system

Abstract

We provide the system. [Solution] A means of receiving questions entered from the user's terminal, A means for inputting the received question into a generative artificial intelligence model and generating an answer, means for transmitting the generated response to the user terminal, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] When conducting medical consultations online, conventionally, it has been necessary to rely on manpower, and there has been a problem that a large amount of time and resources of specialized medical staff are required. There has also been a problem that it is difficult to obtain quick, highly accurate, and consistent answers to the diversity of consultation contents. The present invention aims to solve these problems and provides a system that enables more efficient and highly accurate medical consultations.

Means for Solving the Problems

[0005] This invention includes means for receiving questions entered from a user terminal, means for inputting the received questions into a generative artificial intelligence model to generate answers, and means for transmitting the generated answers to the user terminal. This system minimizes the intervention of specialized medical staff in online medical consultations, enabling generative artificial intelligence to provide rapid and highly accurate answers. Furthermore, by utilizing a smartphone or personal computer, this system provides a user-friendly interface and allows for efficient medical consultations via a network.

[0006] A "user terminal" is a device that a user accesses to input and receive information, and includes devices such as smartphones or personal computers.

[0007] "Means for receiving questions" refers to a function or device for receiving questions entered from a user terminal within a server or system.

[0008] A "generative artificial intelligence model" is an artificial intelligence program or system that uses technologies such as natural language processing and machine learning to generate appropriate responses to input text.

[0009] "Means for generating answers" refers to a function or device for inputting a received question into a generative artificial intelligence model and generating an answer based on that input.

[0010] "Means for sending responses" refers to a function or device for sending and displaying the generated responses on the user's terminal.

[0011] "System" refers to the overall structure and network in which multiple functions and devices, such as user terminals, servers, and generative artificial intelligence models, work together to efficiently provide medical consultations. [Brief explanation of the drawing]

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

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

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

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

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

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

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

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

[0020] [First Embodiment]

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

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

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

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

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

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

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

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

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

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

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0033] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and returning the answers to the user terminal. This system includes a server, a user terminal, and a generative artificial intelligence model.

[0034] First, users use their smartphones or personal computers to input questions about their symptoms or medical needs. For example, a question like, "I have a headache, what should I do?" might be entered. This entered question is then sent from the device to the server.

[0035] Next, the server inputs the question received from the user's terminal into a generative artificial intelligence model. This generative AI model uses natural language processing technology to generate appropriate medical knowledge-based answers to the input question. For example, if the question is "I have a headache, what should I do?", the generative AI model will generate an answer such as, "First, it is important to drink plenty of fluids. I recommend that you rest and get more rest than usual. If your symptoms do not improve or if other symptoms appear, please consider seeing a doctor."

[0036] The server then sends the generated response to the device. The receiving device displays the response to the user. For example, if the user asks the question mentioned earlier, the device will display: "AI response: First, it is important to stay well-hydrated. We recommend resting and getting more rest than usual. If your symptoms do not improve or if other symptoms appear, please consider seeing a doctor."

[0037] This entire process allows users to quickly obtain highly accurate answers to their medical inquiries. Furthermore, it enables consistent responses to numerous questions while minimizing the need for specialized medical staff, thus providing an efficient medical consultation service.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user enters their symptoms or medical questions on the device. For example, the user might enter, "I have a headache, what should I do?"

[0041] Step 2:

[0042] The terminal sends user input to the server. During this process, the entered data is transferred to the server via the network.

[0043] Step 3:

[0044] The server processes the questions received from the terminal. The server prepares the received question data to pass to a generative artificial intelligence model.

[0045] Step 4:

[0046] The server inputs the received question into a generative artificial intelligence model, which then generates an answer based on the question. This generative AI model utilizes natural language processing techniques to produce answers based on appropriate medical knowledge. For example, the model might generate an answer such as, "When you have a headache, it's important to drink plenty of fluids. It's recommended that you rest and get more rest than usual. If your symptoms don't improve or if other symptoms appear, consider seeing a doctor."

[0047] Step 5:

[0048] The server sends the generated response to the terminal. During this process, the generated response data is transferred to the terminal via the network.

[0049] Step 6:

[0050] The device displays the answer it received from the server to the user. For example, if the user asks the question mentioned earlier, the device will display: "AI answer: When you have a headache, it is important to drink plenty of fluids. It is recommended that you rest and get more rest than usual. If your symptoms do not improve or if other symptoms appear, please consider seeing a doctor."

[0051] Through this series of steps, users can quickly receive highly accurate medical advice in response to their questions. This system enables the efficient provision of medical consultation services.

[0052] (Example 1)

[0053] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0054] Traditional medical consultation systems struggled to provide quick and accurate answers to a large number of user inquiries. Furthermore, the reliance on specialized medical staff meant limited personnel resources and high costs. Additionally, the inconsistent user experience on user terminals, including the cumbersome process of entering questions and verifying answers, was a significant challenge.

[0055] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0056] In this invention, the server includes means for receiving questions entered from a user terminal, means for inputting the questions into a generative artificial intelligence model and generating answers, means for transmitting the generated answers to the user terminal, and means for displaying the answers on the user terminal. This not only provides quick and accurate answers to a large number of questions, but also minimizes the need for intervention by specialized medical staff, thereby reducing costs. Furthermore, users can obtain a consistent user experience, and it becomes easier to input questions and confirm answers.

[0057] A "user terminal" is a device used by a user to input questions and receive answers, and includes personal digital assistants and computers.

[0058] A "server" is a computing system that receives questions sent from a user terminal, inputs those questions into a generative artificial intelligence model, and then sends the generated answers back to the user terminal.

[0059] A "generative artificial intelligence model" refers to an artificial intelligence algorithm that uses natural language processing technology to generate appropriate answers to input questions.

[0060] A "question" is information that includes medical questions and descriptions of symptoms entered by the user, transmitted from the user's terminal to the server.

[0061] "Answer" refers to information, including solutions and advice, to a user's question, which is generated by a generative artificial intelligence model and sent to the user's terminal via a server.

[0062] "Natural language processing technology" refers to the techniques used by generative artificial intelligence models to understand questions and generate appropriate answers in human language.

[0063] "Means of receiving" refers to the collective functions and interfaces that a server uses to receive questions sent from a user's terminal and process them as information.

[0064] "Means of transmission" refers to the collective functions and interfaces that the server uses to send the generated response back to the user's terminal.

[0065] "Means of display" refers to the collective term for functions and interfaces that visually present the responses received by the user's terminal to the user.

[0066] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and returning the answers to the user terminal. This system includes a server, a user terminal, and a generative artificial intelligence model.

[0067] First, the user uses a user device such as a smartphone or personal computer to input questions about their symptoms or medical issues. For example, they might input a question like, "My throat has been sore since yesterday, what should I do?" This entered question is then sent from the device to the server.

[0068] Next, the server inputs the question received from the user's terminal into a generative artificial intelligence model. In this case, a model utilizing natural language processing technology (for example, OpenAI® GPT-3®) is used as the generative artificial intelligence model. The server sends the received question to the generative artificial intelligence model as part of a prompt. For example, the prompt might be: "A user has entered a medical consultation question. The question is 'My throat has been sore since yesterday, what should I do?' Please generate an appropriate answer to this question."

[0069] Generative artificial intelligence models produce appropriate answers to input questions. For example, if the question is "My throat has been sore since yesterday, what should I do?", the generative AI model will produce an answer such as, "If you have a sore throat, first we recommend drinking warm tea or water with honey. Also, gargle and, if necessary, try using over-the-counter throat lozenges or pain relievers. If symptoms persist for more than three days or worsen, please consider consulting a medical professional."

[0070] The server then sends the generated response to the user's device. The receiving device displays the response to the user. For example, if the user asks the question mentioned earlier, the device will display: "AI response: If you have a sore throat, we recommend drinking warm tea or water with honey first. Also, gargle and use commercially available throat lozenges or pain relievers if necessary. If symptoms persist for more than 3 days or worsen, please consider consulting a medical professional."

[0071] This entire process allows users to quickly obtain highly accurate answers to their medical inquiries. Furthermore, it enables consistent responses to numerous questions while minimizing the need for specialized medical staff, thus providing an efficient medical consultation service.

[0072] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0073] Step 1:

[0074] The user enters the question. The user uses their device (smartphone or personal computer) to enter the question into a text input field on a dedicated application or web browser and presses the submit button. The user's medical questions are provided as input, and the question text is generated as output.

[0075] Step 2:

[0076] The terminal sends a question to the server. The terminal sends the question text data to the server as an HTTP POST request. It receives the question text data as input and generates a request that is sent to the server as output.

[0077] Step 3:

[0078] The server receives the question. The server receives the HTTP POST request sent from the terminal and retrieves the question text. It receives the request from the terminal as input and extracts the question text as output.

[0079] Step 4:

[0080] The server inputs the question into a generative artificial intelligence model. The server sends the received question text along with a prompt to the generative artificial intelligence model as an API request. For example, the prompt might be in the format: "A user has entered a medical consultation question. The question is: 'My throat has been sore since yesterday, what should I do?' Please generate an appropriate answer to this question." It receives the question text as input and generates an API request as output.

[0081] Step 5:

[0082] A generative artificial intelligence model generates the answer. The generative AI model uses natural language processing techniques based on the received prompt to generate appropriate answer text. For example, if the question is "My throat has been sore since yesterday, what should I do?", it will generate the following answer: "If you have a sore throat, first we recommend drinking warm tea or water with honey. Also, gargle and, if necessary, try using over-the-counter throat lozenges or pain relievers. If symptoms persist for more than three days or worsen, please consider consulting a medical professional." It takes a prompt as input and generates answer text as output.

[0083] Step 6:

[0084] The server receives the generated response. The server receives the response text as an API response from the generative artificial intelligence model. It receives the response from the generative artificial intelligence model as input and obtains the response text as output.

[0085] Step 7:

[0086] The server sends the response to the terminal. The server sends the retrieved response text to the terminal as an HTTP response. It receives the response text as input and generates a response that is sent to the terminal as output.

[0087] Step 8:

[0088] The device displays the answer to the user. The device displays the received answer text on the screen. For example, it might display text such as, "AI response: If you have a sore throat, we recommend first drinking warm tea or water with honey. Also, gargle and use commercially available throat lozenges or pain relievers if necessary. If symptoms persist for more than 3 days or worsen, consider consulting a medical professional." It receives the answer text as input and displays it as output.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] The present invention aims to alleviate the anxieties and questions users have when making electronic payments and to provide a safe and secure payment environment. In particular, there is a need for a method to provide prompt and appropriate answers to questions related to health and safety. Modern consumers often have questions, especially regarding health, when making payments, and by responding quickly to these questions, consumer satisfaction can be improved.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes means for receiving questions entered from a user terminal, means for inputting the received questions into a generative artificial intelligence model to generate answers, and means for transmitting the generated answers to the user terminal. This makes it possible to generate quick and appropriate answers to health and safety-related questions when making electronic payments at the user terminal, thereby alleviating consumer anxiety and providing a safe and secure payment environment.

[0094] A "user terminal" is a device used by a user to input questions or make electronic payments, and can be a smartphone or a personal computer.

[0095] A "question" refers to any doubts or concerns entered by the user, particularly those related to health and safety.

[0096] A "generative artificial intelligence model" is a model that uses natural language processing technology to generate appropriate answers to input questions.

[0097] A "server" is a device that receives questions from a user's terminal, inputs them into a generative artificial intelligence model, and sends the generated answers back to the user's terminal.

[0098] An "answer" refers to the response or advice generated by a generative artificial intelligence model based on a question.

[0099] "Electronic payment" refers to the act of a user paying for goods or services online or offline using digital means.

[0100] This invention is a system designed to alleviate the anxieties and questions users may have when making electronic payments. This system includes a user terminal, a server, and a generative artificial intelligence model.

[0101] 1. User terminal

[0102] The user terminal is a smartphone or personal computer, and it functions as an interface for the user to input questions. For example, when a user makes a payment, they might input, "Is it safe to use this payment method?"

[0103] 2. Server

[0104] The server plays a central role in inputting questions received from user terminals into a generative artificial intelligence model and generating answers. Specifically, the server uses a web framework such as Flask to build a RESTful API, receive questions, and send them to the AI ​​model.

[0105] 3. Generative Artificial Intelligence Models

[0106] Generative artificial intelligence models use natural language processing techniques to generate appropriate answers based on input questions. For example, in response to the question, "Is this payment method safe to use?", it might generate the answer, "This payment method meets our strict security standards and can be used safely." This AI model is built on advanced natural language processing models such as BERT and GPT.

[0107] 4. Data processing and calculations

[0108] When a user enters a question on their device, that question is sent to the server. The server receives the question and inputs it into a generative artificial intelligence model via an API request. The generative AI model analyzes the input question and generates an appropriate answer. The generated answer is then sent back to the user's device via the server and presented to the user.

[0109] Specific example

[0110] When a user is shopping using the "Careful Pay" app, they can instantly enter the question, "Is it safe to use this payment method?" and the AI ​​within the app will respond, "This payment method meets our strict security standards and can be used safely."

[0111] Example of a prompt

[0112] Input: "Is this payment method safe to use?"

[0113] Generated response: "This payment method meets our strict security standards and can be used safely."

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

[0115] Step 1:

[0116] The user enters the question on their device.

[0117] Operation: The user uses a dedicated application on their smartphone or personal computer to enter a question, for example, "Is it safe to use this payment method?"

[0118] Input: The question entered by the user.

[0119] Output: The user's terminal generates the question data.

[0120] Step 2:

[0121] The terminal sends the entered question to the server.

[0122] Operation: The user's terminal sends the entered question data to the server as an HTTP request via an internet connection.

[0123] Input: Question data generated by the user's terminal.

[0124] Output: The server receives the question data.

[0125] Step 3:

[0126] The server inputs the received questions into a generative artificial intelligence model.

[0127] Operation: The server uses a web framework such as Flask to send the received question data as an API request to a generative artificial intelligence model.

[0128] Input: Question data received by the server.

[0129] Output: The generative artificial intelligence model obtains input data to analyze the question.

[0130] Step 4:

[0131] A generative artificial intelligence model generates an answer based on the question.

[0132] Operation: A generative artificial intelligence model (e.g., BERT or GPT) analyzes the question using natural language processing techniques and generates an appropriate answer. For example, it might generate an answer such as, "This payment method meets our strict security standards and can be used safely."

[0133] Input: Question data for analysis by a generative artificial intelligence model.

[0134] Output: Response data generated by a generative artificial intelligence model.

[0135] Step 5:

[0136] The server receives the generated response and sends it to the user's terminal.

[0137] Operation: The server sends the response data received from the generative artificial intelligence model to the user's terminal as an HTTP response.

[0138] Input: Response data generated by a generative artificial intelligence model.

[0139] Output: Response data received by the user's terminal.

[0140] Step 6:

[0141] The user's device displays the answer to the user.

