice recognition technology. This input data becomes the input for the next step.

[1127] Step 2:

[1128] The device sends the entered data to a cloud server. This process is securely carried out over the Internet. The cloud server first receives the data and temporarily stores it. This data is stored as text information about symptoms, diagnosis results, and treatment methods.

[1129] Step 3:

[1130] The server performs a database search. Based on the data stored on the cloud server, it uses a search engine such as Elasticsearch to search for similar cases in the database. Specifically, it uses the symptoms and diagnosis results entered by the user as a query to search for related cases with past diagnoses and treatment methods. The search results are used as input data for the next step.

[1131] Step 4:

[1132] The searched similar cases are input into a generative AI model. The cloud server preprocesses the search results using a programming language such as Python and inputs them into a generative AI model (e.g., GPT-4). The AI ​​model then analyzes the search results and generates a second opinion that suggests the optimal treatment method based on the user's symptoms and diagnosis.

[1133] Step 5:

[1134] The generated second opinion is sent to the user terminal. The server encrypts the generated second opinion data and sends it to the user terminal using a secure communication method to protect the user's privacy. This output data is sent as text data of the final second opinion.

[1135] Step 6:

[1136] The results are displayed on the user's device. The received second opinion is displayed on the smart glasses display or smartphone screen. The user can consider additional treatment methods and policies based on the displayed second opinion. Specifically, the results are displayed visually using the smart device's HUD (head-up display) API.

[1137] These steps allow users to quickly and accurately obtain second opinions, improving the quality of diagnosis and treatment in medical settings.

[1138] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1139] The present invention is a system that provides a second opinion by combining an emotion engine based on the symptoms, diagnosis, and treatment methods entered by the user. The program processing of this system is explained in natural language below. Specific examples are also provided for further explanation.

[1140] System Overview

[1141] 1. User Input

[1142] Users input their symptoms, diagnosis results, and treatment options provided by their doctor into the device. The emotion engine then recognizes emotions from the user's input data, and this data is also collected at the same time. For example, if a user enters symptoms such as "chest pain, cough" into the form, and enters a diagnosis of "suspected lung cancer" and a treatment option of "chemotherapy recommended," the emotion engine will recognize emotions such as stress and anxiety from the user's text input.

[1143] 2. Data Transmission

[1144] The device sends the data entered by the user and the emotion data recognized by the emotion engine to the server in JSON format using a secure communication protocol (e.g., HTTPS).

[1145] 3. Database Search

[1146] The server analyzes the received data and generates a query to the database, specifically, to search for similar past diagnoses and treatment cases based on the user's symptoms and diagnosis.

[1147] 4. Analysis of AI models

[1148] The server inputs similar cases obtained as search results into a generative AI model and performs analysis to generate new treatment methods and policies. In addition, recognized emotional data is also provided to the AI ​​model, which adjusts accordingly.

[1149] 5. Sending the results

[1150] The server then sends the generated second opinion back to the user's device. At this time, the second opinion reflects adjustments based on the emotional data. For example, if a user is feeling very anxious, it will highlight treatment options that allow them to consult without rushing.

[1151] 6. Displaying the results

[1152] The device displays the second opinion received from the server in an appropriate format, allowing users to quickly check the second opinion whether they are at home or on the go. Furthermore, the information is presented in a way that takes the user's emotions into consideration, making it easier for them to calmly consider treatment methods and policies.

[1153] Specific examples

[1154] Suppose a patient has symptoms such as "chest pain and cough," is diagnosed by a doctor with "suspected lung cancer," and is recommended chemotherapy as a treatment. When the patient enters this information using a device, the emotion engine recognizes "anxiety" and "stress" from the patient's input. The device sends this information to a server, which searches for similar cases in a database. For example, if a past case with similar symptoms and diagnosis is found, the generative AI model can use this to generate a second opinion that "radiation therapy is likely to be effective." This result is sent from the server to the patient's device, and when it is displayed to the patient on the device, a message to alleviate the patient's anxiety is added.

