system

Abstract

We provide the system. [Solution] A means of receiving user input from a terminal, A method for analyzing received user input to extract concerns and questions, A means of querying a generative AI based on extracted problems and questions to generate appropriate answers, A means of sending the generated response to the terminal, A system that includes this.

Description

Technical Field

[0001] The technology disclosed herein relates to a system.

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern society, there are many people who find it difficult to talk about or verbalize their troubles to others. Also, the generation that uses social networking services (SNS) more often than phones is increasing, and its importance is growing. The number of suicides nationwide has increased by 2.7% compared to the previous year, the penetration rate of SNS is 80% or more, and the penetration rate of a specific messaging application has reached 87% or more. In this situation, there is an increasing need for a system that can resolve troubles that one cannot talk to others about without telling anyone.

Means for Solving the Problems

[0005] This invention provides a system that includes means for receiving user input from a terminal, means for analyzing the received user input to extract concerns and questions, means for querying a generative AI based on the extracted concerns and questions to generate an appropriate answer, and means for sending the generated answer to the terminal. This allows users to easily consult about their concerns through the terminal, and is expected to contribute to maintaining mental health and reducing the number of suicides. In this system, user input is in the format of a message from a social networking service, and a text generation model is used as the generative AI, thus enabling smooth and effective consultation.

[0006] A "terminal" is a device operated by a user, and is a device used for inputting and sending messages.

[0007] "User input" refers to messages and information entered by a user using a terminal and sent to the system.

[0008] "Means of receiving" refers to the function or process by which the system receives user input transmitted from a terminal.

[0009] "Means for analyzing and extracting concerns and questions" refers to a function or process that analyzes the content of received user input and identifies the specific content of the user's concerns and questions.

[0010] "Generative AI" refers to artificial intelligence that uses natural language processing technology to automatically generate appropriate answers to users' problems and questions.

[0011] "Means for generating appropriate answers" refers to a function or process that generates appropriate answers to problems and questions extracted using generative AI.

[0012] "Means of transmission" refers to the function or process for sending the generated response to the user's device.

[0013] A "system" is a collection of processes and devices that include these means, and is designed to effectively support users in seeking advice on their problems.

[0014] A "social networking service" is an online platform for people to communicate with each other via the internet.

[0015] A "text generation model" is a type of artificial intelligence that generates appropriate text in response to input questions or instructions. [Brief explanation of the drawing]

[0016] [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] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of 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.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention aims to maintain mental health and reduce the number of suicides by providing a system that allows users to anonymously consult about their worries and questions. Specific embodiments are described below.

[0038] 1. User-side processing

[0039] What the user does first

[0040] The user launches a social networking service application on their device and accesses the official account "Trouble Consultation Service".

[0041] Enter message

[0042] The user enters their worries or questions as a text message and presses the send button. For example, they might type, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?"

[0043] 2. Processing on the terminal side

[0044] Send message

[0045] After the user presses the send button, the device sends the message to the server via the LINE API or similar.

[0046] 3. Server-side processing

[0047] Message received

[0048] The server receives user messages sent from the terminal.

[0049] Message Controller

[0050] The server analyzes the received messages and extracts the user's concerns and questions. Using natural language processing techniques, it identifies information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[0051] Answer generation by generative AI

[0052] The server queries a generative AI (for example, a text generation model) based on the analyzed data. The AI ​​generates an appropriate answer to the user's question. For example, it might generate an answer such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0053] Submit your response

[0054] The generated response is sent from the server to the user's device via the LINE API.

[0055] 4. User-side processing

[0056] Display and view answers

[0057] The response message received on the user's device is displayed. The user reads something like, "Message from the counseling service: If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0058] Specific example

[0059] Flow from user problem input to resolution

[0060] 1. User-side processing

[0061] A user enters "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" into the "Advice Service" on the LINE app and presses the send button.

[0062] 2. Processing on the terminal side

[0063] The device sends the user's message to the server via the LINE API.

[0064] 3. Server-side processing

[0065] The server receives the message and analyzes its contents. For example, it might extract information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[0066] The server queries the generative AI based on the extracted results, and the AI ​​generates a response such as, "If you're feeling down, it's important to set aside time to relax. You can change your mood by getting some moderate exercise or enjoying a hobby."

[0067] The server sends the generated response to the device via the LINE API.

[0068] 4. User-side processing

[0069] The device receives the response message from the server and displays it in the LINE app.

[0070] Users read messages such as, "Message from the advice service: If you're feeling down, it's important to take time to relax. Moderate exercise or enjoying a hobby can help lift your spirits," and then take the advice to heart.

[0071] This format allows users to easily discuss problems and questions they can't talk about with others and receive appropriate advice. This is expected to promote mental health and reduce the number of suicides.

[0072] The following describes the processing flow.

[0073] Step 1:

[0074] The user launches the LINE app and starts a chat with the official account "Trouble Consultation Service." The user types, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" and presses the send button.

[0075] Step 2:

[0076] The device sends the user's input message to the server via the LINE API. The message is forwarded to the server in the appropriate format.

[0077] Step 3:

[0078] The server receives messages from users via the LINE API. The received messages are stored as they are.

[0079] Step 4:

[0080] The server analyzes the received messages. Using natural language processing techniques, it extracts specific details about the user's concerns and questions. The analysis results include information such as "I'm feeling down about work" and "I'd like to know how to cheer myself up."

[0081] Step 5:

[0082] The server queries the generative AI based on the analysis results. It sends a specific question to the generative AI (for example, a text generation model), such as "Please tell me how to deal with a user who is feeling down at work."

[0083] Step 6:

[0084] A generative AI receives inquiries from the server and generates appropriate answers to the user's concerns. For example, it might generate answers such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0085] Step 7:

[0086] The server receives the response from the generative AI and converts it into an appropriate message format. The converted message is then sent to the user's device via the LINE API.

[0087] Step 8:

[0088] The device receives a message from the server and displays it. The user's LINE app displays the following message: "Message from the advice service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0089] Step 9:

[0090] Users read messages displayed on their devices and consider actions based on the advice. For example, they might try to change their mood by taking time to relax or getting some moderate exercise.

[0091] (Example 1)

[0092] 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."

[0093] In modern society, many people suffer from mental health problems and have questions, but are often unable to confide in others or receive appropriate advice. As a result, their mental health suffers, and we face the serious problem of an increase in the number of suicides. Therefore, there is a need to provide a system in which users can anonymously and safely discuss their problems and questions and receive appropriate advice.

[0094] 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.

[0095] In this invention, the server includes means for receiving user input from a terminal, means for analyzing the received user input to extract the content of worries and questions, means for sending prompt sentences to a generative AI based on the extracted content of worries and questions to generate an appropriate answer, means for sending the generated answer to the terminal, and means for displaying the generated answer on the user's terminal. This makes it possible for users to anonymously consult about worries and questions they cannot talk about with others and to receive appropriate advice.

[0096] A "device" refers to a device that a user can directly operate, and this includes smartphones, tablets, and personal computers.

[0097] "User input" refers to information such as text messages and voice commands that users send to the system using their terminals.

[0098] A "server" refers to a central control unit that receives user input and performs processing and analysis; this includes databases and application servers.

[0099] "Means of receiving" refers to a function or module that allows the server to receive input sent from the user's terminal.

[0100] "Means of analysis" refers to a function or module that analyzes received user input, understands and interprets its content, and extracts concerns and questions.

[0101] "Means of extraction" refers to a function or module used to extract specific information from analyzed data.

[0102] "Generative AI" refers to artificial intelligence models that generate appropriate output (in this case, an answer) based on input data, and includes models specifically designed for text generation.

[0103] A "prompt sentence" refers to the input sentence presented to a generative AI, which serves as the basis for the AI ​​to generate an appropriate response.

[0104] "Means of transmission" refers to a function or module for sending the generated response from the server to the user's terminal.

[0105] "Means of display" refers to a function or module for visually displaying the response received on the user's device.

[0106] "Natural Language Processing (NLP)" refers to techniques for processing and understanding human language, and includes text analysis and sentiment analysis.

[0107] An "appropriate response" refers to a message that is useful to the user's concerns or questions and includes appropriate advice and information relevant to the situation.

[0108] "Social networking services" refer to platforms for people to interact online, and specific examples include SNS apps and messaging services.

[0109] This invention aims to maintain mental health and reduce the number of suicides by providing a system that allows users to anonymously consult about their worries and questions. Specific embodiments are described below.

[0110] Hardware and software to be used

[0111] This system uses the following hardware and software:

[0112] Devices: Smartphones, tablets, PCs, etc.

[0113] Servers: Database server, application server

[0114] Software: Social networking service (SNS) applications (e.g., LINE), natural language processing libraries (e.g., spacy, NLTK), generative AI models (e.g., GPT-3®).

[0115] System operation

[0116] The operation of this system is as follows:

[0117] User-side processing

[0118] First, the user launches a social networking application on their device and accesses the official account "Trouble Consultation Service." The user then enters their problem or question as a text message and presses the send button. For example, they might type, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?"

[0119] Terminal-side processing

[0120] After the user presses the send button, the device sends the message to the server via the LINE API. At this time, the user's input is encoded, and an HTTP request is generated to send it to the LINE API.

[0121] Server-side processing

[0122] The server analyzes HTTP requests received through the LINE API and extracts the user's text message. Then, it uses natural language processing (NLP) to analyze the message content and extract the user's concerns and questions. For example, it identifies information such as "I'm feeling down at work" or "I want to know how to cheer myself up."