[0142] Operation: The user's device displays the received response data in the user interface. For example, if the user is using the "Careful Pay" app, the app will display the response "This payment method meets our strict security standards and can be used safely" on the screen.

[0143] Input: Response data received by the user's terminal.

[0144] Output: The answer displayed to the user.

[0145] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0146] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and further combining this with an emotion engine that recognizes the user's emotions, before returning the answers to the user terminal. This system includes a server, a user terminal, a generative artificial intelligence model, and an emotion engine.

[0147] First, the user uses a smartphone or personal computer to input questions about their symptoms or medical issues. For example, the user might input, "I have a headache, what should I do?" This entered question is then sent from the device to the server.

[0148] Next, the server first inputs the question received from the user's terminal into the emotion engine. The emotion engine analyzes the text entered by the user and determines the user's emotional state. For example, if the user's question contains the emotion of "urgent," the emotion engine recognizes this and determines the emotional state to be "urgent."

[0149] The server then considers the emotional state determined by the emotion engine and inputs the received question into a generative artificial intelligence model to generate an answer. This generative AI model uses natural language processing technology to produce an appropriate answer based on medical knowledge in response to the input question. For example, if the question is "I have a headache, what should I do?" and the emotional state is determined to be "urgent," the generative AI model will generate an answer such as, "First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to drink plenty of fluids and rest."

[0150] Next, the server sends the generated answer to the device. The receiving device then displays the answer to the user. For example, if the user asks the question mentioned earlier, the device will display the answer: "AI answer: First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay hydrated and rest."

[0151] This entire process allows users to receive highly accurate medical advice quickly and with appropriate responses that take their emotions into consideration. This system enables the provision of efficient and accurate medical consultation services that also consider the user's emotional state.

[0152] The following describes the processing flow.

[0153] Step 1:

[0154] The user enters their symptoms or medical questions on the device. For example, the user might enter, "I have a headache, what should I do?"

[0155] Step 2:

[0156] The terminal sends the user's entered question data to the server. Here, the data is transmitted over the network.

[0157] Step 3:

[0158] The server inputs the question received from the terminal into the sentiment engine. The sentiment engine analyzes the question text and determines the user's emotional state.

[0159] Step 4:

[0160] The server prepares input data for the generative artificial intelligence model based on the emotion determination results from the emotion engine. For example, if the user's emotion is determined to be "anxiety," that information is added to the generative artificial intelligence model.

[0161] Step 5:

[0162] The server inputs question data and sentiment information into a generative artificial intelligence model to generate an answer. For example, if the question is "I have a headache, what should I do?" and the user is feeling "anxious," the generative AI model will generate an answer such as, "First, calm down, take a deep breath, and drink plenty of water. If the headache persists, please see a doctor."

[0163] Step 6:

[0164] The server sends the generated response data to the terminal. Here too, the data is transmitted over the network.

[0165] Step 7:

[0166] The device displays the response received from the server to the user. For example, the user's device might display the response: "AI response: First, calm down, take a deep breath, and drink plenty of water. If your headache persists, please consult a medical professional."

[0167] Through these processing steps, highly accurate medical consultations that take the user's emotions into account are provided quickly. This system enables efficient medical consultation services by providing appropriate advice tailored to the user's emotional state.

[0168] (Example 2)

[0169] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0170] Traditional medical consultation systems often provided answers without considering the user's emotional state, resulting in inappropriate responses tailored to the user's psychological condition. Consequently, users did not receive the support and advice they truly needed, leading to increased dissatisfaction and anxiety.

[0171] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a question input from a user terminal, means for analyzing the received question using emotion analysis means to determine the emotional state, means for inputting the determined emotional state and the question into a generative artificial intelligence model to generate an answer, and means for transmitting the generated answer to the user terminal. This makes it possible to provide appropriate medical consultation answers that take into account the user's emotional state.

[0172] A "user terminal" refers to an electronic device used by a user to input questions, such as a smartphone or personal computer.

[0173] A "server" refers to a central processing unit that processes questions received from user terminals and performs sentiment analysis and response generation.

[0174] "Emotional analysis means" refers to technical methods for analyzing text data entered by users to determine the user's emotional state (e.g., urgency, anxiety, doubt).

[0175] A "generative artificial intelligence model" refers to an artificial intelligence model that uses natural language processing techniques to generate appropriate answers to input questions.

[0176] "Answer generation means" refers to a technical means for inputting a question and emotional state into a generative artificial intelligence model to generate an appropriate medical answer.

[0177] "Questions" refer to text data related to medical consultations that users input through their devices.

[0178] "Answer" refers to text data generated by a generative artificial intelligence model, containing appropriate responses and advice to the user's question.

[0179] "Emotional state" refers to states such as urgency, anxiety, and relief, which are obtained by analyzing the emotions contained in the questions entered by the user.

[0180] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and further combining this with an emotion engine that recognizes the user's emotions, before returning the answers to the user terminal. This system includes a server, a user terminal, a generative artificial intelligence model, and an emotion engine.

[0181] First, the user uses a smartphone or personal computer to input questions about their symptoms or medical issues. For example, the user might input, "I have a headache, what should I do?" This entered question is then sent from the device to the server.

[0182] Next, the server first inputs the question received from the user's terminal into the emotion engine. The emotion engine analyzes the text entered by the user and determines the user's emotional state. For example, if the user's question contains the emotion of "urgent," the emotion engine recognizes this and determines the emotional state to be "urgent."

[0183] The server then considers the emotional state determined by the emotion engine and inputs the received question into a generative artificial intelligence model to generate an answer. This generative AI model uses natural language processing technology (e.g., OpenAI GPT-4®) to produce an appropriate answer based on medical knowledge in response to the input question. For example, if the question is "I have a headache, what should I do?" and the emotional state is determined to be "urgent," the generative AI model will generate the answer "First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to drink plenty of fluids and rest."

[0184] Next, the server sends the generated answer to the device. The receiving device then displays the answer to the user. For example, if the user asks the question mentioned earlier, the device will display the answer: "AI answer: First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay hydrated and rest."

[0185] The following is an example of a prompt statement that demonstrates the specific operation of this system:

[0186] A user asked: "I have a headache, what should I do?"

[0187] The emotional state was determined to be "urgent."

[0188] --- Please generate an AI response below ---

[0189] answer:

[0190] This entire process allows users to receive highly accurate medical advice quickly and with appropriate responses that take their emotions into consideration. This system enables the provision of efficient and accurate medical consultation services that also consider the user's emotional state.

[0191] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0192] Step 1:

[0193] Users use their smartphones or personal computers to enter questions about medical consultations into a text box. For example, they might enter a question like, "I have a headache, what should I do?"

[0194] Input: Text question (user input)

[0195] Output: Input text question

[0196] Step 2:

[0197] When the user presses the "Send" button, the device begins the process of sending the entered text data to the server. Specifically, it uses the TCP / IP protocol to generate an HTTP POST request, packages the entered text data in JSON format, and sends it to the server over the internet.

[0198] Input: Entered text question (by pressing the send button on the device)

[0199] Output: Text data in JSON format sent to the server

[0200] Step 3:

[0201] The server parses the received HTTP POST request and extracts the text data. Specifically, it uses a JSON parser to extract the text data.

[0202] Input: Submitted text data in JSON format

[0203] Output: Extracted text data

[0204] Step 4:

[0205] The server sends the analyzed text data to the sentiment engine and begins sentiment analysis. Specifically, it calls the sentiment engine's API to perform sentiment analysis on the text.

[0206] Input: Extracted text data

[0207] Output: API request to the emotion engine

[0208] Step 5:

[0209] The emotion engine analyzes text and determines the user's emotional state. Specifically, it uses natural language processing algorithms to assign emotion labels (such as urgency, anxiety, and relief).

[0210] Input: API request to the emotion engine (text data)

[0211] Output: Determined emotion label (e.g., "Urgent")

[0212] Step 6:

[0213] The server considers the emotion labels obtained from the emotion engine and inputs them, along with the question, as prompts to the generative artificial intelligence model. Specifically, it calls the API of the generative AI model (e.g., GPT-4) to generate prompts in the following format:

[0214] User question: "I have a headache, what should I do?"

[0215] Emotional state: "Urgent"

[0216] --- Please generate an AI response below ---

[0217] answer:

[0218] Input: Question and sentiment label

[0219] Output: Prompts for the generative artificial intelligence model

[0220] Step 7:

[0221] Generative artificial intelligence models generate appropriate responses based on prompts. Specifically, they generate text and create responses based on the medical knowledge the model has learned.

[0222] Input: Prompt text for a generative artificial intelligence model

[0223] Output: Generated response (Example: "First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay well-hydrated and rest.")

[0224] Step 8:

[0225] The server receives the generated response and sends it to the user's terminal as an HTTP response. Specifically, it packages the generated response in JSON format and sends the response.

[0226] Input: Generated answer

[0227] Output: HTTP response to the user's terminal

[0228] Step 9:

[0229] The device displays the received response to the user. Specifically, it uses a module for displaying text on the screen to show the response: "AI response: First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay hydrated and rest."

[0230] Input: HTTP response (generated answer)

[0231] Output: Text response displayed to the user

[0232] (Application Example 2)

[0233] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0234] Conventional medical consultation systems often generate simple responses that do not take into account the user's emotional state, sometimes failing to provide appropriate support. Furthermore, they are not designed for use in vehicles, making it impossible to provide health consultation services during long-distance travel. Therefore, there is a need to develop a system that considers the user's emotional state and provides highly accurate medical advice via in-vehicle terminals.

[0235] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0236] In this invention, the server includes means for receiving questions entered from a user terminal; means for inputting the received questions into an emotion engine and determining the user's emotional state; means for inputting the received questions into a generative artificial intelligence model, taking into account the emotional state determined by the emotion engine, and generating an answer; means for transmitting the generated answer to the user terminal; and means including a technique for transmitting questions entered from a tablet or smartphone terminal used as an in-vehicle terminal to an in-vehicle cloud server, and executing processing on the cloud server using the emotion engine and the generative artificial intelligence model. This makes it possible to easily provide appropriate medical consultations that reflect the user's emotional state even while in a vehicle.

[0237] "User terminal" refers to a smartphone, tablet, or similar device used by the user to input and receive questions.

[0238] An "emotion engine" refers to a system that analyzes text entered by a user and determines the user's emotional state.

[0239] A "generative artificial intelligence model" refers to an artificial intelligence model that generates appropriate answers using natural language processing techniques based on questions received from users.

[0240] "In-vehicle terminal" refers to a tablet or smartphone device used inside a vehicle.

[0241] A "cloud server" refers to a server that exists on the internet and provides data processing and storage functions.

[0242] A "question" refers to the content of an inquiry that a user submits to the system via an input terminal.

[0243] "Answer" refers to the response that a generative artificial intelligence model generates in response to a question and provides to the user.

[0244] This invention relates to a system that automatically processes questions transmitted from a user terminal and returns appropriate answers through a combination of a generative artificial intelligence model and an emotion engine. This system includes a server, an in-vehicle terminal (such as a smartphone or tablet), a generative artificial intelligence model, and an emotion engine.

[0245] Users enter questions using an in-vehicle terminal. For example, a passenger might enter, "I'm tired, what should I do?" This question is sent from the in-vehicle terminal to a cloud server. The cloud server processes the question using the following hardware and software.

[0246] Hardware to use

[0247] In-vehicle tablet devices or smartphone devices (e.g., ANDROID® devices)

[0248] Internet connection

[0249] Cloud servers (e.g., AWS®, Google® Cloud)

[0250] Software to use

[0251] Emotion engine (e.g., Microsoft® Azure® Sentiment Analysis API)

[0252] Generative artificial intelligence models (e.g., GPT-3, BERT)

[0253] Data processing and data calculation

[0254] 1. Receipt of submitted questions

[0255] When a user enters a question into an in-vehicle terminal, the data is sent to a cloud server in real time. For example, if a passenger enters the question, "I'm tired, what should I do?", that text data will be sent to the server.

[0256] 2. Determining the emotional state

[0257] The cloud server inputs the received question into the emotion engine to determine the user's emotional state. For example, if the user's question includes the emotion of "fatigue," the emotion engine will determine that emotional state.

[0258] 3. Generating Question and Answer

[0259] The emotional state determined by the emotion engine and the question are input into a generative artificial intelligence model. The generative AI model generates an appropriate answer based on the input question and emotional state. For example, it might generate an answer such as, "First, I recommend drinking plenty of water and getting some rest. Deep breathing can also help you relax."

[0260] 4. Submitting and displaying responses

[0261] The generated answers are sent from the server to the in-car terminals and displayed to the passengers. For example, if a passenger asks, "I'm tired, what should I do?", the terminal will display, "First, we recommend that you drink plenty of fluids and get some rest. Deep breathing can also help you relax."

[0262] Example of a prompt

[0263] When entering the question: "Question: I have a headache, what should I do?"

[0264] When determining emotion: "Emotion: Urgent"

[0265] When the AI ​​generates input: "Input: I have a headache, what should I do? Emotional state: Urgent"

[0266] This will enable us to provide appropriate support for health consultations during long-distance travel and offer highly accurate, emotion-based advice.

[0267] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0268] Step 1:

[0269] The user enters a question into an in-vehicle terminal. For example, the user might type a question like "I'm tired, what should I do?" into a tablet or smartphone. The terminal receives the data, processes it, and converts it into a format that can be sent to a cloud server. The input data here is the user's question text, and the output data is the formatted question text that is sent to the server.

[0270] Step 2:

[0271] The server inputs the question received from the user's terminal into the emotion engine. The emotion engine analyzes the text data and determines the user's emotional state (e.g., fatigue, stress). In this process, the input data is the question text, and the output data is information about the determined emotional state. The server stores this emotional state information in a database.

[0272] Step 3:

[0273] The server inputs the emotional state determined by the emotion engine and the question into a generative artificial intelligence model. The generative AI model receives the prompt "Input: I'm tired, what should I do? Emotional state: Fatigue" and uses natural language processing techniques to generate an appropriate response. Here, the input data is a combination of the question and emotional state, and the output data is the generated response text.

[0274] Step 4:

[0275] The generated response is sent to the user's terminal by the server. The server formats the response received from the generative artificial intelligence model, converts it to an appropriate format, and transfers it to the in-vehicle terminal. The input data is the generated response text, and the output data is the formatted response for display on the user's terminal.

[0276] Step 5:

[0277] The in-vehicle terminal displays the user the response received from the server. For example, the terminal might display content such as, "First, we recommend that you drink plenty of fluids and get some rest. Deep breathing can also help you relax." In this case, the input data is a formatted response received from the server, and the output data is the response displayed on the screen. The user can visually confirm the response.

[0278] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0279] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">)Generative AIs such as etc. can be mentioned. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including instructions 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 input. The data generation model 58 infers the input inference data according to the instructions 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 summary, etc.