[1155] As described above, the present invention is a system that allows users to quickly obtain a second opinion from home or on the go, and provides information that takes the user's feelings into consideration, helping them calmly consider the optimal treatment method and policy. This system significantly reduces the time burden and procedural difficulties, and also reduces the psychological burden.

[1156] The processing flow will be explained below.

[1157] Step 1:

[1158] The user enters their symptoms, diagnosis, and proposed treatment into the terminal. Specifically, the user details symptoms such as "chest pain, cough" in the terminal form, and also enters the doctor's diagnosis of "suspected lung cancer" and the treatment recommended, "chemotherapy."

[1159] Step 2:

[1160] The emotion engine recognizes emotions from the user's input data. Specifically, it analyzes the text entered by the user and identifies emotions such as "anxiety" and "stress." This emotion data is also collected along with the user's input data.

[1161] Step 3:

[1162] The device sends the data entered by the user and the emotion data recognized by the emotion engine to the server in JSON format using a secure communication protocol (e.g., HTTPS).

[1163] Step 4:

[1164] The server analyzes the received data and generates a query to the database. Specifically, it creates and executes an SQL query to search for similar past diagnostic results and treatment cases based on the user's symptoms and diagnostic results.

[1165] Step 5:

[1166] The server retrieves similar cases from the database, analyzes the records returned as a result of the query, and lists the cases with the highest similarity.

[1167] Step 6:

[1168] The server inputs the acquired data of similar cases into the generative AI model for analysis. Specifically, it converts the data of similar cases into an input format for the AI ​​model and applies it to the model to generate new treatment methods and policies.

[1169] Step 7:

[1170] The server also provides emotional data from the emotion engine to the AI ​​model, allowing it to adapt the generated second opinion to the user's emotions. For example, if the user is feeling highly anxious, the second opinion will be adjusted to emphasize treatment options that encourage more patient action.

[1171] Step 8:

[1172] The server then sends the generated second opinion to the user's device. Specifically, the server encodes the second opinion in JSON format and sends it to the device using a secure communication protocol.

[1173] Step 9:

[1174] The device analyzes the second opinion received from the server and displays it in an appropriate format. Specifically, it parses the received JSON format data and displays it on the screen as text and graphics that are easy for the user to understand. The user can review this and consider treatment methods and policies.

[1175] Through the above processing steps, users can quickly obtain a second opinion and obtain information to select the most appropriate treatment method and policy. In addition, by using the emotion engine, it is possible to provide information that takes into account the user's psychological state, reducing stress and anxiety during the treatment decision-making process.

[1176] Example 2

[1177] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1178] This invention relates to a system that simultaneously collects and analyzes emotional data when a user inputs their symptoms, diagnosis, and treatment options, and uses this data to provide an optimal second opinion. Conventional systems present diagnosis and treatment options without taking the user's emotional state into consideration, resulting in problems such as inappropriate communication of information or inability to alleviate the user's anxiety. Furthermore, when there are multiple diagnosis and treatment options, comparing and examining each piece of information takes a significant amount of time, placing a significant burden on the user.

[1179] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1180] In this invention, the server includes: means for a user to input symptoms, diagnosis results, and treatment methods; means for analyzing the input data and recognizing emotional data; means for transmitting the analyzed data and emotional data to the server; means for the server to search for similar cases in a database; means for inputting the search results and emotional data into a generative AI model for analysis; means for transmitting the generated second opinion to the user; and means for displaying the transmitted results and adjustments based on the emotions on a user terminal. This allows for the provision of a prompt and appropriate second opinion that takes into consideration the user's emotional state, enabling the user to calmly consider treatment methods and policies.

[1181] "User" means an individual or corporation that uses this system.

[1182] "Symptoms" refer to changes or abnormalities in the user's physical condition.