[0123] The server sends a prompt to the generative AI model based on the analysis results. For example, it might send a prompt like, "The user is asking, 'I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?' Please generate an appropriate answer for this situation." The generative AI model then generates an appropriate answer. For example, it might generate an answer like, "When you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0124] The generated response is sent from the server to the user's device via the LINE API.

[0125] User-side processing again

[0126] The response message received on the user's device is displayed. The user can gain specific advice by reading the message, such as "Message from the counseling service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0127] Specific example

[0128] Let's say a user enters the message "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" into the "Advice Service" on the LINE app and presses the send button. The device sends this message to the server via the LINE API. The server receives the message and extracts the information that the user is "feeling down because of work" and "wants to know how to cheer themselves up." Based on the extracted information, the server sends a prompt to the generative AI: "The user is asking 'I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?' Please generate an appropriate answer for this situation." The generative AI generates an answer such as "When you're feeling down, it's important to set aside time to relax. You can cheer yourself up by getting some moderate exercise or enjoying a hobby," and the server sends this to the user via the LINE API. The user can then check this content on their device and use it as a reference.

[0129] Thus, the system of the present invention provides users with quick and appropriate answers to their worries and questions, contributing to the maintenance of their mental health.

[0130] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0131] Step 1:

[0132] The user launches the SNS application and enters the details of their inquiry.

[0133] The user launches the SNS application on their device and accesses the official account "Trouble Consultation Service." Next, they enter their problem or question into the text box and press the send button.

[0134] Input: User's text message (e.g., "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?")

[0135] Output: The text message is passed to the sender on the terminal side.

[0136] Step 2:

[0137] The device sends a message to the server.

[0138] The device sends the user's input to the server via the LINE API. During this process, the input data is converted to JSON format and sent as an HTTP request.

[0139] Input: User's text message

[0140] Output: HTTP request sent to the server via the LINE API

[0141] Step 3:

[0142] The server receives the message

[0143] The server interprets HTTP requests received through the LINE API and extracts the user's text message. The message data is processed at a specific endpoint within the server.

[0144] Input: HTTP request received from LINE API

[0145] Output: User's text messages are stored on the server.

[0146] Step 4:

[0147] The server analyzes the message content.

[0148] The server uses natural language processing (NLP) techniques to analyze incoming messages. This process involves tokenizing the text and performing sentiment analysis and keyword extraction.

[0149] Input: User's text message

[0150] Output: Analyzed data (e.g., "I'm feeling down at work," "I want to know how to cheer myself up," etc.)

[0151] Step 5:

[0152] The server sends a prompt message to the generative AI model.

[0153] Based on the analysis results, the server generates and sends appropriate prompts to the generative AI model. For example, a prompt might read, "The user is asking, 'I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?' Please generate an appropriate answer to this situation."

[0154] Input: Analyzed data

[0155] Output: Prompt message sent to the generative AI model

[0156] Step 6:

[0157] Generative AI models generate answers.

[0158] The generative AI model receives a prompt and generates an appropriate response. The AI ​​provides the response in natural language and returns it to the server.

[0159] Input: Prompt message

[0160] Output: Response text such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0161] Step 7:

[0162] The server sends the generated response to the user's device via the LINE API.

[0163] The server uses the LINE API to send the retrieved responses to the user's device. The response data is encoded in JSON format and sent to the device as an HTTP request.

[0164] Input: Generated response text

[0165] Output: HTTP request sent via LINE API

[0166] Step 8:

[0167] The device displays the response message.

[0168] The user's device decodes messages received from the LINE API and displays them on the application's interface. The user can then read and refer to these messages.

[0169] Input: HTTP request received from LINE API

[0170] Output: A message from the counseling service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits.

[0171] (Application Example 1)

[0172] 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."

[0173] In modern society, systems that allow people to anonymously seek advice are important for maintaining mental health, but systems that provide health consultations related to meal delivery are still lacking. Therefore, there is a need for an effective system that provides health-conscious meal options in delivery services. To solve this problem, a new system is needed that can suggest appropriate meals according to the user's health condition and concerns.

[0174] 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.

[0175] In this invention, the server includes means for receiving user input from a terminal, means for analyzing the received user input to extract concerns and questions, means for querying a generative AI based on the extracted concerns and questions to generate an appropriate answer, means for transmitting the generated answer to the terminal, and, if the user input relates to meal delivery, means for generating dietary advice tailored to the user's health condition. This allows users to anonymously consult about meal options tailored to their health condition and individual concerns.

[0176] A "terminal" is an electronic device used to send and receive data via a communication network.

[0177] "User input" refers to information or messages entered by a user using a terminal.

[0178] "Analysis" refers to the process of analyzing received user input and extracting its contents.

[0179] "Concerns and questions" refer to information about problems or matters that users want to discuss or ask about.

[0180] "Generative AI" is an artificial intelligence technology that generates appropriate responses based on input information.

[0181] An "inquiry" is a process that requests a generative AI to generate an answer based on the analysis results.

[0182] "Answer" refers to a response generated by a generative AI to a user's question or concern.

[0183] "Sending" refers to the process of sending the generated response back to the user's device.

[0184] "Meal delivery" is a service that delivers meals ordered from a specific location to a location specified by the user.

[0185] "Health status" refers to the user's physical and mental health status.

[0186] "Dietary advice" refers to suggestions regarding meal menus and eating methods based on the user's health condition.

[0187] A "system" is a configuration in which multiple elements, such as terminals, servers, and generative AI, work together to achieve a function.

[0188] This invention provides a system that allows users to anonymously seek health advice regarding meal delivery. Specific embodiments are described below.

[0189] System program

[0190] The system uses the following hardware and software:

[0191] Hardware: Terminals that receive user input, servers that process data.

[0192] Software: Web framework "Flask", natural language processing library "transformers"

[0193] Program processing

[0194] 1. Receiving user input

[0195] The server receives health consultation messages related to meal delivery entered by the user via their terminal. User input is in text format and may include things like, "I've gained weight recently, do you have any recommendations for healthy menu items?"

[0196] 2. Message Analysis

[0197] The server analyzes received messages using natural language processing. Specifically, it extracts concerns and questions from the message content.

[0198] 3. Generating answers using generative AI

[0199] The server queries a generative AI, such as "GPT-3.5-turbo," based on the extracted concerns and questions, and generates appropriate answers. This includes dietary advice that takes health conditions into consideration.

[0200] 4. Submit your response

[0201] The generated response is sent from the server to the user's device. The user views this response on their device and uses the advice as a reference.

[0202] Specific example

[0203] The following is a concrete example of how this system works.

[0204] User input example

[0205] "I've gained weight recently. Do you have any recommendations for healthy meals?"

[0206] Example answers from a generative AI model

[0207] "If you've recently gained weight, healthy options like salads, grilled chicken, and smoothies are recommended. It's especially important to keep your calorie intake down at night."

[0208] In this way, users can easily receive anonymous dietary advice related to health. This system helps users make healthy choices within meal delivery services and can provide meal options tailored to their individual health conditions and concerns.

[0209] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0210] Step 1:

[0211] Receiving user input

[0212] Users enter and send health consultation messages related to meal delivery through their terminals. This input is in text format and may include phrases such as, "I've gained weight recently, do you have any recommendations for healthy menu options?"

[0213] Input: User consultation message sent from the device.

[0214] Output: User input data sent to the server.

[0215] Step 2:

[0216] Message received

[0217] The server receives user input messages sent from the terminal.

[0218] Input: A text message sent by the user from their device.

[0219] Output: Data of the received user message.

[0220] Step 3:

[0221] Message parsing

[0222] The server analyzes received messages using natural language processing (NLP) techniques to extract user concerns and questions. For example, it might extract information such as "I'm gaining weight" or "I'd like recommendations for healthy meals."

[0223] Input: Received user message data.

[0224] Output: Data on concerns and questions extracted through analysis.

[0225] Step 4:

[0226] Generating answers using generative AI

[0227] Based on the analyzed concerns and questions, the server queries a generative AI (e.g., GPT-3.5-turbo) to generate appropriate answers. The generated answers include dietary advice that takes the user's health condition into consideration. For example, "Healthy menu options such as salads, grilled chicken, and smoothies are recommended."

[0228] Input: Data on concerns and questions extracted through analysis.

[0229] Output: Response data generated by a generative AI.

[0230] Step 5:

[0231] Submit your response

[0232] The server sends the generated responses to the user's device, allowing the user to view them on their device. These responses may include advice and recommended meal options.

[0233] Input: Generated response data.

[0234] Output: The response message sent to the user's device.

[0235] Step 6:

[0236] Display and view answers

[0237] The user's device displays the response message received from the server, and the user reads it. For example, they might see a message like, "Healthy menu items such as salads, grilled chicken, and smoothies are recommended."

[0238] Input: The response message sent from the server.

[0239] Output: The advice displayed on the user's terminal.

[0240] 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.

[0241] The present invention aims to provide more accurate advice and support by combining a system for anonymously consulting about users' worries and questions with an emotion engine that recognizes emotions from the user's input messages. Specific embodiments are described below.

[0242] 1. User-side processing

[0243] What the user does first

[0244] The user launches a social networking service application on their device and accesses the official account "Trouble Consultation Service".