[0280] In the above embodiment, an example of a form in which specific processing is performed by the data processing device 12 is given, but the technology of the present disclosure is not limited to this, and specific processing may be performed by the smart device 14.

[0281] <C [Second Embodiment]

[0282] FIG. 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0283] As shown in FIG. 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0284] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer for the technology of the present disclosure. The computer 22 includes a processor 28, a RAM is connected to a bus 34. Also, 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), etc.

[0285] 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. Also, the microphone 238, the speaker 240, and the camera 42 are connected to the bus 52.

[0286] The microphone 238 receives instructions and the like from the user 20 by receiving the voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into voice data, and outputs the voice data to the processor 46. The speaker 240 outputs voice according to an instruction from the processor 46.

[0287] The camera 42 is a small digital camera equipped with an optical system such as a lens, an aperture, and a shutter, and an imaging device such as a CMOS (Complementary Metal - Oxide - Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and images the surroundings of the user 20 (for example, an imaging range defined by an angle of view corresponding to the visual field of a general healthy person).

[0288] 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. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0289] 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, specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32.

[0290] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0291] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0292] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0293] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0294] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and returning the answers to the user terminal. This system includes a server, a user terminal, and a generative artificial intelligence model.

[0295] First, users use their smartphones or personal computers to input questions about their symptoms or medical needs. For example, a question like, "I have a headache, what should I do?" might be entered. This entered question is then sent from the device to the server.

[0296] Next, the server inputs the question received from the user's terminal into a generative artificial intelligence model. This generative AI model uses natural language processing technology to generate appropriate medical knowledge-based answers to the input question. For example, if the question is "I have a headache, what should I do?", the generative AI model will generate an answer such as, "First, it is important to drink plenty of fluids. I recommend that you rest and get more rest than usual. If your symptoms do not improve or if other symptoms appear, please consider seeing a doctor."

[0297] The server then sends the generated response to the device. The receiving device displays the response to the user. For example, if the user asks the question mentioned earlier, the device will display: "AI response: First, it is important to stay well-hydrated. We recommend resting and getting more rest than usual. If your symptoms do not improve or if other symptoms appear, please consider seeing a doctor."

[0298] This entire process allows users to quickly obtain highly accurate answers to their medical inquiries. Furthermore, it enables consistent responses to numerous questions while minimizing the need for specialized medical staff, thus providing an efficient medical consultation service.

[0299] The following describes the processing flow.

[0300] Step 1:

[0301] The user enters their symptoms or medical questions on the device. For example, the user might enter, "I have a headache, what should I do?"

[0302] Step 2:

[0303] The terminal sends user input to the server. During this process, the entered data is transferred to the server via the network.

[0304] Step 3:

[0305] The server processes the question received from the terminal. The server prepares to pass the received question data to the generative artificial intelligence model.

[0306] Step 4:

[0307] The server inputs the question received from the terminal into the generative artificial intelligence model and generates an answer based on the question. This generative artificial intelligence model uses natural language processing technology to create an answer based on appropriate medical knowledge. For example, the model generates an answer such as "When you have a headache, it is important to drink enough water. It is recommended to rest quietly and take more rest than usual. If the symptoms do not improve or other symptoms appear, please consider seeing a doctor at the hospital."

[0308] Step 5:

[0309] The server sends the generated answer to the terminal. At this time, the generated answer data is transferred to the terminal via the network.

[0310] Step 6:

[0311] The terminal displays the answer received from the server to the user. For example, when the user asked the previous question, the terminal displays "Answer by AI: When you have a headache, it is important to drink enough water. It is recommended to rest quietly and take more rest than usual. If the symptoms do not improve or other symptoms appear, please consider seeing a doctor at the hospital."

[0312] Through this series of steps, the user can quickly receive high-precision medical consultations for their questions. This system enables the provision of efficient medical consultation services.

[0313] (Example 1)

[0314] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0315] Traditional medical consultation systems struggled to provide quick and accurate answers to a large number of user inquiries. Furthermore, the reliance on specialized medical staff meant limited personnel resources and high costs. Additionally, the inconsistent user experience on user terminals, including the cumbersome process of entering questions and verifying answers, was a significant challenge.

[0316] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0317] In this invention, the server includes means for receiving questions entered from a user terminal, means for inputting the questions into a generative artificial intelligence model and generating answers, means for transmitting the generated answers to the user terminal, and means for displaying the answers on the user terminal. This not only provides quick and accurate answers to a large number of questions, but also minimizes the need for intervention by specialized medical staff, thereby reducing costs. Furthermore, users can obtain a consistent user experience, and it becomes easier to input questions and confirm answers.

[0318] A "user terminal" is a device used by a user to input questions and receive answers, and includes personal digital assistants and computers.

[0319] A "server" is a computing system that receives questions sent from a user terminal, inputs those questions into a generative artificial intelligence model, and then sends the generated answers back to the user terminal.

[0320] A "generative artificial intelligence model" refers to an artificial intelligence algorithm that uses natural language processing technology to generate appropriate answers to input questions.

[0321] A "question" is information that includes medical questions and descriptions of symptoms entered by the user, transmitted from the user's terminal to the server.

[0322] "Answer" refers to information, including solutions and advice, to a user's question, which is generated by a generative artificial intelligence model and sent to the user's terminal via a server.

[0323] "Natural language processing technology" refers to the techniques used by generative artificial intelligence models to understand questions and generate appropriate answers in human language.

[0324] "Means of receiving" refers to the collective functions and interfaces that a server uses to receive questions sent from a user's terminal and process them as information.

[0325] "Means of transmission" refers to the collective functions and interfaces that the server uses to send the generated response back to the user's terminal.

[0326] "Means of display" refers to the collective term for functions and interfaces that visually present the responses received by the user's terminal to the user.

[0327] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and returning the answers to the user terminal. This system includes a server, a user terminal, and a generative artificial intelligence model.

[0328] First, the user uses a user device such as a smartphone or personal computer to input questions about their symptoms or medical issues. For example, they might input a question like, "My throat has been sore since yesterday, what should I do?" This entered question is then sent from the device to the server.

[0329] Next, the server inputs the question received from the user's terminal into a generative artificial intelligence model. In this case, a model utilizing natural language processing technology (e.g., OpenAI GPT-3) is used as the generative AI model. The server sends the received question to the generative AI model as part of a prompt. For example, the prompt might be: "A user has entered a medical consultation question. The question is: 'My throat has been sore since yesterday, what should I do?' Please generate an appropriate answer to this question."

[0330] Generative artificial intelligence models produce appropriate answers to input questions. For example, if the question is "My throat has been sore since yesterday, what should I do?", the generative AI model will produce an answer such as, "If you have a sore throat, first we recommend drinking warm tea or water with honey. Also, gargle and, if necessary, try using over-the-counter throat lozenges or pain relievers. If symptoms persist for more than three days or worsen, please consider consulting a medical professional."

[0331] The server then sends the generated response to the user's device. The receiving device displays the response to the user. For example, if the user asks the question mentioned earlier, the device will display: "AI response: If you have a sore throat, we recommend drinking warm tea or water with honey first. Also, gargle and use commercially available throat lozenges or pain relievers if necessary. If symptoms persist for more than 3 days or worsen, please consider consulting a medical professional."

[0332] This entire process allows users to quickly obtain highly accurate answers to their medical inquiries. Furthermore, it enables consistent responses to numerous questions while minimizing the need for specialized medical staff, thus providing an efficient medical consultation service.

[0333] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0334] Step 1:

[0335] The user enters the question. The user uses their device (smartphone or personal computer) to enter the question into a text input field on a dedicated application or web browser and presses the submit button. The user's medical questions are provided as input, and the question text is generated as output.

[0336] Step 2:

[0337] The terminal sends a question to the server. The terminal sends the question text data to the server as an HTTP POST request. It receives the question text data as input and generates a request that is sent to the server as output.

[0338] Step 3:

[0339] The server receives the question. The server receives the HTTP POST request sent from the terminal and retrieves the question text. It receives the request from the terminal as input and extracts the question text as output.

[0340] Step 4:

[0341] The server inputs the question into a generative artificial intelligence model. The server sends the received question text along with a prompt to the generative artificial intelligence model as an API request. For example, the prompt might be in the format: "A user has entered a medical consultation question. The question is: 'My throat has been sore since yesterday, what should I do?' Please generate an appropriate answer to this question." It receives the question text as input and generates an API request as output.

[0342] Step 5:

[0343] A generative artificial intelligence model generates the answer. The generative AI model uses natural language processing techniques based on the received prompt to generate appropriate answer text. For example, if the question is "My throat has been sore since yesterday, what should I do?", it will generate the following answer: "If you have a sore throat, first we recommend drinking warm tea or water with honey. Also, gargle and, if necessary, try using over-the-counter throat lozenges or pain relievers. If symptoms persist for more than three days or worsen, please consider consulting a medical professional." It takes a prompt as input and generates answer text as output.

[0344] Step 6:

[0345] The server receives the generated response. The server receives the response text as an API response from the generative artificial intelligence model. It receives the response from the generative artificial intelligence model as input and obtains the response text as output.

[0346] Step 7:

[0347] The server sends the response to the terminal. The server sends the retrieved response text to the terminal as an HTTP response. It receives the response text as input and generates a response that is sent to the terminal as output.

[0348] Step 8:

[0349] The device displays the answer to the user. The device displays the received answer text on the screen. For example, it might display text such as, "AI response: If you have a sore throat, we recommend first drinking warm tea or water with honey. Also, gargle and use commercially available throat lozenges or pain relievers if necessary. If symptoms persist for more than 3 days or worsen, consider consulting a medical professional." It receives the answer text as input and displays it as output.

[0350] (Application Example 1)

[0351] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0352] The present invention aims to alleviate the anxieties and questions users have when making electronic payments and to provide a safe and secure payment environment. In particular, there is a need for a method to provide prompt and appropriate answers to questions related to health and safety. Modern consumers often have questions, especially regarding health, when making payments, and by responding quickly to these questions, consumer satisfaction can be improved.

[0353] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0354] In this invention, the server includes means for receiving questions entered from a user terminal, means for inputting the received questions into a generative artificial intelligence model to generate answers, and means for transmitting the generated answers to the user terminal. This makes it possible to generate quick and appropriate answers to health and safety-related questions when making electronic payments at the user terminal, thereby alleviating consumer anxiety and providing a safe and secure payment environment.

[0355] A "user terminal" is a device used by a user to input questions or make electronic payments, and can be a smartphone or a personal computer.

[0356] A "question" refers to any doubts or concerns entered by the user, particularly those related to health and safety.

[0357] A "generative artificial intelligence model" is a model that uses natural language processing technology to generate appropriate answers to input questions.

[0358] A "server" is a device that receives questions from a user's terminal, inputs them into a generative artificial intelligence model, and sends the generated answers back to the user's terminal.

[0359] An "answer" refers to the response or advice generated by a generative artificial intelligence model based on a question.

[0360] "Electronic payment" refers to the act of a user paying for goods or services online or offline using digital means.

[0361] This invention is a system designed to alleviate the anxieties and questions users may have when making electronic payments. This system includes a user terminal, a server, and a generative artificial intelligence model.

[0362] 1. User terminal

[0363] The user terminal is a smartphone or personal computer, and it functions as an interface for the user to input questions. For example, when a user makes a payment, they might input, "Is it safe to use this payment method?"

[0364] 2. Server

[0365] The server plays a central role in inputting questions received from user terminals into a generative artificial intelligence model and generating answers. Specifically, the server uses a web framework such as Flask to build a RESTful API, receive questions, and send them to the AI ​​model.

[0366] 3. Generative Artificial Intelligence Models

[0367] Generative artificial intelligence models use natural language processing techniques to generate appropriate answers based on input questions. For example, in response to the question, "Is this payment method safe to use?", it might generate the answer, "This payment method meets our strict security standards and can be used safely." This AI model is built on advanced natural language processing models such as BERT and GPT.

[0368] 4. Data processing and calculations

[0369] When a user enters a question on their device, that question is sent to the server. The server receives the question and inputs it into a generative artificial intelligence model via an API request. The generative AI model analyzes the input question and generates an appropriate answer. The generated answer is then sent back to the user's device via the server and presented to the user.

[0370] Specific example

[0371] When a user is shopping using the "Careful Pay" app, they can instantly enter the question, "Is it safe to use this payment method?" and the AI ​​within the app will respond, "This payment method meets our strict security standards and can be used safely."

[0372] Example of a prompt

[0373] Input: "Is this payment method safe to use?"

[0374] Generated response: "This payment method meets our strict security standards and can be used safely."

[0375] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0376] Step 1:

[0377] The user enters the question on their device.

[0378] Operation: The user uses a dedicated application on their smartphone or personal computer to enter a question, for example, "Is it safe to use this payment method?"

[0379] Input: The question entered by the user.

[0380] Output: The user's terminal generates the question data.

[0381] Step 2:

[0382] The terminal sends the entered question to the server.

[0383] Operation: The user's terminal sends the entered question data to the server as an HTTP request via an internet connection.

[0384] Input: Question data generated by the user's terminal.

[0385] Output: The server receives the question data.

[0386] Step 3:

[0387] The server inputs the received questions into a generative artificial intelligence model.

[0388] Operation: The server uses a web framework such as Flask to send the received question data as an API request to a generative artificial intelligence model.

[0389] Input: Question data received by the server.

[0390] Output: The generative artificial intelligence model obtains input data to analyze the question.

[0391] Step 4:

[0392] A generative artificial intelligence model generates an answer based on the question.

[0393] Operation: A generative artificial intelligence model (e.g., BERT or GPT) analyzes the question using natural language processing techniques and generates an appropriate answer. For example, it might generate an answer such as, "This payment method meets our strict security standards and can be used safely."

[0394] Input: Question data for analysis by a generative artificial intelligence model.

[0395] Output: Response data generated by a generative artificial intelligence model.

[0396] Step 5:

[0397] The server receives the generated response and sends it to the user's terminal.

[0398] Operation: The server sends the response data received from the generative artificial intelligence model to the user's terminal as an HTTP response.

[0399] Input: Response data generated by a generative artificial intelligence model.

[0400] Output: Response data received by the user's terminal.

[0401] Step 6:

[0402] The user's device displays the answer to the user.

[0403] Operation: The user's device displays the received response data in the user interface. For example, if the user is using the "Careful Pay" app, the app will display the response "This payment method meets our strict security standards and can be used safely" on the screen.

[0404] Input: Response data received by the user's terminal.

[0405] Output: The answer displayed to the user.

[0406] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0407] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and further combining this with an emotion engine that recognizes the user's emotions, before returning the answers to the user terminal. This system includes a server, a user terminal, a generative artificial intelligence model, and an emotion engine.

[0408] First, the user uses a smartphone or personal computer to input questions about their symptoms or medical issues. For example, the user might input, "I have a headache, what should I do?" This entered question is then sent from the device to the server.