[1183] "Diagnosis results" refers to medical condition assessments and diagnostic information made by medical professionals based on the user's symptoms.

[1184] "Treatment method" refers to the treatment measures or methods suggested by a medical professional based on the diagnosis results.

[1185] "Emotion data" is data that represents the emotional state recognized from the user's input content.

[1186] A "terminal" is a device (e.g., a PC, smartphone, or tablet) that allows a user to input symptoms, diagnosis results, and treatment methods.

[1187] A "server" is a central processing unit that receives and analyzes data sent by users, manages the database, and executes generative AI models.

[1188] A "database" is an information system that stores and manages past diagnostic results and treatment cases.

[1189] A "query" refers to a search request made to a database.

[1190] A "generative AI model" is an artificial intelligence mechanism that generates new treatment methods and policies based on input data.

[1191] "Second opinion" refers to a separate professional opinion or judgment provided in addition to the diagnosis or treatment method obtained by the user.

[1192] The "JSON format" is a lightweight data exchange format for structuring and representing data.

[1193] "Secure protocol" refers to a communication protocol for securely sending and receiving data, and includes HTTPS.

[1194] "Emotion-based adjustment" refers to modifying and adjusting the generated results to suit the user based on recognized emotional data.

[1195] The present invention provides a system for providing a second opinion based on symptoms, diagnosis results, and treatment methods input by a user, and also combining emotion data. This system includes the following elements:

[1196] The user enters their symptoms, diagnosis, and treatment options provided by the doctor into the device. In the device's form, the user writes down symptoms such as "chest pain, cough," and enters the diagnosis of "suspected lung cancer" and the treatment option of "chemotherapy recommended." In addition, an emotion engine works in the background to recognize emotion data such as "anxiety" and "stress" from the user's input. This emotion data is generated based on the user's text input.

[1197] The device sends the data entered by the user and emotion data to the server. The data is structured in JSON format and transmitted via a secure communication protocol (e.g., HTTPS), which prevents the risk of data eavesdropping or tampering.

[1198] The server analyzes the received data and generates a query for the database. Specifically, it executes a query to search for similar past diagnosis results and treatment cases based on the user's symptoms and diagnosis results. For example, a search using the conditions "chest pain," "cough," and "suspected lung cancer" is performed to retrieve similar past cases.

[1199] The server then inputs the similar cases obtained as search results into a generative AI model (such as GPT-4). The generative AI model then uses this data to perform analysis to generate new treatment methods and policies. Emotional data is also provided to the AI ​​model, and the results are adjusted accordingly. For example, a second opinion is generated that "radiation therapy is likely to be effective," and detailed explanations and success stories are added for users who are feeling stressed.

[1200] The generated second opinion is then sent back to the user's device from the server. At this time, the second opinion reflects adjustments based on the emotional data. For example, if a user is feeling anxious, a message emphasizing treatment options and detailed explanations will be added so that the user can consult calmly.

[1201] The device receives the second opinion from the server and displays it to the user in an appropriate format. Users can quickly check the second opinion at home or on the go. Furthermore, the information is provided in a way that takes emotions into consideration, making it easier to calmly consider treatment methods and policies.

[1202] As a specific example, consider the case where a patient has symptoms such as "chest pain and cough" and is diagnosed by a doctor with "suspected lung cancer" and told that "chemotherapy is recommended." The patient enters this information using a device, and the emotion engine recognizes "anxiety" and "stress" from the input. The device sends this information to a server, which searches for similar cases in a database. For example, if a past case of a patient with similar symptoms is found, the generative AI model may use this to generate a second opinion stating that "radiotherapy is likely to be effective." This result is then sent back to the patient's device, and when displayed to the patient on the device, a message such as "There are many successful cases of radiation therapy, so don't worry" is added.

[1203] An example of a prompt sentence could be in the following format:

[1204] "Generate second opinions based on user-entered symptoms, diagnoses, and treatment options. Include appropriate treatment options and messages, taking into account user sentiment data."