[0245] Enter message

[0246] The user enters their worries or questions as a text message and presses the send button. For example, they might type, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?"

[0247] 2. Processing on the terminal side

[0248] Send message

[0249] After the user presses the send button, the device sends the message to the server via the LINE API or similar.

[0250] 3. Server-side processing

[0251] Message received

[0252] The server receives user messages sent from the terminal.

[0253] Message Controller

[0254] The server analyzes the received messages and extracts the user's concerns and questions. Using natural language processing techniques, it identifies information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[0255] Emotion recognition by an emotion engine

[0256] The server uses an emotion engine to recognize the user's emotions based on the extracted information. For example, it identifies emotional states such as "depressed" or "anxious."

[0257] Answer generation by generative AI

[0258] The server queries a generative AI (for example, a text generation model) based on the analysis results and emotion recognition results. The AI ​​generates an appropriate response according to the user's emotional state. For example, it might generate a response such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0259] Submit your response

[0260] The generated response is sent from the server to the user's device via the LINE API.

[0261] 4. User-side processing

[0262] Display and view answers

[0263] The response message received on the user's device is displayed. The user reads something like, "Message from the counseling service: If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0264] Specific example

[0265] Flow from user problem input to resolution

[0266] 1. User-side processing

[0267] A user enters "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" into the "Advice Service" on the LINE app and presses the send button.

[0268] 2. Processing on the terminal side

[0269] The device sends the user's message to the server via the LINE API.

[0270] 3. Server-side processing

[0271] The server receives the message and analyzes its contents. For example, it might extract information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[0272] The server uses an emotion engine to recognize emotions based on the extracted results. For example, it identifies emotional states such as "depressed" or "anxious."

[0273] The server queries the generative AI based on the analysis results and emotion recognition results, and the AI ​​generates a response such as, "If you're feeling down, it's important to set aside time to relax. You can change your mood by getting some moderate exercise or enjoying a hobby."

[0274] The server sends the generated response to the device via the LINE API.

[0275] 4. User-side Processing

[0276] The terminal receives the response message from the server and displays it on the LINE app.

[0277] The user reads content such as "Message from the Counseling Service: If you're feeling down, it's important to set aside time to relax. You can change your mood by enjoying moderate exercise or hobbies." and refers to the advice.

[0278] In this way, the user can easily consult about worries and questions that they can't talk to others about and receive appropriate advice. Also, by using the emotion engine, more accurate answers according to the user's emotional state are provided, so it is expected that maintaining mental health will be further promoted and the number of suicides will be reduced.

[0279] The following explains the processing flow.

[0280] Step 1:

[0281] The user launches the LINE app and starts a conversation with the official account "Counseling Service". The user enters "Recently, work isn't going well and I'm feeling down. What can I do to feel better?" and presses the send button.

[0282] Step 2:

[0283] The terminal sends the user's input message to the server via the LINE API. The message is transferred to the server in an appropriate format.

[0284] Step 3:

[0285] The server receives the message from the user via the LINE API. The received message is saved as it is.

[0286] Step 4:

[0287] The server analyzes the received message. Using natural language processing technology, it extracts the specific content of the user's worries and questions. As analysis results, information such as "feeling down at work" and "wanting to know ways to change my mood" is obtained.

[0288] Step 5:

[0289] Based on the information extracted by the server, it uses an emotion engine to recognize the user's emotions. For example, emotional states such as "feeling down" and "anxious" are identified.

[0290] Step 6:

[0291] The server makes an inquiry to the generative AI based on the analysis results and emotion recognition results. It sends a specific question such as "Please tell me how to deal with the situation when the user is feeling down at work" to the generative AI.

[0292] Step 7:

[0293] The generative AI receives the inquiry from the server and generates an appropriate answer according to the user's emotional state. For example, an answer such as "When you're feeling down, it's important to set aside some time to relax. You can change your mood by enjoying moderate exercise or hobbies." is generated.

[0294] Step 8:

[0295] The server receives the answer obtained from the generative AI and converts it into an appropriate message format. The converted message is sent to the user's terminal through the LINE API.

[0296] Step 9:

[0297] The device receives a message from the server and displays it. The user's LINE app displays the following message: "Message from the advice service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0298] Step 10:

[0299] Users read messages displayed on their devices and consider actions based on the advice. For example, they might try to change their mood by taking time to relax or getting some moderate exercise.

[0300] (Example 2)

[0301] 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".

[0302] Conventional counseling systems have had difficulty accurately recognizing users' emotions, resulting in answers that do not adequately address the user's psychological state. This has led to decreased user satisfaction and a lack of essential support. This invention aims to solve these problems and provide more accurate answers that are tailored to the user's emotional state.

[0303] 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 user input from a terminal, means for analyzing the received user input to extract the content of worries and questions, means for recognizing the user's emotions using an emotion engine based on the extracted content of worries and questions, means for generating an answer corresponding to the recognized user's emotions, means for querying a generative AI to generate an appropriate answer, and means for transmitting the generated answer to the terminal. This makes it possible to provide more accurate answers that correspond to the user's emotional state.

[0304] "Terminal" refers to a device used by a user to send an input message.

[0305] "User input" refers to a text message including the user's troubles and questions sent using a terminal.

[0306] "Receiving means" refers to a function or method for a server to receive user input sent from a terminal.

[0307] "Analyzing means" refers to a function or method for analyzing the content of received user input and extracting specific information about troubles and questions.

[0308] <s "Emotion engine" refers to software or an algorithm for recognizing the user's emotion based on the extracted troubles and question content.

[0309] "Recognizing means" refers to a function or method for specifying the user's emotional state using an emotion engine.

[0310] "Generative AI" refers to artificial intelligence technology for generating an appropriate answer in natural language based on the input information.

[0311] "Generating means" refers to a function or method for creating an answer according to the user's troubles and emotional state using generative AI.

[0312] "Sending means" refers to a function or method for sending back the generated answer to the terminal.

[0313] The purpose of the present invention is to provide more accurate advice and support by combining an emotion engine in a system for anonymously consulting the user's troubles and questions. The configuration and operation of this system will be specifically described below.

[0314] The user launches a social networking service application on their device and accesses the official account "Trouble Consultation Service." They enter their troubles or questions as a text message, for example, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" and then press the send button to send it.

[0315] The terminal sends messages sent by the user to the server via the communication network. The communication protocol used for this is the common HTTP request. Messages are sent using services such as the LINE API.

[0316] The server receives user messages sent from terminals. The received messages are first analyzed using natural language processing (NLP) techniques. For example, natural language processing libraries such as spaCy or BERT are used for this analysis. Through this analysis, information about specific problems or questions is extracted from the message content. For example, information such as "I'm feeling down at work" or "I want to know how to cheer myself up" may be identified.

[0317] Next, the server uses an emotion engine to recognize the user's emotions based on the extracted information. This emotion recognition can utilize emotion recognition software such as IBM Watson®, Aylien, or Microsoft® Text Analytics API. This allows the server to identify emotional states such as "depressed" or "anxious."

[0318] Based on the emotion recognition results and analysis results, the server queries a generative AI model. For example, OpenAI's GPT-3 is used as the generative AI model. Specific prompt statements are input to this AI model, and an appropriate response is generated. Examples of prompt statements include the following:

[0319] "The user is feeling depressed. The following input is advice regarding the user's depression at work: Moderate exercise and enjoying hobbies can help lift their spirits."

[0320] The generated responses might include something like, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0321] Finally, the server sends the generated response to the device via the LINE API. The device displays the received response message in the LINE app, allowing the user to view it and receive advice.

[0322] As described above, the present invention makes it possible to automatically generate and provide appropriate advice according to the user's emotional state. This allows users to receive more specific and appropriate support for their worries and questions.

[0323] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0324] Step 1:

[0325] The user launches the LINE app and accesses the official account "Trouble Consultation Service".

[0326] Specifically, the user searches for the official account within the LINE app and opens the chat window.

[0327] Step 2:

[0328] The user enters their concerns or questions as a text message and presses the send button.

[0329] Input: A text message entered by the user (e.g., "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?")

[0330] Output: Message sent via LINE API

[0331] At this point, the user presses the "Send" button, sending a text message to the server.

[0332] Step 3:

[0333] The device sends the entered message to the server via the LINE API.

[0334] Input: Text message sent by the user

[0335] Output: Messages are received on the server via the LINE API.

[0336] Specifically, the device sends the message to the server in the format of an HTTP request using the LINE API.

[0337] Step 4:

[0338] The server analyzes the received messages and extracts the content of the concerns and questions.

[0339] Input: Text message received from LINE API

[0340] Output: Specific information about the analyzed worries and questions (e.g., "I'm feeling down about work," "I want to know how to change my mood")

[0341] The server analyzes the received message using a natural language processing library (e.g., spaCy or BERT) and extracts specific information.

[0342] Step 5:

[0343] The server uses an emotion engine to recognize the user's emotions based on the extracted information.

[0344] Input: Information on extracted problems and questions

[0345] Output: Recognized user emotional state (e.g., "depressed," "anxious")

[0346] This involves performing specific actions to identify the user's emotional state using an emotion engine (e.g., IBM Watson, Aylien, Microsoft Text Analytics API).

[0347] Step 6:

[0348] The server inputs prompts into a generative AI model based on the analysis results and emotion recognition results, generating an appropriate response.

[0349] Input: Analysis results and emotion recognition results

[0350] Output: Generated response text (Example: "When you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits.")