[0409] Next, the server first inputs the question received from the user's terminal into the emotion engine. The emotion engine analyzes the text entered by the user and determines the user's emotional state. For example, if the user's question contains the emotion of "urgent," the emotion engine recognizes this and determines the emotional state to be "urgent."

[0410] The server then considers the emotional state determined by the emotion engine and inputs the received question into a generative artificial intelligence model to generate an answer. This generative AI model uses natural language processing technology to produce an appropriate answer based on medical knowledge in response to the input question. For example, if the question is "I have a headache, what should I do?" and the emotional state is determined to be "urgent," the generative AI model will generate an answer such as, "First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to drink plenty of fluids and rest."

[0411] Next, the server sends the generated answer to the device. The receiving device then displays the answer to the user. For example, if the user asks the question mentioned earlier, the device will display the answer: "AI answer: First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay hydrated and rest."

[0412] This entire process allows users to receive highly accurate medical advice quickly and with appropriate responses that take their emotions into consideration. This system enables the provision of efficient and accurate medical consultation services that also consider the user's emotional state.

[0413] The following describes the processing flow.

[0414] Step 1:

[0415] The user enters their symptoms or medical questions on the device. For example, the user might enter, "I have a headache, what should I do?"

[0416] Step 2:

[0417] The terminal sends the user's entered question data to the server. Here, the data is transmitted over the network.

[0418] Step 3:

[0419] The server inputs the question received from the terminal into the sentiment engine. The sentiment engine analyzes the question text and determines the user's emotional state.

[0420] Step 4:

[0421] The server prepares input data for the generative artificial intelligence model based on the emotion determination results from the emotion engine. For example, if the user's emotion is determined to be "anxiety," that information is added to the generative artificial intelligence model.

[0422] Step 5:

[0423] The server inputs question data and sentiment information into a generative artificial intelligence model to generate an answer. For example, if the question is "I have a headache, what should I do?" and the user is feeling "anxious," the generative AI model will generate an answer such as, "First, calm down, take a deep breath, and drink plenty of water. If the headache persists, please see a doctor."

[0424] Step 6:

[0425] The server sends the generated response data to the terminal. Here too, the data is transmitted over the network.

[0426] Step 7:

[0427] The device displays the response received from the server to the user. For example, the user's device might display the response: "AI response: First, calm down, take a deep breath, and drink plenty of water. If your headache persists, please consult a medical professional."

[0428] Through these processing steps, highly accurate medical consultations that take the user's emotions into account are provided quickly. This system enables efficient medical consultation services by providing appropriate advice tailored to the user's emotional state.

[0429] (Example 2)

[0430] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0431] Traditional medical consultation systems often provided answers without considering the user's emotional state, resulting in inappropriate responses tailored to the user's psychological condition. Consequently, users did not receive the support and advice they truly needed, leading to increased dissatisfaction and anxiety.

[0432] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a question input from a user terminal, means for analyzing the received question using emotion analysis means to determine the emotional state, means for inputting the determined emotional state and the question into a generative artificial intelligence model to generate an answer, and means for transmitting the generated answer to the user terminal. This makes it possible to provide appropriate medical consultation answers that take into account the user's emotional state.

[0433] A "user terminal" refers to an electronic device used by a user to input questions, such as a smartphone or personal computer.

[0434] A "server" refers to a central processing unit that processes questions received from user terminals and performs sentiment analysis and response generation.

[0435] "Emotional analysis means" refers to technical methods for analyzing text data entered by users to determine the user's emotional state (e.g., urgency, anxiety, doubt).

[0436] A "generative artificial intelligence model" refers to an artificial intelligence model that uses natural language processing techniques to generate appropriate answers to input questions.

[0437] "Answer generation means" refers to a technical means for inputting a question and emotional state into a generative artificial intelligence model to generate an appropriate medical answer.

[0438] "Questions" refer to text data related to medical consultations that users input through their devices.

[0439] "Answer" refers to text data generated by a generative artificial intelligence model, containing appropriate responses and advice to the user's question.

[0440] "Emotional state" refers to states such as urgency, anxiety, and relief, which are obtained by analyzing the emotions contained in the questions entered by the user.

[0441] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and further combining this with an emotion engine that recognizes the user's emotions, before returning the answers to the user terminal. This system includes a server, a user terminal, a generative artificial intelligence model, and an emotion engine.

[0442] First, the user uses a smartphone or personal computer to input questions about their symptoms or medical issues. For example, the user might input, "I have a headache, what should I do?" This entered question is then sent from the device to the server.

[0443] Next, the server first inputs the question received from the user's terminal into the emotion engine. The emotion engine analyzes the text entered by the user and determines the user's emotional state. For example, if the user's question contains the emotion of "urgent," the emotion engine recognizes this and determines the emotional state to be "urgent."

[0444] The server then considers the emotional state determined by the emotion engine and inputs the received question into a generative artificial intelligence model to generate an answer. This generative AI model uses natural language processing technology (e.g., OpenAI GPT-4) to produce an appropriate answer based on medical knowledge in response to the input question. For example, if the question is "I have a headache, what should I do?" and the emotional state is determined to be "urgent," the generative AI model will generate the answer "First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to drink plenty of fluids and rest."

[0445] Next, the server sends the generated answer to the device. The receiving device then displays the answer to the user. For example, if the user asks the question mentioned earlier, the device will display the answer: "AI answer: First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay hydrated and rest."

[0446] The following is an example of a prompt statement that demonstrates the specific operation of this system:

[0447] A user asked: "I have a headache, what should I do?"

[0448] The emotional state was determined to be "urgent."

[0449] --- Please generate an AI response below ---

[0450] answer:

[0451] This entire process allows users to receive highly accurate medical advice quickly and with appropriate responses that take their emotions into consideration. This system enables the provision of efficient and accurate medical consultation services that also consider the user's emotional state.

[0452] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0453] Step 1:

[0454] Users use their smartphones or personal computers to enter questions about medical consultations into a text box. For example, they might enter a question like, "I have a headache, what should I do?"

[0455] Input: Text question (user input)

[0456] Output: Input text question

[0457] Step 2:

[0458] When the user presses the "Send" button, the device begins the process of sending the entered text data to the server. Specifically, it uses the TCP / IP protocol to generate an HTTP POST request, packages the entered text data in JSON format, and sends it to the server over the internet.

[0459] Input: Entered text question (by pressing the send button on the device)

[0460] Output: Text data in JSON format sent to the server

[0461] Step 3:

[0462] The server parses the received HTTP POST request and extracts the text data. Specifically, it uses a JSON parser to extract the text data.

[0463] Input: Submitted text data in JSON format

[0464] Output: Extracted text data

[0465] Step 4:

[0466] The server sends the analyzed text data to the sentiment engine and begins sentiment analysis. Specifically, it calls the sentiment engine's API to perform sentiment analysis on the text.

[0467] Input: Extracted text data

[0468] Output: API request to the emotion engine

[0469] Step 5:

[0470] The emotion engine analyzes text and determines the user's emotional state. Specifically, it uses natural language processing algorithms to assign emotion labels (such as urgency, anxiety, and relief).

[0471] Input: API request to the emotion engine (text data)

[0472] Output: Determined emotion label (e.g., "Urgent")

[0473] Step 6:

[0474] The server considers the emotion labels obtained from the emotion engine and inputs them, along with the question, as prompts to the generative artificial intelligence model. Specifically, it calls the API of the generative AI model (e.g., GPT-4) to generate prompts in the following format:

[0475] User question: "I have a headache, what should I do?"

[0476] Emotional state: "Urgent"

[0477] --- Please generate an AI response below ---

[0478] answer:

[0479] Input: Question and sentiment label

[0480] Output: Prompts for the generative artificial intelligence model

[0481] Step 7:

[0482] Generative artificial intelligence models generate appropriate responses based on prompts. Specifically, they generate text and create responses based on the medical knowledge the model has learned.

[0483] Input: Prompt text for a generative artificial intelligence model

[0484] Output: Generated response (Example: "First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay well-hydrated and rest.")

[0485] Step 8:

[0486] The server receives the generated response and sends it to the user's terminal as an HTTP response. Specifically, it packages the generated response in JSON format and sends the response.

[0487] Input: Generated answer

[0488] Output: HTTP response to the user's terminal

[0489] Step 9:

[0490] The device displays the received response to the user. Specifically, it uses a module for displaying text on the screen to show the response: "AI response: First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay hydrated and rest."

[0491] Input: HTTP response (generated answer)

[0492] Output: Text response displayed to the user

[0493] (Application Example 2)

[0494] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0495] Conventional medical consultation systems often generate simple responses that do not take into account the user's emotional state, sometimes failing to provide appropriate support. Furthermore, they are not designed for use in vehicles, making it impossible to provide health consultation services during long-distance travel. Therefore, there is a need to develop a system that considers the user's emotional state and provides highly accurate medical advice via in-vehicle terminals.

[0496] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0497] In this invention, the server includes means for receiving questions entered from a user terminal; means for inputting the received questions into an emotion engine and determining the user's emotional state; means for inputting the received questions into a generative artificial intelligence model, taking into account the emotional state determined by the emotion engine, and generating an answer; means for transmitting the generated answer to the user terminal; and means including a technique for transmitting questions entered from a tablet or smartphone terminal used as an in-vehicle terminal to an in-vehicle cloud server, and executing processing on the cloud server using the emotion engine and the generative artificial intelligence model. This makes it possible to easily provide appropriate medical consultations that reflect the user's emotional state even while in a vehicle.

[0498] "User terminal" refers to a smartphone, tablet, or similar device used by the user to input and receive questions.

[0499] An "emotion engine" refers to a system that analyzes text entered by a user and determines the user's emotional state.

[0500] A "generative artificial intelligence model" refers to an artificial intelligence model that generates appropriate answers using natural language processing techniques based on questions received from users.

[0501] "In-vehicle terminal" refers to a tablet or smartphone device used inside a vehicle.

[0502] A "cloud server" refers to a server that exists on the internet and provides data processing and storage functions.

[0503] A "question" refers to the content of an inquiry that a user submits to the system via an input terminal.

[0504] "Answer" refers to the response that a generative artificial intelligence model generates in response to a question and provides to the user.

[0505] This invention relates to a system that automatically processes questions transmitted from a user terminal and returns appropriate answers through a combination of a generative artificial intelligence model and an emotion engine. This system includes a server, an in-vehicle terminal (such as a smartphone or tablet), a generative artificial intelligence model, and an emotion engine.

[0506] Users enter questions using an in-vehicle terminal. For example, a passenger might enter, "I'm tired, what should I do?" This question is sent from the in-vehicle terminal to a cloud server. The cloud server processes the question using the following hardware and software.

[0507] Hardware to use

[0508] In-car tablet or smartphone (e.g., Android device)

[0509] Internet connection

[0510] Cloud servers (e.g., AWS, Google Cloud)

[0511] Software to use

[0512] Emotion engine (e.g., Microsoft Azure Sentiment Analysis API)

[0513] Generative artificial intelligence models (e.g., GPT-3, BERT)

[0514] Data processing and data calculation

[0515] 1. Receipt of submitted questions

[0516] When a user enters a question into an in-vehicle terminal, the data is sent to a cloud server in real time. For example, if a passenger enters the question, "I'm tired, what should I do?", that text data will be sent to the server.

[0517] 2. Determining the emotional state

[0518] The cloud server inputs the received question into the emotion engine to determine the user's emotional state. For example, if the user's question includes the emotion of "fatigue," the emotion engine will determine that emotional state.

[0519] 3. Generating Question and Answer

[0520] The emotional state determined by the emotion engine and the question are input into a generative artificial intelligence model. The generative AI model generates an appropriate answer based on the input question and emotional state. For example, it might generate an answer such as, "First, I recommend drinking plenty of water and getting some rest. Deep breathing can also help you relax."

[0521] 4. Submitting and displaying responses

[0522] The generated answers are sent from the server to the in-car terminals and displayed to the passengers. For example, if a passenger asks, "I'm tired, what should I do?", the terminal will display, "First, we recommend that you drink plenty of fluids and get some rest. Deep breathing can also help you relax."

[0523] Example of a prompt

[0524] When entering the question: "Question: I have a headache, what should I do?"

[0525] When determining emotion: "Emotion: Urgent"

[0526] When the AI ​​generates input: "Input: I have a headache, what should I do? Emotional state: Urgent"

[0527] This will enable us to provide appropriate support for health consultations during long-distance travel and offer highly accurate, emotion-based advice.

[0528] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0529] Step 1:

[0530] The user enters a question into an in-vehicle terminal. For example, the user might type a question like "I'm tired, what should I do?" into a tablet or smartphone. The terminal receives the data, processes it, and converts it into a format that can be sent to a cloud server. The input data here is the user's question text, and the output data is the formatted question text that is sent to the server.

[0531] Step 2:

[0532] The server inputs the question received from the user's terminal into the emotion engine. The emotion engine analyzes the text data and determines the user's emotional state (e.g., fatigue, stress). In this process, the input data is the question text, and the output data is information about the determined emotional state. The server stores this emotional state information in a database.

[0533] Step 3:

[0534] The server inputs the emotional state determined by the emotion engine and the question into a generative artificial intelligence model. The generative AI model receives the prompt "Input: I'm tired, what should I do? Emotional state: Fatigue" and uses natural language processing techniques to generate an appropriate response. Here, the input data is a combination of the question and emotional state, and the output data is the generated response text.

[0535] Step 4:

[0536] The generated response is sent to the user's terminal by the server. The server formats the response received from the generative artificial intelligence model, converts it to an appropriate format, and transfers it to the in-vehicle terminal. The input data is the generated response text, and the output data is the formatted response for display on the user's terminal.

[0537] Step 5:

[0538] The in-vehicle terminal displays the user the response received from the server. For example, the terminal might display content such as, "First, we recommend that you drink plenty of fluids and get some rest. Deep breathing can also help you relax." In this case, the input data is a formatted response received from the server, and the output data is the response displayed on the screen. The user can visually confirm the response.

[0539] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0540] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0541] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0542] [Third Embodiment]

[0543] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0544] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0545] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0546] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0547] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0548] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0549] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0550] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0551] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0552] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0553] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0554] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0555] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and returning the answers to the user terminal. This system includes a server, a user terminal, and a generative artificial intelligence model.

[0556] First, users use their smartphones or personal computers to input questions about their symptoms or medical needs. For example, a question like, "I have a headache, what should I do?" might be entered. This entered question is then sent from the device to the server.

[0557] Next, the server inputs the question received from the user's terminal into a generative artificial intelligence model. This generative AI model uses natural language processing technology to generate appropriate medical knowledge-based answers to the input question. For example, if the question is "I have a headache, what should I do?", the generative AI model will generate an answer such as, "First, it is important to drink plenty of fluids. I recommend that you rest and get more rest than usual. If your symptoms do not improve or if other symptoms appear, please consider seeing a doctor."