[1205] As described above, the present invention is a system that allows users to quickly obtain a second opinion from home or on the go, and helps them calmly consider the optimal treatment method and policy by providing information that takes the user's emotions into consideration. This system significantly reduces the time burden and procedures, and also reduces the psychological burden.

[1206] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1207] Program processing steps

[1208] Step 1: User enters symptoms, diagnosis, and treatment

[1209] The user enters their symptoms, diagnosis, and treatment method into a dedicated form on the device. The input data includes "chest pain, cough" (symptoms), "suspected lung cancer" (diagnosis), and "chemotherapy recommended" (treatment method), and enters it into the device's form. The device collects this data. Once the input data is ready, the user clicks the "Submit" button.

[1210] Step 2: Analyzing input data and recognizing emotions

[1211] The device analyzes the data entered by the user and recognizes emotional data using an emotion engine. The input data includes symptoms, diagnosis, and treatment methods. The emotion engine uses a text analysis algorithm to extract emotions (e.g., anxiety, stress) contained in the user's input data. The output at this point is symptoms, diagnosis, treatment, and recognized emotional data.

[1212] Step 3: Sending data

[1213] The device sends the analyzed data (symptoms, diagnosis results, treatment methods) and recognized emotion data in JSON format to the server. The HTTPS protocol is used for transmission, ensuring secure data transfer. The input data is the information entered by the user and emotion data, and this information is sent to the server as output.

[1214] Step 4: Receiving and analyzing data

[1215] The server receives data sent from the device. The received data includes symptoms, diagnosis results, treatment methods, and emotion data. The server analyzes this data and generates a query for database search. The received data is the input, and the query for database search is generated as the output.

[1216] Step 5: Database Search

[1217] The server uses the generated query to search for similar cases in the database. The query includes conditions such as "chest pain," "cough," and "suspected lung cancer." The database returns past diagnoses and treatment cases that match these conditions. The search is performed based on the input query, and a list of similar cases is obtained as the output.

[1218] Step 6: Input and analysis into the AI ​​model

[1219] The server inputs similar cases obtained as search results into a generative AI model. The generative AI model performs analysis based on this data to generate new treatment methods and policies. Emotional data is also provided to the AI ​​model, which makes adjustments accordingly. Search result data and emotional data are given as input, and new treatment methods and policies are generated as output.

[1220] Step 7: Generate a second opinion

[1221] The server receives the second opinion generated by the generative AI model and adjusts it based on emotional data. For example, if a second opinion is generated stating that "radiation therapy is likely to be effective," the server adds a message such as "If you feel anxious, we recommend that you seek professional counseling." The generated second opinion is the input, and the adjusted second opinion is the output.

[1222] Step 8: Submit a second opinion

[1223] The server then sends the adjusted second opinion back to the user's device, again using the HTTPS protocol. The adjusted second opinion is given as input and sent to the device as output.

[1224] Step 9: View the results

[1225] The terminal displays the second opinion received from the server to the user in an appropriate format. The user can quickly check this second opinion at home or on the go. The display is done in a way that takes emotions into consideration, for example, by adding a message such as "Don't rush, let's hear other opinions." The second opinion received is the input, and it is displayed to the user as the output.

[1226] (Application example 2)

[1227] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1228] Conventional systems that provide second opinions present diagnosis results and treatment methods without considering the user's feelings, which increases the user's psychological burden. In addition, when users enter medical information in a physical store, a quick and appropriate response is required, but there was no system that could efficiently do this.

[1229] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1230] In this invention, the server includes means for a user to input symptoms, diagnosis results, and treatment methods, means for transmitting the input data and emotion data to the server, means for the server to search for similar cases in a database, means for inputting the search results into a generative AI model for analysis, means for transmitting the generated second opinion to the user, means for displaying the transmitted results on a user terminal, means for adjusting the message based on the emotion data, and means for the user to input information using a tablet terminal or smartphone. This makes it possible to provide second opinions that take the user's emotions into consideration, thereby realizing fast and efficient service provision in physical stores.