[0351] The server generates prompts for a generative AI model (e.g., OpenAI's GPT-3), and an example of a prompt is as follows: "The user is feeling depressed. The following input is advice regarding the user's depression at work. Moderate exercise and enjoying hobbies can help lift your spirits."

[0352] Step 7:

[0353] The server sends the generated response to the device via the LINE API.

[0354] Input: Generated response text

[0355] Output: Message sent to the device via LINE API

[0356] Specifically, the generated response is sent in the format of an HTTP request using the LINE API.

[0357] Step 8:

[0358] The device displays the response message from the server in the LINE app.

[0359] Input: Response message received from the server

[0360] Output: The response text displayed within the LINE app.

[0361] Specifically, the device displays the message received from the LINE API.

[0362] Step 9:

[0363] View the response messages the user has received and get advice.

[0364] Input: Answer text displayed within the LINE app

[0365] Output: Advice the user receives (Example: "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits.")

[0366] Specifically, the user reads messages within the LINE app and uses the appropriate advice as a reference.

[0367] (Application Example 2)

[0368] 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."

[0369] In recent years, with the increasing prevalence of autonomous vehicles and the resulting increase in the amount of time spent inside vehicles, there is a growing need to reduce the stress and anxiety users experience and provide a comfortable in-car environment. However, conventional systems lack the mechanisms to provide appropriate support in real time in response to users' emotional states. In particular, it is crucial to maintain users' mental well-being by appropriately recognizing negative emotions they experience and providing appropriate advice.

[0370] 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.

[0371] In this invention, the server includes means for receiving user input from a terminal, means for analyzing the received user input to extract concerns and questions, means for using an emotion engine to recognize the user's emotions based on the extracted concerns and questions, means for querying a generative AI based on the extracted concerns and questions and the recognized user emotions to generate an appropriate response, and means for transmitting the generated response to the terminal. This makes it possible to sense the stress and anxiety felt by the user in real time within an autonomous vehicle and provide appropriate advice according to their emotions.

[0372] A "terminal" is an electronic device used by a user for input, and includes smartphones, tablets, computers, and other similar devices.

[0373] A "server" is a computer system that receives requests from multiple terminals via a network and provides services or information in response to those requests.

[0374] "User input" refers to text and audio data of concerns and questions provided by users through their devices.

[0375] An "emotion engine" is software or an algorithm that recognizes and analyzes emotions from user input data.

[0376] "Generative AI" refers to artificial intelligence that generates natural language text based on a given prompt, and generally uses natural language processing techniques.

[0377] "Answer" refers to advice and information provided in response to a user's concerns or questions, and is text data generated by a generative AI.

[0378] An "autonomous vehicle" is a vehicle designed to drive automatically without human intervention.

[0379] An "infotainment system" is an in-vehicle system that provides information and entertainment to the vehicle's user, and includes displays, sound systems, and other components.

[0380] A "social networking service" is a platform on the internet that allows users to share information and communicate with each other.

[0381] A "text generation model" is a machine learning model used to generate natural language text, and is based on deep learning technology.

[0382] "Voice input" refers to input data provided by the user via voice, which is converted into text using speech recognition technology.

[0383] This invention is a system that senses the stress and anxiety experienced by users in autonomous vehicles in real time and provides appropriate advice. Specifically, it receives voice or text messages entered by the user in the vehicle, analyzes the user's concerns and questions based on their content, and recognizes their emotions using an emotion engine. Based on this, it generates an appropriate response using a generative AI and outputs it to the infotainment system either by displaying it or by voice.

[0384] The server receives messages entered from the user's terminal. This includes a step where, in the case of voice input, speech recognition technology is used to convert it into text. The server then analyzes the received text message to extract the user's concerns and questions. This analysis uses natural language processing (NLP) techniques to identify the user's intent and requests.

[0385] Next, the server uses an emotion engine to recognize the user's emotions from the analyzed worries and questions. The emotion engine identifies emotional states such as "feeling stressed" or "feeling anxious." This can be done using emotion recognition APIs such as IBM Watson or Microsoft Azure®.

[0386] Based on the recognized emotional state and the nature of the problem, the server sends a prompt to a generative AI to generate an appropriate response. For example, a text generation model such as OpenAI's GPT-3 is used as the generative AI. This prompt might take the following format:

[0387] "Please write advice on what to do if a user is experiencing significant stress."

[0388] The responses from the generative AI will be tailored to the user's emotional state. For example, "If you're feeling stressed, take a deep breath and relax. Listening to your favorite music or taking a short walk will refresh your mood."

[0389] The generated responses are sent from the server to the vehicle's infotainment system. The infotainment system presents the responses to the user through a display or audio output device. This reduces stress and anxiety the user feels in the car, resulting in a more comfortable driving experience.

[0390] For example, if a user in a car types "I've been feeling very stressed at work lately," the emotion engine will recognize the user's emotional state as "feeling very stressed" from this text. Then, the generative AI will generate advice such as, "If you're feeling stressed, take a deep breath and relax. Listening to your favorite music or taking a short walk can refresh your mood," and display it on the infotainment system.

[0391] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0392] Step 1:

[0393] The user enters a voice or text message using an input device (e.g., microphone, touchscreen) inside the autonomous vehicle. The user says, "I've been feeling very stressed at work lately." This voice is captured by the microphone or the text is entered.

[0394] Step 2:

[0395] The device converts the input audio data into text using speech recognition software (e.g., Google® Speech-to-Text API). The input is audio data, and the output is the converted text data. The text would be "I've been feeling very stressed at work lately."

[0396] Step 3:

[0397] The terminal sends the received text message to the server. The input is text data, and the output is the data sent to the server. The server receives the user's message.

[0398] Step 4:

[0399] The server analyzes received text messages using natural language processing (NLP) techniques (e.g., spaCy) to extract concerns and questions. The input is text data, and the output is the analyzed elements of concerns and questions (e.g., "work," "stress").

[0400] Step 5:

[0401] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) based on the analyzed elements to recognize the user's emotional state. The input is the analyzed elements, and the output is the recognized emotional state (e.g., "stressed").

[0402] Step 6:

[0403] Based on the recognized emotional state and the nature of the problem, the server sends a prompt to a generative AI (e.g., OpenAI GPT-3) to generate an appropriate response. The input is a prompt sentence (e.g., "Please write advice on what to do if the user is feeling very stressed."), and the output is a generated text response (e.g., "If you are feeling stressed, take a deep breath and relax. Listening to your favorite music or taking a short walk can refresh your mood.").

[0404] Step 7:

[0405] The server sends the generated response to the terminal. The input is the generated text response, and the output is the data sent to the terminal.

[0406] Step 8:

[0407] The terminal displays or outputs the received response to the infotainment system. Input is a text response, and output is either displayed on the screen or output as audio from the speaker. The user confirms advice such as, "If you are feeling stressed, take a deep breath and relax."

[0408] Step 9:

[0409] The user attempts relaxation techniques based on the advice provided. The input is the received advice, and the output is the user's actions (e.g., deep breathing, listening to music). Through this process, the user's stress is reduced, and a comfortable in-car environment is provided.

[0410] 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.

[0411] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0412] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0413] [Second Embodiment]

[0414] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0415] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0416] 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).

[0417] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0418] 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.

[0419] 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).

[0420] 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.

[0421] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0422] 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.

[0423] 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.

[0424] 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.

[0425] 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".

[0426] This invention aims to maintain mental health and reduce the number of suicides by providing a system that allows users to anonymously consult about their worries and questions. Specific embodiments are described below.

[0427] 1. User-side processing

[0428] What the user does first

[0429] The user launches a social networking service application on their device and accesses the official account "Trouble Consultation Service".

[0430] Enter message

[0431] The user enters their worries or questions as a text message and presses the send button. For example, they might type, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?"

[0432] 2. Processing on the terminal side

[0433] Send message

[0434] After the user presses the send button, the device sends the message to the server via the LINE API or similar.

[0435] 3. Server-side processing

[0436] Message received

[0437] The server receives user messages sent from the terminal.

[0438] Message Controller

[0439] The server analyzes the received messages and extracts the user's concerns and questions. Using natural language processing techniques, it identifies information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[0440] Answer generation by generative AI

[0441] The server queries a generative AI (for example, a text generation model) based on the analyzed data. The AI ​​generates an appropriate answer to the user's question. For example, it might generate an answer such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0442] Submit your response

[0443] The generated response is sent from the server to the user's device via the LINE API.

[0444] 4. User-side processing

[0445] Display and view answers

[0446] The response message received on the user's device is displayed. The user reads something like, "Message from the counseling service: If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0447] Specific example

[0448] Flow from user problem input to resolution

[0449] 1. User-side processing

[0450] A user enters "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" into the "Advice Service" on the LINE app and presses the send button.

[0451] 2. Processing on the terminal side

[0452] The device sends the user's message to the server via the LINE API.

[0453] 3. Server-side processing

[0454] The server receives the message and analyzes its contents. For example, it might extract information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[0455] The server queries the generative AI based on the extracted results, and the AI ​​generates a response such as, "If you're feeling down, it's important to set aside time to relax. You can change your mood by getting some moderate exercise or enjoying a hobby."

[0456] The server sends the generated response to the device via the LINE API.

[0457] 4. User-side processing

[0458] The device receives the response message from the server and displays it in the LINE app.