[0558] The server then sends the generated response to the device. The receiving device displays the response to the user. For example, if the user asks the question mentioned earlier, the device will display: "AI response: First, it is important to stay well-hydrated. We recommend resting and getting more rest than usual. If your symptoms do not improve or if other symptoms appear, please consider seeing a doctor."

[0559] This entire process allows users to quickly obtain highly accurate answers to their medical inquiries. Furthermore, it enables consistent responses to numerous questions while minimizing the need for specialized medical staff, thus providing an efficient medical consultation service.

[0560] The following describes the processing flow.

[0561] Step 1:

[0562] The user enters their symptoms or medical questions on the device. For example, the user might enter, "I have a headache, what should I do?"

[0563] Step 2:

[0564] The terminal sends user input to the server. During this process, the entered data is transferred to the server via the network.

[0565] Step 3:

[0566] The server processes the questions received from the terminal. The server prepares the received question data to pass to a generative artificial intelligence model.

[0567] Step 4:

[0568] The server inputs the received question into a generative artificial intelligence model, which then generates an answer based on the question. This generative AI model utilizes natural language processing techniques to produce answers based on appropriate medical knowledge. For example, the model might generate an answer such as, "When you have a headache, it's important to drink plenty of fluids. It's recommended that you rest and get more rest than usual. If your symptoms don't improve or if other symptoms appear, consider seeing a doctor."

[0569] Step 5:

[0570] The server sends the generated response to the terminal. During this process, the generated response data is transferred to the terminal via the network.

[0571] Step 6:

[0572] The device displays the answer it received from the server to the user. For example, if the user asks the question mentioned earlier, the device will display: "AI answer: When you have a headache, it is important to drink plenty of fluids. It is recommended that you rest and get more rest than usual. If your symptoms do not improve or if other symptoms appear, please consider seeing a doctor."

[0573] Through this series of steps, users can quickly receive highly accurate medical advice in response to their questions. This system enables the efficient provision of medical consultation services.

[0574] (Example 1)

[0575] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0576] Traditional medical consultation systems struggled to provide quick and accurate answers to a large number of user inquiries. Furthermore, the reliance on specialized medical staff meant limited personnel resources and high costs. Additionally, the inconsistent user experience on user terminals, including the cumbersome process of entering questions and verifying answers, was a significant challenge.

[0577] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0578] In this invention, the server includes means for receiving questions entered from a user terminal, means for inputting the questions into a generative artificial intelligence model and generating answers, means for transmitting the generated answers to the user terminal, and means for displaying the answers on the user terminal. This not only provides quick and accurate answers to a large number of questions, but also minimizes the need for intervention by specialized medical staff, thereby reducing costs. Furthermore, users can obtain a consistent user experience, and it becomes easier to input questions and confirm answers.

[0579] A "user terminal" is a device used by a user to input questions and receive answers, and includes personal digital assistants and computers.

[0580] A "server" is a computing system that receives questions sent from a user terminal, inputs those questions into a generative artificial intelligence model, and then sends the generated answers back to the user terminal.

[0581] A "generative artificial intelligence model" refers to an artificial intelligence algorithm that uses natural language processing technology to generate appropriate answers to input questions.

[0582] A "question" is information that includes medical questions and descriptions of symptoms entered by the user, transmitted from the user's terminal to the server.

[0583] "Answer" refers to information, including solutions and advice, to a user's question, which is generated by a generative artificial intelligence model and sent to the user's terminal via a server.

[0584] "Natural language processing technology" refers to the techniques used by generative artificial intelligence models to understand questions and generate appropriate answers in human language.

[0585] "Means of receiving" refers to the collective functions and interfaces that a server uses to receive questions sent from a user's terminal and process them as information.

[0586] "Means of transmission" refers to the collective functions and interfaces that the server uses to send the generated response back to the user's terminal.

[0587] "Means of display" refers to the collective term for functions and interfaces that visually present the responses received by the user's terminal to the user.

[0588] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and returning the answers to the user terminal. This system includes a server, a user terminal, and a generative artificial intelligence model.

[0589] First, the user uses a user device such as a smartphone or personal computer to input questions about their symptoms or medical issues. For example, they might input a question like, "My throat has been sore since yesterday, what should I do?" This entered question is then sent from the device to the server.

[0590] Next, the server inputs the question received from the user's terminal into a generative artificial intelligence model. In this case, a model utilizing natural language processing technology (e.g., OpenAI GPT-3) is used as the generative AI model. The server sends the received question to the generative AI model as part of a prompt. For example, the prompt might be: "A user has entered a medical consultation question. The question is: 'My throat has been sore since yesterday, what should I do?' Please generate an appropriate answer to this question."

[0591] Generative artificial intelligence models produce appropriate answers to input questions. For example, if the question is "My throat has been sore since yesterday, what should I do?", the generative AI model will produce an answer such as, "If you have a sore throat, first we recommend drinking warm tea or water with honey. Also, gargle and, if necessary, try using over-the-counter throat lozenges or pain relievers. If symptoms persist for more than three days or worsen, please consider consulting a medical professional."

[0592] The server then sends the generated response to the user's device. The receiving device displays the response to the user. For example, if the user asks the question mentioned earlier, the device will display: "AI response: If you have a sore throat, we recommend drinking warm tea or water with honey first. Also, gargle and use commercially available throat lozenges or pain relievers if necessary. If symptoms persist for more than 3 days or worsen, please consider consulting a medical professional."

[0593] This entire process allows users to quickly obtain highly accurate answers to their medical inquiries. Furthermore, it enables consistent responses to numerous questions while minimizing the need for specialized medical staff, thus providing an efficient medical consultation service.

[0594] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0595] Step 1:

[0596] The user enters the question. The user uses their device (smartphone or personal computer) to enter the question into a text input field on a dedicated application or web browser and presses the submit button. The user's medical questions are provided as input, and the question text is generated as output.

[0597] Step 2:

[0598] The terminal sends a question to the server. The terminal sends the question text data to the server as an HTTP POST request. It receives the question text data as input and generates a request that is sent to the server as output.

[0599] Step 3:

[0600] The server receives the question. The server receives the HTTP POST request sent from the terminal and retrieves the question text. It receives the request from the terminal as input and extracts the question text as output.

[0601] Step 4:

[0602] The server inputs the question into a generative artificial intelligence model. The server sends the received question text along with a prompt to the generative artificial intelligence model as an API request. For example, the prompt might be in the format: "A user has entered a medical consultation question. The question is: 'My throat has been sore since yesterday, what should I do?' Please generate an appropriate answer to this question." It receives the question text as input and generates an API request as output.

[0603] Step 5:

[0604] A generative artificial intelligence model generates the answer. The generative AI model uses natural language processing techniques based on the received prompt to generate appropriate answer text. For example, if the question is "My throat has been sore since yesterday, what should I do?", it will generate the following answer: "If you have a sore throat, first we recommend drinking warm tea or water with honey. Also, gargle and, if necessary, try using over-the-counter throat lozenges or pain relievers. If symptoms persist for more than three days or worsen, please consider consulting a medical professional." It takes a prompt as input and generates answer text as output.

[0605] Step 6:

[0606] The server receives the generated response. The server receives the response text as an API response from the generative artificial intelligence model. It receives the response from the generative artificial intelligence model as input and obtains the response text as output.

[0607] Step 7:

[0608] The server sends the response to the terminal. The server sends the retrieved response text to the terminal as an HTTP response. It receives the response text as input and generates a response that is sent to the terminal as output.

[0609] Step 8:

[0610] The device displays the answer to the user. The device displays the received answer text on the screen. For example, it might display text such as, "AI response: If you have a sore throat, we recommend first drinking warm tea or water with honey. Also, gargle and use commercially available throat lozenges or pain relievers if necessary. If symptoms persist for more than 3 days or worsen, consider consulting a medical professional." It receives the answer text as input and displays it as output.

[0611] (Application Example 1)

[0612] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0613] The present invention aims to alleviate the anxieties and questions users have when making electronic payments and to provide a safe and secure payment environment. In particular, there is a need for a method to provide prompt and appropriate answers to questions related to health and safety. Modern consumers often have questions, especially regarding health, when making payments, and by responding quickly to these questions, consumer satisfaction can be improved.

[0614] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0615] In this invention, the server includes means for receiving questions entered from a user terminal, means for inputting the received questions into a generative artificial intelligence model to generate answers, and means for transmitting the generated answers to the user terminal. This makes it possible to generate quick and appropriate answers to health and safety-related questions when making electronic payments at the user terminal, thereby alleviating consumer anxiety and providing a safe and secure payment environment.

[0616] A "user terminal" is a device used by a user to input questions or make electronic payments, and can be a smartphone or a personal computer.

[0617] A "question" refers to any doubts or concerns entered by the user, particularly those related to health and safety.

[0618] A "generative artificial intelligence model" is a model that uses natural language processing technology to generate appropriate answers to input questions.

[0619] A "server" is a device that receives questions from a user's terminal, inputs them into a generative artificial intelligence model, and sends the generated answers back to the user's terminal.

[0620] An "answer" refers to the response or advice generated by a generative artificial intelligence model based on a question.

[0621] "Electronic payment" refers to the act of a user paying for goods or services online or offline using digital means.

[0622] This invention is a system designed to alleviate the anxieties and questions users may have when making electronic payments. This system includes a user terminal, a server, and a generative artificial intelligence model.

[0623] 1. User terminal

[0624] The user terminal is a smartphone or personal computer, and it functions as an interface for the user to input questions. For example, when a user makes a payment, they might input, "Is it safe to use this payment method?"

[0625] 2. Server

[0626] The server plays a central role in inputting questions received from user terminals into a generative artificial intelligence model and generating answers. Specifically, the server uses a web framework such as Flask to build a RESTful API, receive questions, and send them to the AI ​​model.

[0627] 3. Generative Artificial Intelligence Models

[0628] Generative artificial intelligence models use natural language processing techniques to generate appropriate answers based on input questions. For example, in response to the question, "Is this payment method safe to use?", it might generate the answer, "This payment method meets our strict security standards and can be used safely." This AI model is built on advanced natural language processing models such as BERT and GPT.

[0629] 4. Data processing and calculations

[0630] When a user enters a question on their device, that question is sent to the server. The server receives the question and inputs it into a generative artificial intelligence model via an API request. The generative AI model analyzes the input question and generates an appropriate answer. The generated answer is then sent back to the user's device via the server and presented to the user.

[0631] Specific example

[0632] When a user is shopping using the "Careful Pay" app, they can instantly enter the question, "Is it safe to use this payment method?" and the AI ​​within the app will respond, "This payment method meets our strict security standards and can be used safely."

[0633] Example of a prompt

[0634] Input: "Is this payment method safe to use?"

[0635] Generated response: "This payment method meets our strict security standards and can be used safely."

[0636] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0637] Step 1:

[0638] The user enters the question on their device.

[0639] Operation: The user uses a dedicated application on their smartphone or personal computer to enter a question, for example, "Is it safe to use this payment method?"

[0640] Input: The question entered by the user.

[0641] Output: The user's terminal generates the question data.

[0642] Step 2:

[0643] The terminal sends the entered question to the server.

[0644] Operation: The user's terminal sends the entered question data to the server as an HTTP request via an internet connection.

[0645] Input: Question data generated by the user's terminal.

[0646] Output: The server receives the question data.

[0647] Step 3:

[0648] The server inputs the received questions into a generative artificial intelligence model.

[0649] Operation: The server uses a web framework such as Flask to send the received question data as an API request to a generative artificial intelligence model.

[0650] Input: Question data received by the server.

[0651] Output: The generative artificial intelligence model obtains input data to analyze the question.

[0652] Step 4:

[0653] A generative artificial intelligence model generates an answer based on the question.

[0654] Operation: A generative artificial intelligence model (e.g., BERT or GPT) analyzes the question using natural language processing techniques and generates an appropriate answer. For example, it might generate an answer such as, "This payment method meets our strict security standards and can be used safely."

[0655] Input: Question data for analysis by a generative artificial intelligence model.

[0656] Output: Response data generated by a generative artificial intelligence model.

[0657] Step 5:

[0658] The server receives the generated response and sends it to the user's terminal.

[0659] Operation: The server sends the response data received from the generative artificial intelligence model to the user's terminal as an HTTP response.

[0660] Input: Response data generated by a generative artificial intelligence model.

[0661] Output: Response data received by the user's terminal.

[0662] Step 6:

[0663] The user's device displays the answer to the user.

[0664] Operation: The user's device displays the received response data in the user interface. For example, if the user is using the "Careful Pay" app, the app will display the response "This payment method meets our strict security standards and can be used safely" on the screen.

[0665] Input: Response data received by the user's terminal.

[0666] Output: The answer displayed to the user.

[0667] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0668] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and further combining this with an emotion engine that recognizes the user's emotions, before returning the answers to the user terminal. This system includes a server, a user terminal, a generative artificial intelligence model, and an emotion engine.

[0669] First, the user uses a smartphone or personal computer to input questions about their symptoms or medical issues. For example, the user might input, "I have a headache, what should I do?" This entered question is then sent from the device to the server.

[0670] Next, the server first inputs the question received from the user's terminal into the emotion engine. The emotion engine analyzes the text entered by the user and determines the user's emotional state. For example, if the user's question contains the emotion of "urgent," the emotion engine recognizes this and determines the emotional state to be "urgent."

[0671] The server then considers the emotional state determined by the emotion engine and inputs the received question into a generative artificial intelligence model to generate an answer. This generative AI model uses natural language processing technology to produce an appropriate answer based on medical knowledge in response to the input question. For example, if the question is "I have a headache, what should I do?" and the emotional state is determined to be "urgent," the generative AI model will generate an answer such as, "First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to drink plenty of fluids and rest."

[0672] Next, the server sends the generated answer to the device. The receiving device then displays the answer to the user. For example, if the user asks the question mentioned earlier, the device will display the answer: "AI answer: First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay hydrated and rest."

[0673] This entire process allows users to receive highly accurate medical advice quickly and with appropriate responses that take their emotions into consideration. This system enables the provision of efficient and accurate medical consultation services that also consider the user's emotional state.

[0674] The following describes the processing flow.

[0675] Step 1:

[0676] The user enters their symptoms or medical questions on the device. For example, the user might enter, "I have a headache, what should I do?"

[0677] Step 2:

[0678] The terminal sends the user's entered question data to the server. Here, the data is transmitted over the network.

[0679] Step 3:

[0680] The server inputs the question received from the terminal into the sentiment engine. The sentiment engine analyzes the question text and determines the user's emotional state.

[0681] Step 4:

[0682] The server prepares input data for the generative artificial intelligence model based on the emotion determination results from the emotion engine. For example, if the user's emotion is determined to be "anxiety," that information is added to the generative artificial intelligence model.