[1231] "User" refers to a person who utilizes the system to input symptoms, diagnoses, and treatments.

[1232] "Symptoms" refers to information that indicates the health condition or physical abnormality that the user is experiencing.

[1233] "Diagnosis results" refers to information on evaluations and judgments regarding the user's health condition provided by a doctor or medical institution.

[1234] "Treatment method" refers to the means or measures proposed by a medical institution to improve the user's health condition.

[1235] "Emotion data" refers to information that indicates a subjective emotional state extracted from the user's input.

[1236] "Server" refers to a central system that receives input data from users and processes it, such as by analyzing, searching, or generating.

[1237] "Database" refers to data storage within a system that stores information such as past diagnostic results and treatment cases.

[1238] A "generative AI model" refers to an artificial intelligence algorithm that generates new diagnostic results and treatment methods based on past cases and input data.

[1239] "Tablet device" refers to a portable electronic device that users use to input information such as symptoms, diagnosis, and treatment.

[1240] The term "smartphone" refers to a portable communication terminal device that is used by users to input information, similar to a tablet terminal.

[1241] "Search results" refer to new treatment methods or second opinions analyzed by the generative AI model based on similar cases in the database.

[1242] A "secure communication protocol" refers to a communication method for securely transmitting user input data to a server.

[1243] "User terminal" refers to an electronic device used by a user to receive and display the results of a second opinion.

[1244] The "means for adjusting a message" refers to a method for providing information in a form appropriate for a user based on emotional data.

[1245] This invention is a system that allows users to input their symptoms, diagnosis results, and treatment methods using a portable electronic device such as a tablet or smartphone, and provides second opinions based on that information. This system analyzes emotional data and proposes new treatment methods that take the user's psychological state into consideration.

[1246] Hardware and Software Configuration

[1247] Hardware

[1248] Tablets and smartphones: Handheld electronic devices that allow users to input information.

[1249] Server: A central system that receives user input data and performs analysis and database lookup.

[1250] software

[1251] Emotion engine: An algorithm that analyzes emotional data from user input.

[1252] Database: Data storage where similar cases and treatment cases are kept.

[1253] Generative AI model: An artificial intelligence algorithm that generates new treatment methods based on input data and past cases.

[1254] Communication protocol: A protocol for secure communication, such as HTTPS.

[1255] Program processing

[1256] User Input

[1257] Users use a tablet or smartphone to input their symptoms, diagnosis results, and treatment options. The input data is analyzed in real time by an emotion engine, and emotional data such as "anxiety" and "stress" is collected.

[1258] Sending data to the server

[1259] The device sends the data entered by the user and the emotion data together in JSON format to the server. This communication is performed using the HTTPS protocol, ensuring data security.

[1260] Database Search and Analysis

[1261] The server performs a database search based on the received data. Specifically, it executes queries based on the user's symptoms and diagnosis results to extract similar past cases.

[1262] Generative AI model for generating treatment methods

[1263] The server inputs the search results obtained from the database into the generative AI model, which analyzes and generates new treatment methods and policies. At this time, emotional data is also provided to the AI ​​model, so it can make adjustments according to the user's psychological state.

[1264] Sending and displaying results

[1265] The generated second opinion is then sent back to the user's device, which then displays the results in an appropriate format. For example, a user who is feeling anxious might be presented with a relaxing message or be given a treatment option to consider without rushing.

[1266] Specific examples

[1267] Consider a case where a patient has symptoms such as "chest pain" and "cough" and is diagnosed with "suspected lung cancer," and is advised to undergo chemotherapy. The patient enters this information using a tablet device. The emotion engine recognizes "anxiety" and "stress" from the patient's input. The device sends this information to a server, which searches for similar cases and uses a generative AI model to generate a second opinion that "radiation therapy is likely to be effective." This result is sent from the server to the patient's device and displayed along with a message to alleviate the patient's anxiety.