[0459] Users read messages such as, "Message from the advice service: If you're feeling down, it's important to take time to relax. Moderate exercise or enjoying a hobby can help lift your spirits," and then take the advice to heart.

[0460] This format allows users to easily discuss problems and questions they can't talk about with others and receive appropriate advice. This is expected to promote mental health and reduce the number of suicides.

[0461] The following describes the processing flow.

[0462] Step 1:

[0463] The user launches the LINE app and starts a chat with the official account "Trouble Consultation Service." The user types, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" and presses the send button.

[0464] Step 2:

[0465] The device sends the user's input message to the server via the LINE API. The message is forwarded to the server in the appropriate format.

[0466] Step 3:

[0467] The server receives messages from users via the LINE API. The received messages are stored as they are.

[0468] Step 4:

[0469] The server analyzes the received messages. Using natural language processing techniques, it extracts specific details about the user's concerns and questions. The analysis results include information such as "I'm feeling down about work" and "I'd like to know how to cheer myself up."

[0470] Step 5:

[0471] The server queries the generative AI based on the analysis results. It sends a specific question to the generative AI (for example, a text generation model), such as "Please tell me how to deal with a user who is feeling down at work."

[0472] Step 6:

[0473] A generative AI receives inquiries from the server and generates appropriate answers to the user's concerns. For example, it might generate answers such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0474] Step 7:

[0475] The server receives the response from the generative AI and converts it into an appropriate message format. The converted message is then sent to the user's device via the LINE API.

[0476] Step 8:

[0477] The device receives a message from the server and displays it. The user's LINE app displays the following message: "Message from the advice service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0478] Step 9:

[0479] Users read messages displayed on their devices and consider actions based on the advice. For example, they might try to change their mood by taking time to relax or getting some moderate exercise.

[0480] (Example 1)

[0481] 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".

[0482] In modern society, many people suffer from mental health problems and have questions, but are often unable to confide in others or receive appropriate advice. As a result, their mental health suffers, and we face the serious problem of an increase in the number of suicides. Therefore, there is a need to provide a system in which users can anonymously and safely discuss their problems and questions and receive appropriate advice.

[0483] 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.

[0484] In this invention, the server includes means for receiving user input from a terminal, means for analyzing the received user input to extract the content of worries and questions, means for sending prompt sentences to a generative AI based on the extracted content of worries and questions to generate an appropriate answer, means for sending the generated answer to the terminal, and means for displaying the generated answer on the user's terminal. This makes it possible for users to anonymously consult about worries and questions they cannot talk about with others and to receive appropriate advice.

[0485] A "device" refers to a device that a user can directly operate, and this includes smartphones, tablets, and personal computers.

[0486] "User input" refers to information such as text messages and voice commands that users send to the system using their terminals.

[0487] A "server" refers to a central control unit that receives user input and performs processing and analysis; this includes databases and application servers.

[0488] "Means of receiving" refers to a function or module that allows the server to receive input sent from the user's terminal.

[0489] "Means of analysis" refers to a function or module that analyzes received user input, understands and interprets its content, and extracts concerns and questions.

[0490] "Means of extraction" refers to a function or module used to extract specific information from analyzed data.

[0491] "Generative AI" refers to artificial intelligence models that generate appropriate output (in this case, an answer) based on input data, and includes models specifically designed for text generation.

[0492] A "prompt sentence" refers to the input sentence presented to a generative AI, which serves as the basis for the AI ​​to generate an appropriate response.

[0493] "Means of transmission" refers to a function or module for sending the generated response from the server to the user's terminal.

[0494] "Means of display" refers to a function or module for visually displaying the response received on the user's device.

[0495] "Natural Language Processing (NLP)" refers to techniques for processing and understanding human language, and includes text analysis and sentiment analysis.

[0496] An "appropriate response" refers to a message that is useful to the user's concerns or questions and includes appropriate advice and information relevant to the situation.

[0497] "Social networking services" refer to platforms for people to interact online, and specific examples include SNS apps and messaging services.

[0498] This invention aims to maintain mental health and reduce the number of suicides by providing a system that allows users to anonymously consult about their worries and questions. Specific embodiments are described below.

[0499] Hardware and software to be used

[0500] This system uses the following hardware and software:

[0501] Devices: Smartphones, tablets, PCs, etc.

[0502] Servers: Database server, application server

[0503] Software: Social networking service (SNS) applications (e.g., LINE), natural language processing libraries (e.g., spacy, NLTK), generative AI models (e.g., GPT-3)

[0504] System operation

[0505] The operation of this system is as follows:

[0506] User-side processing

[0507] First, the user launches a social networking application on their device and accesses the official account "Trouble Consultation Service." The user then enters their problem or question as a text message and presses the send button. For example, they might type, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?"

[0508] Terminal-side processing

[0509] After the user presses the send button, the device sends the message to the server via the LINE API. At this time, the user's input is encoded, and an HTTP request is generated to send it to the LINE API.

[0510] Server-side processing

[0511] The server analyzes HTTP requests received through the LINE API and extracts the user's text message. Then, it uses natural language processing (NLP) to analyze the message content and extract the user's concerns and questions. For example, it identifies information such as "I'm feeling down at work" or "I want to know how to cheer myself up."

[0512] The server sends a prompt to the generative AI model based on the analysis results. For example, it might send a prompt like, "The user is asking, 'I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?' Please generate an appropriate answer for this situation." The generative AI model then generates an appropriate answer. For example, it might generate an answer like, "When you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0513] The generated response is sent from the server to the user's device via the LINE API.

[0514] User-side processing again

[0515] The response message received on the user's device is displayed. The user can gain specific advice by reading the message, such as "Message from the counseling service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0516] Specific example

[0517] Let's say a user enters the message "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" into the "Advice Service" on the LINE app and presses the send button. The device sends this message to the server via the LINE API. The server receives the message and extracts the information that the user is "feeling down because of work" and "wants to know how to cheer themselves up." Based on the extracted information, the server sends a prompt to the generative AI: "The user is asking 'I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?' Please generate an appropriate answer for this situation." The generative AI generates an answer such as "When you're feeling down, it's important to set aside time to relax. You can cheer yourself up by getting some moderate exercise or enjoying a hobby," and the server sends this to the user via the LINE API. The user can then check this content on their device and use it as a reference.

[0518] Thus, the system of the present invention provides users with quick and appropriate answers to their worries and questions, contributing to the maintenance of their mental health.

[0519] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0520] Step 1:

[0521] The user launches the SNS application and enters the details of their inquiry.

[0522] The user launches the SNS application on their device and accesses the official account "Trouble Consultation Service." Next, they enter their problem or question into the text box and press the send button.

[0523] Input: User's text message (e.g., "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?")

[0524] Output: The text message is passed to the sender on the terminal side.

[0525] Step 2:

[0526] The device sends a message to the server.

[0527] The device sends the user's input to the server via the LINE API. During this process, the input data is converted to JSON format and sent as an HTTP request.

[0528] Input: User's text message

[0529] Output: HTTP request sent to the server via the LINE API

[0530] Step 3:

[0531] The server receives the message

[0532] The server interprets HTTP requests received through the LINE API and extracts the user's text message. The message data is processed at a specific endpoint within the server.

[0533] Input: HTTP request received from LINE API

[0534] Output: User's text messages are stored on the server.

[0535] Step 4:

[0536] The server analyzes the message content.

[0537] The server uses natural language processing (NLP) techniques to analyze incoming messages. This process involves tokenizing the text and performing sentiment analysis and keyword extraction.

[0538] Input: User's text message

[0539] Output: Analyzed data (e.g., "I'm feeling down at work," "I want to know how to cheer myself up," etc.)

[0540] Step 5:

[0541] The server sends a prompt message to the generative AI model.

[0542] Based on the analysis results, the server generates and sends appropriate prompts to the generative AI model. For example, a prompt might read, "The user is asking, 'I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?' Please generate an appropriate answer to this situation."

[0543] Input: Analyzed data

[0544] Output: Prompt message sent to the generative AI model

[0545] Step 6:

[0546] Generative AI models generate answers.

[0547] The generative AI model receives a prompt and generates an appropriate response. The AI ​​provides the response in natural language and returns it to the server.

[0548] Input: Prompt message

[0549] Output: Response text such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0550] Step 7:

[0551] The server sends the generated response to the user's device via the LINE API.

[0552] The server uses the LINE API to send the retrieved responses to the user's device. The response data is encoded in JSON format and sent to the device as an HTTP request.

[0553] Input: Generated response text

[0554] Output: HTTP request sent via LINE API

[0555] Step 8:

[0556] The device displays the response message.

[0557] The user's device decodes messages received from the LINE API and displays them on the application's interface. The user can then read and refer to these messages.

[0558] Input: HTTP request received from LINE API

[0559] Output: A message from the counseling service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits.

[0560] (Application Example 1)

[0561] 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."

[0562] In modern society, systems that allow people to anonymously seek advice are important for maintaining mental health, but systems that provide health consultations related to meal delivery are still lacking. Therefore, there is a need for an effective system that provides health-conscious meal options in delivery services. To solve this problem, a new system is needed that can suggest appropriate meals according to the user's health condition and concerns.

[0563] 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.

[0564] In this invention, the server includes means for receiving user input from a terminal, means for analyzing the received user input to extract concerns and questions, means for querying a generative AI based on the extracted concerns and questions to generate an appropriate answer, means for transmitting the generated answer to the terminal, and, if the user input relates to meal delivery, means for generating dietary advice tailored to the user's health condition. This allows users to anonymously consult about meal options tailored to their health condition and individual concerns.