[0683] Step 5:

[0684] The server inputs question data and sentiment information into a generative artificial intelligence model to generate an answer. For example, if the question is "I have a headache, what should I do?" and the user is feeling "anxious," the generative AI model will generate an answer such as, "First, calm down, take a deep breath, and drink plenty of water. If the headache persists, please see a doctor."

[0685] Step 6:

[0686] The server sends the generated response data to the terminal. Here too, the data is transmitted over the network.

[0687] Step 7:

[0688] The device displays the response received from the server to the user. For example, the user's device might display the response: "AI response: First, calm down, take a deep breath, and drink plenty of water. If your headache persists, please consult a medical professional."

[0689] Through these processing steps, highly accurate medical consultations that take the user's emotions into account are provided quickly. This system enables efficient medical consultation services by providing appropriate advice tailored to the user's emotional state.

[0690] (Example 2)

[0691] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0692] Traditional medical consultation systems often provided answers without considering the user's emotional state, resulting in inappropriate responses tailored to the user's psychological condition. Consequently, users did not receive the support and advice they truly needed, leading to increased dissatisfaction and anxiety.

[0693] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a question input from a user terminal, means for analyzing the received question using emotion analysis means to determine the emotional state, means for inputting the determined emotional state and the question into a generative artificial intelligence model to generate an answer, and means for transmitting the generated answer to the user terminal. This makes it possible to provide appropriate medical consultation answers that take into account the user's emotional state.

[0694] A "user terminal" refers to an electronic device used by a user to input questions, such as a smartphone or personal computer.

[0695] A "server" refers to a central processing unit that processes questions received from user terminals and performs sentiment analysis and response generation.

[0696] "Emotional analysis means" refers to technical methods for analyzing text data entered by users to determine the user's emotional state (e.g., urgency, anxiety, doubt).

[0697] A "generative artificial intelligence model" refers to an artificial intelligence model that uses natural language processing techniques to generate appropriate answers to input questions.

[0698] "Answer generation means" refers to a technical means for inputting a question and emotional state into a generative artificial intelligence model to generate an appropriate medical answer.

[0699] "Questions" refer to text data related to medical consultations that users input through their devices.

[0700] "Answer" refers to text data generated by a generative artificial intelligence model, containing appropriate responses and advice to the user's question.

[0701] "Emotional state" refers to states such as urgency, anxiety, and relief, which are obtained by analyzing the emotions contained in the questions entered by the user.

[0702] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and further combining this with an emotion engine that recognizes the user's emotions, before returning the answers to the user terminal. This system includes a server, a user terminal, a generative artificial intelligence model, and an emotion engine.

[0703] First, the user uses a smartphone or personal computer to input questions about their symptoms or medical issues. For example, the user might input, "I have a headache, what should I do?" This entered question is then sent from the device to the server.

[0704] Next, the server first inputs the question received from the user's terminal into the emotion engine. The emotion engine analyzes the text entered by the user and determines the user's emotional state. For example, if the user's question contains the emotion of "urgent," the emotion engine recognizes this and determines the emotional state to be "urgent."

[0705] The server then considers the emotional state determined by the emotion engine and inputs the received question into a generative artificial intelligence model to generate an answer. This generative AI model uses natural language processing technology (e.g., OpenAI GPT-4) to produce an appropriate answer based on medical knowledge in response to the input question. For example, if the question is "I have a headache, what should I do?" and the emotional state is determined to be "urgent," the generative AI model will generate the answer "First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to drink plenty of fluids and rest."

[0706] Next, the server sends the generated answer to the device. The receiving device then displays the answer to the user. For example, if the user asks the question mentioned earlier, the device will display the answer: "AI answer: First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay hydrated and rest."

[0707] The following is an example of a prompt statement that demonstrates the specific operation of this system:

[0708] A user asked: "I have a headache, what should I do?"

[0709] The emotional state was determined to be "urgent."

[0710] --- Please generate an AI response below ---

[0711] answer:

[0712] This entire process allows users to receive highly accurate medical advice quickly and with appropriate responses that take their emotions into consideration. This system enables the provision of efficient and accurate medical consultation services that also consider the user's emotional state.

[0713] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0714] Step 1:

[0715] Users use their smartphones or personal computers to enter questions about medical consultations into a text box. For example, they might enter a question like, "I have a headache, what should I do?"

[0716] Input: Text question (user input)

[0717] Output: Input text question

[0718] Step 2:

[0719] When the user presses the "Send" button, the device begins the process of sending the entered text data to the server. Specifically, it uses the TCP / IP protocol to generate an HTTP POST request, packages the entered text data in JSON format, and sends it to the server over the internet.

[0720] Input: Entered text question (by pressing the send button on the device)

[0721] Output: Text data in JSON format sent to the server

[0722] Step 3:

[0723] The server parses the received HTTP POST request and extracts the text data. Specifically, it uses a JSON parser to extract the text data.

[0724] Input: Submitted text data in JSON format

[0725] Output: Extracted text data

[0726] Step 4:

[0727] The server sends the analyzed text data to the sentiment engine and begins sentiment analysis. Specifically, it calls the sentiment engine's API to perform sentiment analysis on the text.

[0728] Input: Extracted text data

[0729] Output: API request to the emotion engine

[0730] Step 5:

[0731] The emotion engine analyzes text and determines the user's emotional state. Specifically, it uses natural language processing algorithms to assign emotion labels (such as urgency, anxiety, and relief).

[0732] Input: API request to the emotion engine (text data)

[0733] Output: Determined emotion label (e.g., "Urgent")

[0734] Step 6:

[0735] The server considers the emotion labels obtained from the emotion engine and inputs them, along with the question, as prompts to the generative artificial intelligence model. Specifically, it calls the API of the generative AI model (e.g., GPT-4) to generate prompts in the following format:

[0736] User question: "I have a headache, what should I do?"

[0737] Emotional state: "Urgent"

[0738] --- Please generate an AI response below ---

[0739] answer:

[0740] Input: Question and sentiment label

[0741] Output: Prompts for the generative artificial intelligence model

[0742] Step 7:

[0743] Generative artificial intelligence models generate appropriate responses based on prompts. Specifically, they generate text and create responses based on the medical knowledge the model has learned.

[0744] Input: Prompt text for a generative artificial intelligence model

[0745] Output: Generated response (Example: "First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay well-hydrated and rest.")

[0746] Step 8:

[0747] The server receives the generated response and sends it to the user's terminal as an HTTP response. Specifically, it packages the generated response in JSON format and sends the response.

[0748] Input: Generated answer

[0749] Output: HTTP response to the user's terminal

[0750] Step 9:

[0751] The device displays the received response to the user. Specifically, it uses a module for displaying text on the screen to show the response: "AI response: First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay hydrated and rest."

[0752] Input: HTTP response (generated answer)

[0753] Output: Text response displayed to the user

[0754] (Application Example 2)

[0755] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0756] Conventional medical consultation systems often generate simple responses that do not take into account the user's emotional state, sometimes failing to provide appropriate support. Furthermore, they are not designed for use in vehicles, making it impossible to provide health consultation services during long-distance travel. Therefore, there is a need to develop a system that considers the user's emotional state and provides highly accurate medical advice via in-vehicle terminals.

[0757] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0758] In this invention, the server includes means for receiving questions entered from a user terminal; means for inputting the received questions into an emotion engine and determining the user's emotional state; means for inputting the received questions into a generative artificial intelligence model, taking into account the emotional state determined by the emotion engine, and generating an answer; means for transmitting the generated answer to the user terminal; and means including a technique for transmitting questions entered from a tablet or smartphone terminal used as an in-vehicle terminal to an in-vehicle cloud server, and executing processing on the cloud server using the emotion engine and the generative artificial intelligence model. This makes it possible to easily provide appropriate medical consultations that reflect the user's emotional state even while in a vehicle.

[0759] "User terminal" refers to a smartphone, tablet, or similar device used by the user to input and receive questions.

[0760] An "emotion engine" refers to a system that analyzes text entered by a user and determines the user's emotional state.

[0761] A "generative artificial intelligence model" refers to an artificial intelligence model that generates appropriate answers using natural language processing techniques based on questions received from users.

[0762] "In-vehicle terminal" refers to a tablet or smartphone device used inside a vehicle.

[0763] A "cloud server" refers to a server that exists on the internet and provides data processing and storage functions.

[0764] A "question" refers to the content of an inquiry that a user submits to the system via an input terminal.

[0765] "Answer" refers to the response that a generative artificial intelligence model generates in response to a question and provides to the user.

[0766] This invention relates to a system that automatically processes questions transmitted from a user terminal and returns appropriate answers through a combination of a generative artificial intelligence model and an emotion engine. This system includes a server, an in-vehicle terminal (such as a smartphone or tablet), a generative artificial intelligence model, and an emotion engine.

[0767] Users enter questions using an in-vehicle terminal. For example, a passenger might enter, "I'm tired, what should I do?" This question is sent from the in-vehicle terminal to a cloud server. The cloud server processes the question using the following hardware and software.

[0768] Hardware to use

[0769] In-car tablet or smartphone (e.g., Android device)

[0770] Internet connection

[0771] Cloud servers (e.g., AWS, Google Cloud)

[0772] Software to use

[0773] Emotion engine (e.g., Microsoft Azure Sentiment Analysis API)

[0774] Generative artificial intelligence models (e.g., GPT-3, BERT)

[0775] Data processing and data calculation

[0776] 1. Receipt of submitted questions

[0777] When a user enters a question into an in-vehicle terminal, the data is sent to a cloud server in real time. For example, if a passenger enters the question, "I'm tired, what should I do?", that text data will be sent to the server.

[0778] 2. Determining the emotional state

[0779] The cloud server inputs the received question into the emotion engine to determine the user's emotional state. For example, if the user's question includes the emotion of "fatigue," the emotion engine will determine that emotional state.

[0780] 3. Generating Question and Answer

[0781] The emotional state determined by the emotion engine and the question are input into a generative artificial intelligence model. The generative AI model generates an appropriate answer based on the input question and emotional state. For example, it might generate an answer such as, "First, I recommend drinking plenty of water and getting some rest. Deep breathing can also help you relax."

[0782] 4. Submitting and displaying responses

[0783] The generated answers are sent from the server to the in-car terminals and displayed to the passengers. For example, if a passenger asks, "I'm tired, what should I do?", the terminal will display, "First, we recommend that you drink plenty of fluids and get some rest. Deep breathing can also help you relax."

[0784] Example of a prompt

[0785] When entering the question: "Question: I have a headache, what should I do?"

[0786] When determining emotion: "Emotion: Urgent"

[0787] When the AI ​​generates input: "Input: I have a headache, what should I do? Emotional state: Urgent"

[0788] This will enable us to provide appropriate support for health consultations during long-distance travel and offer highly accurate, emotion-based advice.

[0789] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0790] Step 1:

[0791] The user enters a question into an in-vehicle terminal. For example, the user might type a question like "I'm tired, what should I do?" into a tablet or smartphone. The terminal receives the data, processes it, and converts it into a format that can be sent to a cloud server. The input data here is the user's question text, and the output data is the formatted question text that is sent to the server.

[0792] Step 2:

[0793] The server inputs the question received from the user's terminal into the emotion engine. The emotion engine analyzes the text data and determines the user's emotional state (e.g., fatigue, stress). In this process, the input data is the question text, and the output data is information about the determined emotional state. The server stores this emotional state information in a database.

[0794] Step 3:

[0795] The server inputs the emotional state determined by the emotion engine and the question into a generative artificial intelligence model. The generative AI model receives the prompt "Input: I'm tired, what should I do? Emotional state: Fatigue" and uses natural language processing techniques to generate an appropriate response. Here, the input data is a combination of the question and emotional state, and the output data is the generated response text.

[0796] Step 4:

[0797] The generated response is sent to the user's terminal by the server. The server formats the response received from the generative artificial intelligence model, converts it to an appropriate format, and transfers it to the in-vehicle terminal. The input data is the generated response text, and the output data is the formatted response for display on the user's terminal.

[0798] Step 5:

[0799] The in-vehicle terminal displays the user the response received from the server. For example, the terminal might display content such as, "First, we recommend that you drink plenty of fluids and get some rest. Deep breathing can also help you relax." In this case, the input data is a formatted response received from the server, and the output data is the response displayed on the screen. The user can visually confirm the response.

[0800] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0801] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0802] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0803] [Fourth Embodiment]

[0804] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0805] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0806] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0807] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0808] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0809] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0810] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0811] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0812] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0813] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0814] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0815] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0816] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0817] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and returning the answers to the user terminal. This system includes a server, a user terminal, and a generative artificial intelligence model.

[0818] First, users use their smartphones or personal computers to input questions about their symptoms or medical needs. For example, a question like, "I have a headache, what should I do?" might be entered. This entered question is then sent from the device to the server.

[0819] Next, the server inputs the question received from the user's terminal into a generative artificial intelligence model. This generative AI model uses natural language processing technology to generate appropriate medical knowledge-based answers to the input question. For example, if the question is "I have a headache, what should I do?", the generative AI model will generate an answer such as, "First, it is important to drink plenty of fluids. I recommend that you rest and get more rest than usual. If your symptoms do not improve or if other symptoms appear, please consider seeing a doctor."

[0820] The server then sends the generated response to the device. The receiving device displays the response to the user. For example, if the user asks the question mentioned earlier, the device will display: "AI response: First, it is important to stay well-hydrated. We recommend resting and getting more rest than usual. If your symptoms do not improve or if other symptoms appear, please consider seeing a doctor."

[0821] This entire process allows users to quickly obtain highly accurate answers to their medical inquiries. Furthermore, it enables consistent responses to numerous questions while minimizing the need for specialized medical staff, thus providing an efficient medical consultation service.

[0822] The following describes the processing flow.

[0823] Step 1:

[0824] The user enters their symptoms or medical questions on the device. For example, the user might enter, "I have a headache, what should I do?"

[0825] Step 2:

[0826] The terminal sends user input to the server. During this process, the entered data is transferred to the server via the network.

[0827] Step 3:

[0828] The server processes the questions received from the terminal. The server prepares the received question data to pass to a generative artificial intelligence model.

[0829] Step 4:

[0830] The server inputs the received question into a generative artificial intelligence model, which then generates an answer based on the question. This generative AI model utilizes natural language processing techniques to produce answers based on appropriate medical knowledge. For example, the model might generate an answer such as, "When you have a headache, it's important to drink plenty of fluids. It's recommended that you rest and get more rest than usual. If your symptoms don't improve or if other symptoms appear, consider seeing a doctor."

[0831] Step 5:

[0832] The server sends the generated response to the terminal. During this process, the generated response data is transferred to the terminal via the network.

[0833] Step 6:

[0834] The device displays the answer it received from the server to the user. For example, if the user asks the question mentioned earlier, the device will display: "AI answer: When you have a headache, it is important to drink plenty of fluids. It is recommended that you rest and get more rest than usual. If your symptoms do not improve or if other symptoms appear, please consider seeing a doctor."