[1268] Prompt Sentence Examples

[1269] Symptoms: chest pain, cough

[1270] Diagnosis: Suspected lung cancer

[1271] Suggested treatment: Chemotherapy is recommended

[1272] Emotional data: anxiety, stress

[1273] Generate new treatment options and advice based on previous similar cases and treatment outcomes.

[1274] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1275] Program processing flow

[1276] Step 1:

[1277] Enter user information

[1278] Users use a tablet or smartphone to input their symptoms, diagnosis results, and treatment methods.

[1279] Input: Symptoms, diagnosis, treatment.

[1280] Output: The input data.

[1281] This data is analyzed in real time by an emotion engine to generate the user's emotion data (e.g., anxiety, stress).

[1282] Input: Data entered.

[1283] Output: Emotion data.

[1284] How it works: A user enters their information into the application's input form, which is then analyzed by the emotion engine to generate emotion data.

[1285] Step 2:

[1286] Sending data to the server

[1287] The device sends the data entered by the user and emotion data to the server in JSON format.

[1288] Input: Input data and emotion data (JSON format).

[1289] Output: The data sent to the server.

[1290] How it works: The device uses the HTTPS protocol to send data to the server over a secure connection.

[1291] Step 3:

[1292] Database search

[1293] The server analyzes the received data and generates queries against a database.

[1294] Input: User data and emotion data sent to the server.

[1295] Output: Similar cases retrieved from the database.

[1296] How it works: Based on the symptoms and diagnosis provided by the user, the server queries the database to search for similar past cases.

[1297] Step 4:

[1298] Analysis using generative AI models

[1299] The server inputs similar cases obtained as search results into a generative AI model to generate new treatment methods and policies.

[1300] Input: Similar cases retrieved from the database.

[1301] Output: Generated new treatment methods and second opinions.

[1302] How it works: Using a generative AI model, new treatment methods and policies are generated based on input similar cases and emotional data. For example, a new treatment option such as "radiation therapy is likely to be effective" is generated.

[1303] Step 5:

[1304] Sending the results

[1305] The server transmits the generated second opinion to the user's terminal.

[1306] Input: Generated second opinions and tailored messages.

[1307] Output: Data sent to the user's device.

[1308] How it works: The information generated by the server is sent back to the user's device in JSON format, along with a message based on the emotion data.

[1309] Step 6:

[1310] Displaying the results

[1311] The terminal displays the second opinion received from the server in an appropriate format.

[1312] Input: The second opinion and message sent by the server.

[1313] Output: Information displayed to the user.

[1314] How it works: The user can view the generated second opinion on their device. For anxious users, the message includes tips on how to relax and deal with the situation calmly.

[1315] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1316] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1317] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1318] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1319] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1320] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1321] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1322] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1323] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1324] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1325] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1326] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1327] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1328] 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.

[1329] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1330] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1331] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1332] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1333] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1334] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1335] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1336] The following is further disclosed regarding the above embodiment.

[1337] (Claim 1)

[1338] a means for a user to input symptoms, diagnoses, and treatments;

[1339] means for transmitting the input data to a server;

[1340] A means for the server to search for similar cases in the database;

[1341] A means of inputting search results into a generative AI model for analysis;

[1342] means for transmitting the generated second opinion to a user;

[1343] means for displaying the transmitted results on a user terminal;

[1344] A system including:

[1345] (Claim 2)

[1346] 2. The system of claim 1, wherein the means for searching for similar cases in the database executes a query based on the user's symptoms and diagnosis results.

[1347] (Claim 3)

[1348] The system of claim 1, further comprising means for generating a new treatment method or policy based on data input into the generative AI model.