[0565] A "terminal" is an electronic device used to send and receive data via a communication network.

[0566] "User input" refers to information or messages entered by a user using a terminal.

[0567] "Analysis" refers to the process of analyzing received user input and extracting its contents.

[0568] "Concerns and questions" refer to information about problems or matters that users want to discuss or ask about.

[0569] "Generative AI" is an artificial intelligence technology that generates appropriate responses based on input information.

[0570] An "inquiry" is a process that requests a generative AI to generate an answer based on the analysis results.

[0571] "Answer" refers to a response generated by a generative AI to a user's question or concern.

[0572] "Sending" refers to the process of sending the generated response back to the user's device.

[0573] "Meal delivery" is a service that delivers meals ordered from a specific location to a location specified by the user.

[0574] "Health status" refers to the user's physical and mental health status.

[0575] "Dietary advice" refers to suggestions regarding meal menus and eating methods based on the user's health condition.

[0576] A "system" is a configuration in which multiple elements, such as terminals, servers, and generative AI, work together to achieve a function.

[0577] This invention provides a system that allows users to anonymously seek health advice regarding meal delivery. Specific embodiments are described below.

[0578] System program

[0579] The system uses the following hardware and software:

[0580] Hardware: Terminals that receive user input, servers that process data.

[0581] Software: Web framework "Flask", natural language processing library "transformers"

[0582] Program processing

[0583] 1. Receiving user input

[0584] The server receives health consultation messages related to meal delivery entered by the user via their terminal. User input is in text format and may include things like, "I've gained weight recently, do you have any recommendations for healthy menu items?"

[0585] 2. Message Analysis

[0586] The server analyzes received messages using natural language processing. Specifically, it extracts concerns and questions from the message content.

[0587] 3. Generating answers using generative AI

[0588] The server queries a generative AI, such as "GPT-3.5-turbo," based on the extracted concerns and questions, and generates appropriate answers. This includes dietary advice that takes health conditions into consideration.

[0589] 4. Submit your response

[0590] The generated response is sent from the server to the user's device. The user views this response on their device and uses the advice as a reference.

[0591] Specific example

[0592] The following is a concrete example of how this system works.

[0593] User input example

[0594] "I've gained weight recently. Do you have any recommendations for healthy meals?"

[0595] Example answers from a generative AI model

[0596] "If you've recently gained weight, healthy options like salads, grilled chicken, and smoothies are recommended. It's especially important to keep your calorie intake down at night."

[0597] In this way, users can easily receive anonymous dietary advice related to health. This system helps users make healthy choices within meal delivery services and can provide meal options tailored to their individual health conditions and concerns.

[0598] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0599] Step 1:

[0600] Receiving user input

[0601] Users enter and send health consultation messages related to meal delivery through their terminals. This input is in text format and may include phrases such as, "I've gained weight recently, do you have any recommendations for healthy menu options?"

[0602] Input: User consultation message sent from the device.

[0603] Output: User input data sent to the server.

[0604] Step 2:

[0605] Message received

[0606] The server receives user input messages sent from the terminal.

[0607] Input: A text message sent by the user from their device.

[0608] Output: Data of the received user message.

[0609] Step 3:

[0610] Message parsing

[0611] The server analyzes received messages using natural language processing (NLP) techniques to extract user concerns and questions. For example, it might extract information such as "I'm gaining weight" or "I'd like recommendations for healthy meals."

[0612] Input: Received user message data.

[0613] Output: Data on concerns and questions extracted through analysis.

[0614] Step 4:

[0615] Generating answers using generative AI

[0616] Based on the analyzed concerns and questions, the server queries a generative AI (e.g., GPT-3.5-turbo) to generate appropriate answers. The generated answers include dietary advice that takes the user's health condition into consideration. For example, "Healthy menu options such as salads, grilled chicken, and smoothies are recommended."

[0617] Input: Data on concerns and questions extracted through analysis.

[0618] Output: Response data generated by a generative AI.

[0619] Step 5:

[0620] Submit your response

[0621] The server sends the generated responses to the user's device, allowing the user to view them on their device. These responses may include advice and recommended meal options.

[0622] Input: Generated response data.

[0623] Output: The response message sent to the user's device.

[0624] Step 6:

[0625] Display and view answers

[0626] The user's device displays the response message received from the server, and the user reads it. For example, they might see a message like, "Healthy menu items such as salads, grilled chicken, and smoothies are recommended."

[0627] Input: The response message sent from the server.

[0628] Output: The advice displayed on the user's terminal.

[0629] 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.

[0630] The present invention aims to provide more accurate advice and support by combining a system for anonymously consulting about users' worries and questions with an emotion engine that recognizes emotions from the user's input messages. Specific embodiments are described below.

[0631] 1. User-side processing

[0632] What the user does first

[0633] The user launches a social networking service application on their device and accesses the official account "Trouble Consultation Service".

[0634] Enter message

[0635] The user enters their worries or questions as a text message and presses the send button. For example, they might type, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?"

[0636] 2. Processing on the terminal side

[0637] Send message

[0638] After the user presses the send button, the device sends the message to the server via the LINE API or similar.

[0639] 3. Server-side processing

[0640] Message received

[0641] The server receives user messages sent from the terminal.

[0642] Message Controller

[0643] The server analyzes the received messages and extracts the user's concerns and questions. Using natural language processing techniques, it identifies information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[0644] Emotion recognition by an emotion engine

[0645] The server uses an emotion engine to recognize the user's emotions based on the extracted information. For example, it identifies emotional states such as "depressed" or "anxious."

[0646] Answer generation by generative AI

[0647] The server queries a generative AI (for example, a text generation model) based on the analysis results and emotion recognition results. The AI ​​generates an appropriate response according to the user's emotional state. For example, it might generate a response such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0648] Submit your response

[0649] The generated response is sent from the server to the user's device via the LINE API.

[0650] 4. User-side processing

[0651] Display and view answers

[0652] The response message received on the user's device is displayed. The user reads something like, "Message from the counseling service: If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0653] Specific example

[0654] Flow from user problem input to resolution

[0655] 1. User-side processing

[0656] A user enters "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" into the "Advice Service" on the LINE app and presses the send button.

[0657] 2. Processing on the terminal side

[0658] The device sends the user's message to the server via the LINE API.

[0659] 3. Server-side processing

[0660] The server receives the message and analyzes its contents. For example, it might extract information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[0661] The server uses an emotion engine to recognize emotions based on the extracted results. For example, it identifies emotional states such as "depressed" or "anxious."

[0662] The server queries the generative AI based on the analysis results and emotion recognition results, and the AI ​​generates a response such as, "If you're feeling down, it's important to set aside time to relax. You can change your mood by getting some moderate exercise or enjoying a hobby."

[0663] The server sends the generated response to the device via the LINE API.

[0664] 4. User-side processing

[0665] The device receives the response message from the server and displays it in the LINE app.

[0666] Users read messages such as, "Message from the advice service: If you're feeling down, it's important to take time to relax. Moderate exercise or enjoying a hobby can help lift your spirits," and then take the advice to heart.

[0667] This format allows users to easily discuss worries and questions they can't talk about with others and receive appropriate advice. Furthermore, by using an emotion engine, more accurate responses tailored to the user's emotional state are provided, which is expected to further promote mental health and reduce the number of suicides.

[0668] The following describes the processing flow.

[0669] Step 1:

[0670] The user launches the LINE app and starts a chat with the official account "Trouble Consultation Service." The user types, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" and presses the send button.

[0671] Step 2:

[0672] The device sends the user's input message to the server via the LINE API. The message is forwarded to the server in the appropriate format.

[0673] Step 3:

[0674] The server receives messages from users via the LINE API. The received messages are stored as they are.

[0675] Step 4:

[0676] The server analyzes the received messages. Using natural language processing techniques, it extracts specific details about the user's concerns and questions. The analysis results include information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[0677] Step 5:

[0678] The server uses an emotion engine based on the information it extracts to recognize the user's emotions. For example, it might identify emotional states such as "depressed" or "anxious."

[0679] Step 6:

[0680] The server queries the generative AI based on the analysis results and emotion recognition results. It sends a specific question to the generative AI, such as, "Please tell me how to deal with a user who is feeling down at work."

[0681] Step 7:

[0682] A generative AI receives inquiries from the server and generates appropriate responses based on the user's emotional state. For example, it might generate a response such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0683] Step 8:

[0684] The server receives the response from the generative AI and converts it into an appropriate message format. The converted message is then sent to the user's device via the LINE API.

[0685] Step 9:

[0686] The device receives a message from the server and displays it. The user's LINE app displays the following message: "Message from the advice service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0687] Step 10:

[0688] Users read messages displayed on their devices and consider actions based on the advice. For example, they might try to change their mood by taking time to relax or getting some moderate exercise.

[0689] (Example 2)

[0690] 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".

[0691] Conventional counseling systems have had difficulty accurately recognizing users' emotions, resulting in answers that do not adequately address the user's psychological state. This has led to decreased user satisfaction and a lack of essential support. This invention aims to solve these problems and provide more accurate answers that are tailored to the user's emotional state.