[0835] Through this series of steps, users can quickly receive highly accurate medical advice in response to their questions. This system enables the efficient provision of medical consultation services.

[0836] (Example 1)

[0837] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0838] Traditional medical consultation systems struggled to provide quick and accurate answers to a large number of user inquiries. Furthermore, the reliance on specialized medical staff meant limited personnel resources and high costs. Additionally, the inconsistent user experience on user terminals, including the cumbersome process of entering questions and verifying answers, was a significant challenge.

[0839] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0840] In this invention, the server includes means for receiving questions entered from a user terminal, means for inputting the questions into a generative artificial intelligence model and generating answers, means for transmitting the generated answers to the user terminal, and means for displaying the answers on the user terminal. This not only provides quick and accurate answers to a large number of questions, but also minimizes the need for intervention by specialized medical staff, thereby reducing costs. Furthermore, users can obtain a consistent user experience, and it becomes easier to input questions and confirm answers.

[0841] A "user terminal" is a device used by a user to input questions and receive answers, and includes personal digital assistants and computers.

[0842] A "server" is a computing system that receives questions sent from a user terminal, inputs those questions into a generative artificial intelligence model, and then sends the generated answers back to the user terminal.

[0843] A "generative artificial intelligence model" refers to an artificial intelligence algorithm that uses natural language processing technology to generate appropriate answers to input questions.

[0844] A "question" is information that includes medical questions and descriptions of symptoms entered by the user, transmitted from the user's terminal to the server.

[0845] "Answer" refers to information, including solutions and advice, to a user's question, which is generated by a generative artificial intelligence model and sent to the user's terminal via a server.

[0846] "Natural language processing technology" refers to the techniques used by generative artificial intelligence models to understand questions and generate appropriate answers in human language.

[0847] "Means of receiving" refers to the collective functions and interfaces that a server uses to receive questions sent from a user's terminal and process them as information.

[0848] "Means of transmission" refers to the collective functions and interfaces that the server uses to send the generated response back to the user's terminal.

[0849] "Means of display" refers to the collective term for functions and interfaces that visually present the responses received by the user's terminal to the user.

[0850] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and returning the answers to the user terminal. This system includes a server, a user terminal, and a generative artificial intelligence model.

[0851] First, the user uses a user device such as a smartphone or personal computer to input questions about their symptoms or medical issues. For example, they might input a question like, "My throat has been sore since yesterday, what should I do?" This entered question is then sent from the device to the server.

[0852] Next, the server inputs the question received from the user's terminal into a generative artificial intelligence model. In this case, a model utilizing natural language processing technology (e.g., OpenAI GPT-3) is used as the generative AI model. The server sends the received question to the generative AI model as part of a prompt. For example, the prompt might be: "A user has entered a medical consultation question. The question is: 'My throat has been sore since yesterday, what should I do?' Please generate an appropriate answer to this question."

[0853] Generative artificial intelligence models produce appropriate answers to input questions. For example, if the question is "My throat has been sore since yesterday, what should I do?", the generative AI model will produce an answer such as, "If you have a sore throat, first we recommend drinking warm tea or water with honey. Also, gargle and, if necessary, try using over-the-counter throat lozenges or pain relievers. If symptoms persist for more than three days or worsen, please consider consulting a medical professional."

[0854] The server then sends the generated response to the user's device. The receiving device displays the response to the user. For example, if the user asks the question mentioned earlier, the device will display: "AI response: If you have a sore throat, we recommend drinking warm tea or water with honey first. Also, gargle and use commercially available throat lozenges or pain relievers if necessary. If symptoms persist for more than 3 days or worsen, please consider consulting a medical professional."

[0855] This entire process allows users to quickly obtain highly accurate answers to their medical inquiries. Furthermore, it enables consistent responses to numerous questions while minimizing the need for specialized medical staff, thus providing an efficient medical consultation service.

[0856] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0857] Step 1:

[0858] The user enters the question. The user uses their device (smartphone or personal computer) to enter the question into a text input field on a dedicated application or web browser and presses the submit button. The user's medical questions are provided as input, and the question text is generated as output.

[0859] Step 2:

[0860] The terminal sends a question to the server. The terminal sends the question text data to the server as an HTTP POST request. It receives the question text data as input and generates a request that is sent to the server as output.

[0861] Step 3:

[0862] The server receives the question. The server receives the HTTP POST request sent from the terminal and retrieves the question text. It receives the request from the terminal as input and extracts the question text as output.

[0863] Step 4:

[0864] The server inputs the question into a generative artificial intelligence model. The server sends the received question text along with a prompt to the generative artificial intelligence model as an API request. For example, the prompt might be in the format: "A user has entered a medical consultation question. The question is: 'My throat has been sore since yesterday, what should I do?' Please generate an appropriate answer to this question." It receives the question text as input and generates an API request as output.

[0865] Step 5:

[0866] A generative artificial intelligence model generates the answer. The generative AI model uses natural language processing techniques based on the received prompt to generate appropriate answer text. For example, if the question is "My throat has been sore since yesterday, what should I do?", it will generate the following answer: "If you have a sore throat, first we recommend drinking warm tea or water with honey. Also, gargle and, if necessary, try using over-the-counter throat lozenges or pain relievers. If symptoms persist for more than three days or worsen, please consider consulting a medical professional." It takes a prompt as input and generates answer text as output.

[0867] Step 6:

[0868] The server receives the generated response. The server receives the response text as an API response from the generative artificial intelligence model. It receives the response from the generative artificial intelligence model as input and obtains the response text as output.

[0869] Step 7:

[0870] The server sends the response to the terminal. The server sends the retrieved response text to the terminal as an HTTP response. It receives the response text as input and generates a response that is sent to the terminal as output.

[0871] Step 8:

[0872] The device displays the answer to the user. The device displays the received answer text on the screen. For example, it might display text such as, "AI response: If you have a sore throat, we recommend first drinking warm tea or water with honey. Also, gargle and use commercially available throat lozenges or pain relievers if necessary. If symptoms persist for more than 3 days or worsen, consider consulting a medical professional." It receives the answer text as input and displays it as output.

[0873] (Application Example 1)

[0874] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0875] The present invention aims to alleviate the anxieties and questions users have when making electronic payments and to provide a safe and secure payment environment. In particular, there is a need for a method to provide prompt and appropriate answers to questions related to health and safety. Modern consumers often have questions, especially regarding health, when making payments, and by responding quickly to these questions, consumer satisfaction can be improved.

[0876] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0877] In this invention, the server includes means for receiving questions entered from a user terminal, means for inputting the received questions into a generative artificial intelligence model to generate answers, and means for transmitting the generated answers to the user terminal. This makes it possible to generate quick and appropriate answers to health and safety-related questions when making electronic payments at the user terminal, thereby alleviating consumer anxiety and providing a safe and secure payment environment.

[0878] A "user terminal" is a device used by a user to input questions or make electronic payments, and can be a smartphone or a personal computer.

[0879] A "question" refers to any doubts or concerns entered by the user, particularly those related to health and safety.

[0880] A "generative artificial intelligence model" is a model that uses natural language processing technology to generate appropriate answers to input questions.

[0881] A "server" is a device that receives questions from a user's terminal, inputs them into a generative artificial intelligence model, and sends the generated answers back to the user's terminal.

[0882] An "answer" refers to the response or advice generated by a generative artificial intelligence model based on a question.

[0883] "Electronic payment" refers to the act of a user paying for goods or services online or offline using digital means.

[0884] This invention is a system designed to alleviate the anxieties and questions users may have when making electronic payments. This system includes a user terminal, a server, and a generative artificial intelligence model.

[0885] 1. User terminal

[0886] The user terminal is a smartphone or personal computer, and it functions as an interface for the user to input questions. For example, when a user makes a payment, they might input, "Is it safe to use this payment method?"

[0887] 2. Server

[0888] The server plays a central role in inputting questions received from user terminals into a generative artificial intelligence model and generating answers. Specifically, the server uses a web framework such as Flask to build a RESTful API, receive questions, and send them to the AI ​​model.

[0889] 3. Generative Artificial Intelligence Models

[0890] Generative artificial intelligence models use natural language processing techniques to generate appropriate answers based on input questions. For example, in response to the question, "Is this payment method safe to use?", it might generate the answer, "This payment method meets our strict security standards and can be used safely." This AI model is built on advanced natural language processing models such as BERT and GPT.

[0891] 4. Data processing and calculations

[0892] When a user enters a question on their device, that question is sent to the server. The server receives the question and inputs it into a generative artificial intelligence model via an API request. The generative AI model analyzes the input question and generates an appropriate answer. The generated answer is then sent back to the user's device via the server and presented to the user.

[0893] Specific example

[0894] When a user is shopping using the "Careful Pay" app, they can instantly enter the question, "Is it safe to use this payment method?" and the AI ​​within the app will respond, "This payment method meets our strict security standards and can be used safely."

[0895] Example of a prompt

[0896] Input: "Is this payment method safe to use?"

[0897] Generated response: "This payment method meets our strict security standards and can be used safely."

[0898] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0899] Step 1:

[0900] The user enters the question on their device.

[0901] Operation: The user uses a dedicated application on their smartphone or personal computer to enter a question, for example, "Is it safe to use this payment method?"

[0902] Input: The question entered by the user.

[0903] Output: The user's terminal generates the question data.

[0904] Step 2:

[0905] The terminal sends the entered question to the server.

[0906] Operation: The user's terminal sends the entered question data to the server as an HTTP request via an internet connection.

[0907] Input: Question data generated by the user's terminal.

[0908] Output: The server receives the question data.

[0909] Step 3:

[0910] The server inputs the received questions into a generative artificial intelligence model.

[0911] Operation: The server uses a web framework such as Flask to send the received question data as an API request to a generative artificial intelligence model.

[0912] Input: Question data received by the server.

[0913] Output: The generative artificial intelligence model obtains input data to analyze the question.

[0914] Step 4:

[0915] A generative artificial intelligence model generates an answer based on the question.

[0916] Operation: A generative artificial intelligence model (e.g., BERT or GPT) analyzes the question using natural language processing techniques and generates an appropriate answer. For example, it might generate an answer such as, "This payment method meets our strict security standards and can be used safely."

[0917] Input: Question data for analysis by a generative artificial intelligence model.

[0918] Output: Response data generated by a generative artificial intelligence model.

[0919] Step 5:

[0920] The server receives the generated response and sends it to the user's terminal.

[0921] Operation: The server sends the response data received from the generative artificial intelligence model to the user's terminal as an HTTP response.

[0922] Input: Response data generated by a generative artificial intelligence model.

[0923] Output: Response data received by the user's terminal.

[0924] Step 6:

[0925] The user's device displays the answer to the user.

[0926] Operation: The user's device displays the received response data in the user interface. For example, if the user is using the "Careful Pay" app, the app will display the response "This payment method meets our strict security standards and can be used safely" on the screen.

[0927] Input: Response data received by the user's terminal.

[0928] Output: The answer displayed to the user.

[0929] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0930] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and further combining this with an emotion engine that recognizes the user's emotions, before returning the answers to the user terminal. This system includes a server, a user terminal, a generative artificial intelligence model, and an emotion engine.

[0931] First, the user uses a smartphone or personal computer to input questions about their symptoms or medical issues. For example, the user might input, "I have a headache, what should I do?" This entered question is then sent from the device to the server.

[0932] Next, the server first inputs the question received from the user's terminal into the emotion engine. The emotion engine analyzes the text entered by the user and determines the user's emotional state. For example, if the user's question contains the emotion of "urgent," the emotion engine recognizes this and determines the emotional state to be "urgent."

[0933] The server then considers the emotional state determined by the emotion engine and inputs the received question into a generative artificial intelligence model to generate an answer. This generative AI model uses natural language processing technology to produce an appropriate answer based on medical knowledge in response to the input question. For example, if the question is "I have a headache, what should I do?" and the emotional state is determined to be "urgent," the generative AI model will generate an answer such as, "First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to drink plenty of fluids and rest."

[0934] Next, the server sends the generated answer to the device. The receiving device then displays the answer to the user. For example, if the user asks the question mentioned earlier, the device will display the answer: "AI answer: First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay hydrated and rest."

[0935] This entire process allows users to receive highly accurate medical advice quickly and with appropriate responses that take their emotions into consideration. This system enables the provision of efficient and accurate medical consultation services that also consider the user's emotional state.

[0936] The following describes the processing flow.

[0937] Step 1:

[0938] The user enters their symptoms or medical questions on the device. For example, the user might enter, "I have a headache, what should I do?"

[0939] Step 2:

[0940] The terminal sends the user's entered question data to the server. Here, the data is transmitted over the network.

[0941] Step 3:

[0942] The server inputs the question received from the terminal into the sentiment engine. The sentiment engine analyzes the question text and determines the user's emotional state.

[0943] Step 4:

[0944] The server prepares input data for the generative artificial intelligence model based on the emotion determination results from the emotion engine. For example, if the user's emotion is determined to be "anxiety," that information is added to the generative artificial intelligence model.

[0945] Step 5:

[0946] The server inputs question data and sentiment information into a generative artificial intelligence model to generate an answer. For example, if the question is "I have a headache, what should I do?" and the user is feeling "anxious," the generative AI model will generate an answer such as, "First, calm down, take a deep breath, and drink plenty of water. If the headache persists, please see a doctor."

[0947] Step 6:

[0948] The server sends the generated response data to the terminal. Here too, the data is transmitted over the network.

[0949] Step 7:

[0950] The device displays the response received from the server to the user. For example, the user's device might display the response: "AI response: First, calm down, take a deep breath, and drink plenty of water. If your headache persists, please consult a medical professional."

[0951] Through these processing steps, highly accurate medical consultations that take the user's emotions into account are provided quickly. This system enables efficient medical consultation services by providing appropriate advice tailored to the user's emotional state.

[0952] (Example 2)

[0953] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0954] Traditional medical consultation systems often provided answers without considering the user's emotional state, resulting in inappropriate responses tailored to the user's psychological condition. Consequently, users did not receive the support and advice they truly needed, leading to increased dissatisfaction and anxiety.

[0955] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving a question input from a user terminal, means for analyzing the received question using emotion analysis means to determine the emotional state, means for inputting the determined emotional state and the question into a generative artificial intelligence model to generate an answer, and means for transmitting the generated answer to the user terminal. This makes it possible to provide appropriate medical consultation answers that take into account the user's emotional state.

[0956] A "user terminal" refers to an electronic device used by a user to input questions, such as a smartphone or personal computer.

[0957] A "server" refers to a central processing unit that processes questions received from user terminals and performs sentiment analysis and response generation.

[0958] "Emotional analysis means" refers to technical methods for analyzing text data entered by users to determine the user's emotional state (e.g., urgency, anxiety, doubt).

[0959] A "generative artificial intelligence model" refers to an artificial intelligence model that uses natural language processing techniques to generate appropriate answers to input questions.