[1349] "Example 1"

[1350] (Claim 1)

[1351] a means for a user to input symptoms, diagnoses, and treatments;

[1352] means for transmitting the input data to a server;

[1353] A means for the server to search for similar cases in the database;

[1354] A means of inputting search results into a generative AI model for analysis;

[1355] means for transmitting the generated second opinion to a user;

[1356] means for displaying the transmitted results on a user terminal;

[1357] A means for providing the input data as a prompt sentence to the generative AI model;

[1358] a means for encrypting and transmitting the generated second opinion;

[1359] A system including:

[1360] (Claim 2)

[1361] 2. The system of claim 1, wherein the means for searching for similar cases in the database executes a query based on the user's symptoms and diagnosis results.

[1362] (Claim 3)

[1363] The system of claim 1, further comprising means for generating a new treatment method or policy based on data input into the generative AI model.

[1364] "Application Example 1"

[1365] (Claim 1)

[1366] a means for a user to input symptoms, diagnoses, and treatments;

[1367] means for transmitting the input data to a server;

[1368] A means for the server to search for similar cases in the database;

[1369] A means of inputting search results into a generative AI model for analysis;

[1370] means for transmitting the generated second opinion to a user;

[1371] means for displaying the transmitted results on a user terminal;

[1372] means for accepting voice input using a smart device;

[1373] means for transmitting voice-input data to a cloud server;

[1374] a means for displaying the generated second opinion on a smart device;

[1375] A system including:

[1376] (Claim 2)

[1377] 2. The system of claim 1, wherein the means for searching for similar cases in the database executes a query based on the user's symptoms and diagnosis results.

[1378] (Claim 3)

[1379] The system of claim 1, further comprising means for generating a new treatment method or policy based on data input into the generative AI model.

[1380] "Example 2: Combining Emotion Engines"

[1381] (Claim 1)

[1382] a means for a user to input symptoms, diagnoses, and treatments;

[1383] A means for analyzing input data and recognizing emotion data;

[1384] means for transmitting the analyzed data and emotion data to a server;

[1385] A means for the server to search for similar cases in the database;

[1386] A means for inputting search results and sentiment data into a generative AI model for analysis;

[1387] means for transmitting the generated second opinion to a user;

[1388] means for displaying the transmitted results and emotion-based adjustments at the user terminal;

[1389] A system including:

[1390] (Claim 2)

[1391] 2. The system of claim 1, wherein the means for searching for similar cases in the database executes a query based on the user's symptoms and diagnosis results.

[1392] (Claim 3)

[1393] 10. The system of claim 1, further comprising means for generating a new treatment method or policy based on the data and emotion data input into the generative AI model.

[1394] "Application example 2 when combining emotion engines"

[1395] (Claim 1)

[1396] a means for a user to input symptoms, diagnoses, and treatments;

[1397] means for transmitting the input data and emotion data to a server;

[1398] A means for the server to search for similar cases in the database;

[1399] A means of inputting search results into a generative AI model for analysis;

[1400] means for transmitting the generated second opinion to a user;

[1401] means for displaying the transmitted results on a user terminal;

[1402] means for tailoring messages based on emotion data;

[1403] A means for users to input information using a tablet or smartphone;

[1404] A system including:

[1405] (Claim 2)

[1406] 2. The system of claim 1, wherein the means for searching for similar cases in the database executes a query based on the user's symptoms and diagnosis results.

[1407] (Claim 3)

[1408] The system of claim 1, further comprising means for generating a new treatment method or policy based on data input into the generative AI model. [Explanation of symbols]

[1409] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for a user to input symptoms, diagnoses, and treatments; means for transmitting the input data to a server; A means for the server to search for similar cases in the database; A means of inputting search results into a generative AI model for analysis; means for transmitting the generated second opinion to a user; means for displaying the transmitted results on a user terminal; A system including:

2. 2. The system of claim 1, wherein the means for searching for similar cases in the database executes a query based on the user's symptoms and diagnosis.

3. The system of claim 1 further comprising means for generating a new treatment method or policy based on data input into the generative AI model.

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