[0692] 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 user input from a terminal, means for analyzing the received user input to extract the content of worries and questions, means for recognizing the user's emotions using an emotion engine based on the extracted content of worries and questions, means for generating an answer corresponding to the recognized user's emotions, means for querying a generative AI to generate an appropriate answer, and means for transmitting the generated answer to the terminal. This makes it possible to provide more accurate answers that correspond to the user's emotional state.

[0693] "Terminal" refers to a device that a user uses to send input messages.

[0694] "User input" refers to text messages containing concerns or questions sent by users using their devices.

[0695] "Means of receiving" refers to the functions and methods by which a server receives user input transmitted from a terminal.

[0696] "Means of analysis" refers to functions and methods for analyzing the content of received user input and extracting specific information about their concerns and questions.

[0697] An "emotion engine" refers to software or an algorithm that recognizes a user's emotions based on extracted concerns and questions.

[0698] "Means of recognition" refers to functions and methods for identifying a user's emotional state using an emotion engine.

[0699] "Generative AI" refers to artificial intelligence technology that generates appropriate responses in natural language based on input information.

[0700] "Generative means" refers to functions and methods that use generative AI to create answers tailored to the user's concerns and emotional state.

[0701] "Means of transmission" refers to the functions or methods for sending the generated response back to the device.

[0702] This invention aims to provide more accurate advice and support by combining an emotion engine with a system for anonymously consulting users about their worries and questions. The configuration and operation of this system are described in detail below.

[0703] The user launches a social networking service application on their device and accesses the official account "Trouble Consultation Service." They enter their troubles or questions as a text message, for example, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" and then press the send button to send it.

[0704] The terminal sends messages sent by the user to the server via the communication network. The communication protocol used for this is the common HTTP request. Messages are sent using services such as the LINE API.

[0705] The server receives user messages sent from terminals. The received messages are first analyzed using natural language processing (NLP) techniques. For example, natural language processing libraries such as spaCy or BERT are used for this analysis. Through this analysis, information about specific problems or questions is extracted from the message content. For example, information such as "I'm feeling down at work" or "I want to know how to cheer myself up" may be identified.

[0706] Next, the server uses an emotion engine to recognize the user's emotions based on the extracted information. This emotion recognition can utilize emotion recognition software such as IBM Watson, Aylien, or Microsoft Text Analytics API. This allows the server to identify emotional states such as "depressed" or "anxious."

[0707] Based on the emotion recognition results and analysis results, the server queries a generative AI model. For example, OpenAI's GPT-3 is used as the generative AI model. Specific prompt statements are input to this AI model, and an appropriate response is generated. Examples of prompt statements include the following:

[0708] "The user is feeling depressed. The following input is advice regarding the user's depression at work: Moderate exercise and enjoying hobbies can help lift their spirits."

[0709] The generated responses might include something like, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0710] Finally, the server sends the generated response to the device via the LINE API. The device displays the received response message in the LINE app, allowing the user to view it and receive advice.

[0711] As described above, the present invention makes it possible to automatically generate and provide appropriate advice according to the user's emotional state. This allows users to receive more specific and appropriate support for their worries and questions.

[0712] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0713] Step 1:

[0714] The user launches the LINE app and accesses the official account "Trouble Consultation Service".

[0715] Specifically, the user searches for the official account within the LINE app and opens the chat window.

[0716] Step 2:

[0717] The user enters their concerns or questions as a text message and presses the send button.

[0718] Input: A text message entered by the user (e.g., "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?")

[0719] Output: Message sent via LINE API

[0720] At this point, the user presses the "Send" button, sending a text message to the server.

[0721] Step 3:

[0722] The device sends the entered message to the server via the LINE API.

[0723] Input: Text message sent by the user

[0724] Output: Messages are received on the server via the LINE API.

[0725] Specifically, the device sends the message to the server in the format of an HTTP request using the LINE API.

[0726] Step 4:

[0727] The server analyzes the received messages and extracts the content of the concerns and questions.

[0728] Input: Text message received from LINE API

[0729] Output: Specific information about the analyzed worries and questions (e.g., "I'm feeling down about work," "I want to know how to change my mood")

[0730] The server analyzes the received message using a natural language processing library (e.g., spaCy or BERT) and extracts specific information.

[0731] Step 5:

[0732] The server uses an emotion engine to recognize the user's emotions based on the extracted information.

[0733] Input: Information on extracted problems and questions

[0734] Output: Recognized user emotional state (e.g., "depressed," "anxious")

[0735] This involves performing specific actions to identify the user's emotional state using an emotion engine (e.g., IBM Watson, Aylien, Microsoft Text Analytics API).

[0736] Step 6:

[0737] The server inputs prompts into a generative AI model based on the analysis results and emotion recognition results, generating an appropriate response.

[0738] Input: Analysis results and emotion recognition results

[0739] Output: Generated response text (Example: "When you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits.")

[0740] The server generates prompts for a generative AI model (e.g., OpenAI's GPT-3), and an example of a prompt is as follows: "The user is feeling depressed. The following input is advice regarding the user's depression at work. Moderate exercise and enjoying hobbies can help lift your spirits."

[0741] Step 7:

[0742] The server sends the generated response to the device via the LINE API.

[0743] Input: Generated response text

[0744] Output: Message sent to the device via LINE API

[0745] Specifically, the generated response is sent in the format of an HTTP request using the LINE API.

[0746] Step 8:

[0747] The device displays the response message from the server in the LINE app.

[0748] Input: Response message received from the server

[0749] Output: The response text displayed within the LINE app.

[0750] Specifically, the device displays the message received from the LINE API.

[0751] Step 9:

[0752] View the response messages the user has received and get advice.

[0753] Input: Answer text displayed within the LINE app

[0754] Output: Advice the user receives (Example: "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits.")

[0755] Specifically, the user reads messages within the LINE app and uses the appropriate advice as a reference.

[0756] (Application Example 2)

[0757] 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."

[0758] In recent years, with the increasing prevalence of autonomous vehicles and the resulting increase in the amount of time spent inside vehicles, there is a growing need to reduce the stress and anxiety users experience and provide a comfortable in-car environment. However, conventional systems lack the mechanisms to provide appropriate support in real time in response to users' emotional states. In particular, it is crucial to maintain users' mental well-being by appropriately recognizing negative emotions they experience and providing appropriate advice.

[0759] 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.

[0760] In this invention, the server includes means for receiving user input from a terminal, means for analyzing the received user input to extract concerns and questions, means for using an emotion engine to recognize the user's emotions based on the extracted concerns and questions, means for querying a generative AI based on the extracted concerns and questions and the recognized user emotions to generate an appropriate response, and means for transmitting the generated response to the terminal. This makes it possible to sense the stress and anxiety felt by the user in real time within an autonomous vehicle and provide appropriate advice according to their emotions.

[0761] A "terminal" is an electronic device used by a user for input, and includes smartphones, tablets, computers, and other similar devices.

[0762] A "server" is a computer system that receives requests from multiple terminals via a network and provides services or information in response to those requests.

[0763] "User input" refers to text and audio data of concerns and questions provided by users through their devices.

[0764] An "emotion engine" is software or an algorithm that recognizes and analyzes emotions from user input data.

[0765] "Generative AI" refers to artificial intelligence that generates natural language text based on a given prompt, and generally uses natural language processing techniques.

[0766] "Answer" refers to advice and information provided in response to a user's concerns or questions, and is text data generated by a generative AI.

[0767] An "autonomous vehicle" is a vehicle designed to drive automatically without human intervention.

[0768] An "infotainment system" is an in-vehicle system that provides information and entertainment to the vehicle's user, and includes displays, sound systems, and other components.

[0769] A "social networking service" is a platform on the internet that allows users to share information and communicate with each other.

[0770] A "text generation model" is a machine learning model used to generate natural language text, and is based on deep learning technology.

[0771] "Voice input" refers to input data provided by the user via voice, which is converted into text using speech recognition technology.

[0772] This invention is a system that senses the stress and anxiety experienced by users in autonomous vehicles in real time and provides appropriate advice. Specifically, it receives voice or text messages entered by the user in the vehicle, analyzes the user's concerns and questions based on their content, and recognizes their emotions using an emotion engine. Based on this, it generates an appropriate response using a generative AI and outputs it to the infotainment system either by displaying it or by voice.

[0773] The server receives messages entered from the user's terminal. This includes a step where, in the case of voice input, speech recognition technology is used to convert it into text. The server then analyzes the received text message to extract the user's concerns and questions. This analysis uses natural language processing (NLP) techniques to identify the user's intent and requests.

[0774] Next, the server uses an emotion engine to recognize the user's emotions from the analyzed worries and questions. The emotion engine identifies emotional states such as "feeling stressed" or "feeling anxious." This can be done using emotion recognition APIs such as IBM Watson or Microsoft Azure.

[0775] Based on the recognized emotional state and the nature of the problem, the server sends a prompt to a generative AI to generate an appropriate response. For example, a text generation model such as OpenAI's GPT-3 is used as the generative AI. This prompt might take the following format:

[0776] "Please write advice on what to do if a user is experiencing significant stress."

[0777] The responses from the generative AI will be tailored to the user's emotional state. For example, "If you're feeling stressed, take a deep breath and relax. Listening to your favorite music or taking a short walk will refresh your mood."

[0778] The generated responses are sent from the server to the vehicle's infotainment system. The infotainment system presents the responses to the user through a display or audio output device. This reduces stress and anxiety the user feels in the car, resulting in a more comfortable driving experience.