[0960] "Answer generation means" refers to a technical means for inputting a question and emotional state into a generative artificial intelligence model to generate an appropriate medical answer.

[0961] "Questions" refer to text data related to medical consultations that users input through their devices.

[0962] "Answer" refers to text data generated by a generative artificial intelligence model, containing appropriate responses and advice to the user's question.

[0963] "Emotional state" refers to states such as urgency, anxiety, and relief, which are obtained by analyzing the emotions contained in the questions entered by the user.

[0964] The present invention relates to a system that has a series of processes for automatically processing medical consultation questions transmitted from a user terminal, generating answers using a generative artificial intelligence model, and further combining this with an emotion engine that recognizes the user's emotions, before returning the answers to the user terminal. This system includes a server, a user terminal, a generative artificial intelligence model, and an emotion engine.

[0965] First, the user uses a smartphone or personal computer to input questions about their symptoms or medical issues. For example, the user might input, "I have a headache, what should I do?" This entered question is then sent from the device to the server.

[0966] Next, the server first inputs the question received from the user's terminal into the emotion engine. The emotion engine analyzes the text entered by the user and determines the user's emotional state. For example, if the user's question contains the emotion of "urgent," the emotion engine recognizes this and determines the emotional state to be "urgent."

[0967] The server then considers the emotional state determined by the emotion engine and inputs the received question into a generative artificial intelligence model to generate an answer. This generative AI model uses natural language processing technology (e.g., OpenAI GPT-4) to produce an appropriate answer based on medical knowledge in response to the input question. For example, if the question is "I have a headache, what should I do?" and the emotional state is determined to be "urgent," the generative AI model will generate the answer "First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to drink plenty of fluids and rest."

[0968] Next, the server sends the generated answer to the device. The receiving device then displays the answer to the user. For example, if the user asks the question mentioned earlier, the device will display the answer: "AI answer: First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay hydrated and rest."

[0969] The following is an example of a prompt statement that demonstrates the specific operation of this system:

[0970] A user asked: "I have a headache, what should I do?"

[0971] The emotional state was determined to be "urgent."

[0972] --- Please generate an AI response below ---

[0973] answer:

[0974] This entire process allows users to receive highly accurate medical advice quickly and with appropriate responses that take their emotions into consideration. This system enables the provision of efficient and accurate medical consultation services that also consider the user's emotional state.

[0975] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0976] Step 1:

[0977] Users use their smartphones or personal computers to enter questions about medical consultations into a text box. For example, they might enter a question like, "I have a headache, what should I do?"

[0978] Input: Text question (user input)

[0979] Output: Input text question

[0980] Step 2:

[0981] When the user presses the "Send" button, the device begins the process of sending the entered text data to the server. Specifically, it uses the TCP / IP protocol to generate an HTTP POST request, packages the entered text data in JSON format, and sends it to the server over the internet.

[0982] Input: Entered text question (by pressing the send button on the device)

[0983] Output: Text data in JSON format sent to the server

[0984] Step 3:

[0985] The server parses the received HTTP POST request and extracts the text data. Specifically, it uses a JSON parser to extract the text data.

[0986] Input: Submitted text data in JSON format

[0987] Output: Extracted text data

[0988] Step 4:

[0989] The server sends the analyzed text data to the sentiment engine and begins sentiment analysis. Specifically, it calls the sentiment engine's API to perform sentiment analysis on the text.

[0990] Input: Extracted text data

[0991] Output: API request to the emotion engine

[0992] Step 5:

[0993] The emotion engine analyzes text and determines the user's emotional state. Specifically, it uses natural language processing algorithms to assign emotion labels (such as urgency, anxiety, and relief).

[0994] Input: API request to the emotion engine (text data)

[0995] Output: Determined emotion label (e.g., "Urgent")

[0996] Step 6:

[0997] The server considers the emotion labels obtained from the emotion engine and inputs them, along with the question, as prompts to the generative artificial intelligence model. Specifically, it calls the API of the generative AI model (e.g., GPT-4) to generate prompts in the following format:

[0998] User question: "I have a headache, what should I do?"

[0999] Emotional state: "Urgent"

[1000] --- Please generate an AI response below ---

[1001] answer:

[1002] Input: Question and sentiment label

[1003] Output: Prompts for the generative artificial intelligence model

[1004] Step 7:

[1005] Generative artificial intelligence models generate appropriate responses based on prompts. Specifically, they generate text and create responses based on the medical knowledge the model has learned.

[1006] Input: Prompt text for a generative artificial intelligence model

[1007] Output: Generated response (Example: "First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay well-hydrated and rest.")

[1008] Step 8:

[1009] The server receives the generated response and sends it to the user's terminal as an HTTP response. Specifically, it packages the generated response in JSON format and sends the response.

[1010] Input: Generated answer

[1011] Output: HTTP response to the user's terminal

[1012] Step 9:

[1013] The device displays the received response to the user. Specifically, it uses a module for displaying text on the screen to show the response: "AI response: First, calm down and regulate your breathing, and contact a medical professional if necessary. If the headache persists, it is also important to stay hydrated and rest."

[1014] Input: HTTP response (generated answer)

[1015] Output: Text response displayed to the user

[1016] (Application Example 2)

[1017] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1018] Conventional medical consultation systems often generate simple responses that do not take into account the user's emotional state, sometimes failing to provide appropriate support. Furthermore, they are not designed for use in vehicles, making it impossible to provide health consultation services during long-distance travel. Therefore, there is a need to develop a system that considers the user's emotional state and provides highly accurate medical advice via in-vehicle terminals.

[1019] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1020] In this invention, the server includes means for receiving questions entered from a user terminal; means for inputting the received questions into an emotion engine and determining the user's emotional state; means for inputting the received questions into a generative artificial intelligence model, taking into account the emotional state determined by the emotion engine, and generating an answer; means for transmitting the generated answer to the user terminal; and means including a technique for transmitting questions entered from a tablet or smartphone terminal used as an in-vehicle terminal to an in-vehicle cloud server, and executing processing on the cloud server using the emotion engine and the generative artificial intelligence model. This makes it possible to easily provide appropriate medical consultations that reflect the user's emotional state even while in a vehicle.

[1021] "User terminal" refers to a smartphone, tablet, or similar device used by the user to input and receive questions.

[1022] An "emotion engine" refers to a system that analyzes text entered by a user and determines the user's emotional state.

[1023] A "generative artificial intelligence model" refers to an artificial intelligence model that generates appropriate answers using natural language processing techniques based on questions received from users.

[1024] "In-vehicle terminal" refers to a tablet or smartphone device used inside a vehicle.

[1025] A "cloud server" refers to a server that exists on the internet and provides data processing and storage functions.

[1026] A "question" refers to the content of an inquiry that a user submits to the system via an input terminal.

[1027] "Answer" refers to the response that a generative artificial intelligence model generates in response to a question and provides to the user.

[1028] This invention relates to a system that automatically processes questions transmitted from a user terminal and returns appropriate answers through a combination of a generative artificial intelligence model and an emotion engine. This system includes a server, an in-vehicle terminal (such as a smartphone or tablet), a generative artificial intelligence model, and an emotion engine.

[1029] Users enter questions using an in-vehicle terminal. For example, a passenger might enter, "I'm tired, what should I do?" This question is sent from the in-vehicle terminal to a cloud server. The cloud server processes the question using the following hardware and software.

[1030] Hardware to use

[1031] In-car tablet or smartphone (e.g., Android device)

[1032] Internet connection

[1033] Cloud servers (e.g., AWS, Google Cloud)

[1034] Software to use

[1035] Emotion engine (e.g., Microsoft Azure Sentiment Analysis API)

[1036] Generative artificial intelligence models (e.g., GPT-3, BERT)

[1037] Data processing and data calculation

[1038] 1. Receipt of submitted questions

[1039] When a user enters a question into an in-vehicle terminal, the data is sent to a cloud server in real time. For example, if a passenger enters the question, "I'm tired, what should I do?", that text data will be sent to the server.

[1040] 2. Determining the emotional state

[1041] The cloud server inputs the received question into the emotion engine to determine the user's emotional state. For example, if the user's question includes the emotion of "fatigue," the emotion engine will determine that emotional state.

[1042] 3. Generating Question and Answer

[1043] The emotional state determined by the emotion engine and the question are input into a generative artificial intelligence model. The generative AI model generates an appropriate answer based on the input question and emotional state. For example, it might generate an answer such as, "First, I recommend drinking plenty of water and getting some rest. Deep breathing can also help you relax."

[1044] 4. Submitting and displaying responses

[1045] The generated answers are sent from the server to the in-car terminals and displayed to the passengers. For example, if a passenger asks, "I'm tired, what should I do?", the terminal will display, "First, we recommend that you drink plenty of fluids and get some rest. Deep breathing can also help you relax."

[1046] Example of a prompt

[1047] When entering the question: "Question: I have a headache, what should I do?"

[1048] When determining emotion: "Emotion: Urgent"

[1049] When the AI ​​generates input: "Input: I have a headache, what should I do? Emotional state: Urgent"

[1050] This will enable us to provide appropriate support for health consultations during long-distance travel and offer highly accurate, emotion-based advice.

[1051] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1052] Step 1:

[1053] The user enters a question into an in-vehicle terminal. For example, the user might type a question like "I'm tired, what should I do?" into a tablet or smartphone. The terminal receives the data, processes it, and converts it into a format that can be sent to a cloud server. The input data here is the user's question text, and the output data is the formatted question text that is sent to the server.

[1054] Step 2:

[1055] The server inputs the question received from the user's terminal into the emotion engine. The emotion engine analyzes the text data and determines the user's emotional state (e.g., fatigue, stress). In this process, the input data is the question text, and the output data is information about the determined emotional state. The server stores this emotional state information in a database.

[1056] Step 3:

[1057] The server inputs the emotional state determined by the emotion engine and the question into a generative artificial intelligence model. The generative AI model receives the prompt "Input: I'm tired, what should I do? Emotional state: Fatigue" and uses natural language processing techniques to generate an appropriate response. Here, the input data is a combination of the question and emotional state, and the output data is the generated response text.

[1058] Step 4:

[1059] The generated response is sent to the user's terminal by the server. The server formats the response received from the generative artificial intelligence model, converts it to an appropriate format, and transfers it to the in-vehicle terminal. The input data is the generated response text, and the output data is the formatted response for display on the user's terminal.

[1060] Step 5:

[1061] The in-vehicle terminal displays the user the response received from the server. For example, the terminal might display content such as, "First, we recommend that you drink plenty of fluids and get some rest. Deep breathing can also help you relax." In this case, the input data is a formatted response received from the server, and the output data is the response displayed on the screen. The user can visually confirm the response.

[1062] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1063] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1064] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1065] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1066] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1067] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1068] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1069] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1070] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1071] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1072] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1073] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1074] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1075] 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.

[1076] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1077] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1078] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1079] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1080] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1081] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1082] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1083] The following is further disclosed regarding the embodiments described above.

[1084] (Claim 1)

[1085] A means of receiving questions entered from the user's terminal,

[1086] A means for inputting the received question into a generative artificial intelligence model and generating an answer,

[1087] means for transmitting the generated response to the user terminal,

[1088] A system that includes this.

[1089] (Claim 2)

[1090] The system according to claim 1, characterized in that the user terminal is a smartphone or a personal computer.

[1091] (Claim 3)

[1092] The system according to claim 1, characterized in that the generative artificial intelligence model is a model that generates answers based on natural language processing.

[1093] (Claim 4)

[1094] The system according to claim 1, characterized in that the exchange of questions and answers is conducted via a network.

[1095] "Example 1"

[1096] (Claim 1)

[1097] A means of receiving questions entered from the user's terminal,

[1098] A means for inputting the received question into a generative artificial intelligence model and generating an answer,

[1099] means for transmitting the generated response to the user terminal,

[1100] A means for displaying the answer on the user terminal,

[1101] A system that includes this.

[1102] (Claim 2)

[1103] The system according to claim 1, characterized in that the user terminal is a portable information terminal or a computer.

[1104] (Claim 3)

[1105] The system according to claim 1, characterized in that the generative artificial intelligence model is a model that generates answers based on natural language processing technology.

[1106] "Application Example 1"

[1107] (Claim 1)

[1108] A means of receiving questions entered from the user's terminal,

[1109] A means for inputting the received question into a generative artificial intelligence model and generating an answer,

[1110] means for transmitting the generated response to the user terminal,

[1111] The electronic payment method executed on the user terminal,

[1112] When making a payment using the aforementioned electronic payment method, the means includes receiving questions about health and safety, inputting them into a generative artificial intelligence model, and generating answers.

[1113] A system that includes this.

[1114] (Claim 2)

[1115] The system according to claim 1, characterized in that the user terminal is a smartphone or a personal computer.

[1116] (Claim 3)

[1117] The system according to claim 1, characterized in that the generative artificial intelligence model is a model that generates answers based on natural language processing.

[1118] "Example 2 of combining an emotion engine"

[1119] (Claim 1)

[1120] A means of receiving questions entered from the user's terminal,

[1121] A means for analyzing the received question using an emotion analysis means and determining the emotional state,

[1122] A means for inputting the determined emotional state and question into a generative artificial intelligence model and generating an answer,

[1123] means for transmitting the generated response to the user terminal,

[1124] A system that includes this.

[1125] (Claim 2)

[1126] The system according to claim 1, characterized in that the user terminal is a smartphone or a personal computer.

[1127] (Claim 3)

[1128] The system according to claim 1, characterized in that the generative artificial intelligence model is a model that generates answers based on natural language processing.

[1129] "Application example 2 when combining with an emotional engine"

[1130] (Claim 1)

[1131] A means of receiving questions entered from the user's terminal,

[1132] A means for inputting the received question into an emotion engine and determining the user's emotional state,

[1133] A means for inputting the received question into a generative artificial intelligence model and generating an answer, taking into account the emotional state determined by the emotion engine,

[1134] means for transmitting the generated response to the user terminal,

[1135] A means including a technique for sending questions entered from a tablet or smartphone terminal used as an in-vehicle terminal to an in-vehicle cloud server, and for performing processing on the cloud server using an emotion engine and a generative artificial intelligence model,

[1136] A system that includes this.

[1137] (Claim 2)

[1138] The system according to claim 1, characterized in that the user terminal is a smartphone or a tablet.

[1139] (Claim 3)

[1140] The system according to claim 1, characterized in that the generative artificial intelligence model is a model that generates answers based on natural language processing. [Explanation of Symbols]

[1141] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving questions entered from the user's terminal, A means for inputting the received question into a generative artificial intelligence model and generating an answer, means for transmitting the generated response to the user terminal, A system that includes this.

2. The system according to claim 1, characterized in that the user terminal is a smartphone or a personal computer.

3. The system according to claim 1, characterized in that the generative artificial intelligence model is a model that generates answers based on natural language processing.

4. The system according to claim 1, characterized in that the exchange of questions and answers is conducted via a network.

Images