[0779] For example, if a user in a car types "I've been feeling very stressed at work lately," the emotion engine will recognize the user's emotional state as "feeling very stressed" from this text. Then, the generative AI will generate advice such as, "If you're feeling stressed, take a deep breath and relax. Listening to your favorite music or taking a short walk can refresh your mood," and display it on the infotainment system.

[0780] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0781] Step 1:

[0782] The user enters a voice or text message using an input device (e.g., microphone, touchscreen) inside the autonomous vehicle. The user says, "I've been feeling very stressed at work lately." This voice is captured by the microphone or the text is entered.

[0783] Step 2:

[0784] The device converts the input audio data into text using speech recognition software (e.g., Google Speech-to-Text API). The input is audio data, and the output is the converted text data. The text would be "I've been feeling very stressed at work lately."

[0785] Step 3:

[0786] The terminal sends the received text message to the server. The input is text data, and the output is the data sent to the server. The server receives the user's message.

[0787] Step 4:

[0788] The server analyzes received text messages using natural language processing (NLP) techniques (e.g., spaCy) to extract concerns and questions. The input is text data, and the output is the analyzed elements of concerns and questions (e.g., "work," "stress").

[0789] Step 5:

[0790] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) based on the analyzed elements to recognize the user's emotional state. The input is the analyzed elements, and the output is the recognized emotional state (e.g., "stressed").

[0791] Step 6:

[0792] Based on the recognized emotional state and the nature of the problem, the server sends a prompt to a generative AI (e.g., OpenAI GPT-3) to generate an appropriate response. The input is a prompt sentence (e.g., "Please write advice on what to do if the user is feeling very stressed."), and the output is a generated text response (e.g., "If you are feeling stressed, take a deep breath and relax. Listening to your favorite music or taking a short walk can refresh your mood.").

[0793] Step 7:

[0794] The server sends the generated response to the terminal. The input is the generated text response, and the output is the data sent to the terminal.

[0795] Step 8:

[0796] The terminal displays or outputs the received response to the infotainment system. Input is a text response, and output is either displayed on the screen or output as audio from the speaker. The user confirms advice such as, "If you are feeling stressed, take a deep breath and relax."

[0797] Step 9:

[0798] The user attempts relaxation techniques based on the advice provided. The input is the received advice, and the output is the user's actions (e.g., deep breathing, listening to music). Through this process, the user's stress is reduced, and a comfortable in-car environment is provided.

[0799] 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.

[0800] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0801] In the above embodiment, an example was given in which 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.

[0802] [Third Embodiment]

[0803] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0804] 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.

[0805] 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).

[0806] 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.

[0807] 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.

[0808] 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).

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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".

[0815] This invention aims to maintain mental health and reduce the number of suicides by providing a system that allows users to anonymously consult about their worries and questions. Specific embodiments are described below.

[0816] 1. User-side processing

[0817] What the user does first

[0818] The user launches a social networking service application on their device and accesses the official account "Trouble Consultation Service".

[0819] Enter message

[0820] The user enters their worries or questions as a text message and presses the send button. For example, they might type, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?"

[0821] 2. Processing on the terminal side

[0822] Send message

[0823] After the user presses the send button, the device sends the message to the server via the LINE API or similar.

[0824] 3. Server-side processing

[0825] Message received

[0826] The server receives user messages sent from the terminal.

[0827] Message Controller

[0828] The server analyzes the received messages and extracts the user's concerns and questions. Using natural language processing techniques, it identifies information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[0829] Answer generation by generative AI

[0830] The server queries a generative AI (for example, a text generation model) based on the analyzed data. The AI ​​generates an appropriate answer to the user's question. For example, it might generate an answer such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0831] Submit your response

[0832] The generated response is sent from the server to the user's device via the LINE API.

[0833] 4. User-side processing

[0834] Display and view answers

[0835] The response message received on the user's device is displayed. The user reads something like, "Message from the counseling service: If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0836] Specific example

[0837] Flow from user problem input to resolution

[0838] 1. User-side processing

[0839] A user enters "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" into the "Advice Service" on the LINE app and presses the send button.

[0840] 2. Processing on the terminal side

[0841] The device sends the user's message to the server via the LINE API.

[0842] 3. Server-side processing

[0843] The server receives the message and analyzes its contents. For example, it might extract information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[0844] The server queries the generative AI based on the extracted results, and the AI ​​generates a response such as, "If you're feeling down, it's important to set aside time to relax. You can change your mood by getting some moderate exercise or enjoying a hobby."

[0845] The server sends the generated response to the device via the LINE API.

[0846] 4. User-side processing

[0847] The device receives the response message from the server and displays it in the LINE app.

[0848] Users read messages such as, "Message from the advice service: If you're feeling down, it's important to take time to relax. Moderate exercise or enjoying a hobby can help lift your spirits," and then take the advice to heart.

[0849] This format allows users to easily discuss problems and questions they can't talk about with others and receive appropriate advice. This is expected to promote mental health and reduce the number of suicides.

[0850] The following describes the processing flow.

[0851] Step 1:

[0852] The user launches the LINE app and starts a chat with the official account "Trouble Consultation Service." The user types, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" and presses the send button.

[0853] Step 2:

[0854] The device sends the user's input message to the server via the LINE API. The message is forwarded to the server in the appropriate format.

[0855] Step 3:

[0856] The server receives messages from users via the LINE API. The received messages are stored as they are.

[0857] Step 4:

[0858] The server analyzes the received messages. Using natural language processing techniques, it extracts specific details about the user's concerns and questions. The analysis results include information such as "I'm feeling down about work" and "I'd like to know how to cheer myself up."

[0859] Step 5:

[0860] The server queries the generative AI based on the analysis results. It sends a specific question to the generative AI (for example, a text generation model), such as "Please tell me how to deal with a user who is feeling down at work."

[0861] Step 6:

[0862] A generative AI receives inquiries from the server and generates appropriate answers to the user's concerns. For example, it might generate answers such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0863] Step 7:

[0864] The server receives the response from the generative AI and converts it into an appropriate message format. The converted message is then sent to the user's device via the LINE API.

[0865] Step 8:

[0866] The device receives a message from the server and displays it. The user's LINE app displays the following message: "Message from the advice service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0867] Step 9:

[0868] Users read messages displayed on their devices and consider actions based on the advice. For example, they might try to change their mood by taking time to relax or getting some moderate exercise.

[0869] (Example 1)

[0870] 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."

[0871] In modern society, many people suffer from mental health problems and have questions, but are often unable to confide in others or receive appropriate advice. As a result, their mental health suffers, and we face the serious problem of an increase in the number of suicides. Therefore, there is a need to provide a system in which users can anonymously and safely discuss their problems and questions and receive appropriate advice.

[0872] 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.

[0873] In this invention, the server includes means for receiving user input from a terminal, means for analyzing the received user input to extract the content of worries and questions, means for sending prompt sentences to a generative AI based on the extracted content of worries and questions to generate an appropriate answer, means for sending the generated answer to the terminal, and means for displaying the generated answer on the user's terminal. This makes it possible for users to anonymously consult about worries and questions they cannot talk about with others and to receive appropriate advice.

[0874] A "device" refers to a device that a user can directly operate, and this includes smartphones, tablets, and personal computers.

[0875] "User input" refers to information such as text messages and voice commands that users send to the system using their terminals.

[0876] A "server" refers to a central control unit that receives user input and performs processing and analysis; this includes databases and application servers.

[0877] "Means of receiving" refers to a function or module that allows the server to receive input sent from the user's terminal.

[0878] "Means of analysis" refers to a function or module that analyzes received user input, understands and interprets its content, and extracts concerns and questions.

[0879] "Means of extraction" refers to a function or module used to extract specific information from analyzed data.

[0880] "Generative AI" refers to artificial intelligence models that generate appropriate output (in this case, an answer) based on input data, and includes models specifically designed for text generation.

[0881] A "prompt sentence" refers to the input sentence presented to a generative AI, which serves as the basis for the AI ​​to generate an appropriate response.

[0882] "Means of transmission" refers to a function or module for sending the generated response from the server to the user's terminal.

[0883] "Means of display" refers to a function or module for visually displaying the response received on the user's device.

[0884] "Natural Language Processing (NLP)" refers to techniques for processing and understanding human language, and includes text analysis and sentiment analysis.

[0885] An "appropriate response" refers to a message that is useful to the user's concerns or questions and includes appropriate advice and information relevant to the situation.

[0886] "Social networking services" refer to platforms for people to interact online, and specific examples include SNS apps and messaging services.

[0887] This invention aims to maintain mental health and reduce the number of suicides by providing a system that allows users to anonymously consult about their worries and questions. Specific embodiments are described below.

[0888] Hardware and software to be used

[0889] This system uses the following hardware and software:

[0890] Devices: Smartphones, tablets, PCs, etc.

[0891] Servers: Database server, application server

[0892] Software: Social networking service (SNS) applications (e.g., LINE), natural language processing libraries (e.g., spacy, NLTK), generative AI models (e.g., GPT-3)

[0893] System operation

[0894] The operation of this system is as follows:

[0895] User-side processing

[0896] First, the user launches a social networking application on their device and accesses the official account "Trouble Consultation Service." The user then enters their problem or question as a text message and presses the send button. For example, they might type, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?"

[0897] Terminal-side processing

[0898] After the user presses the send button, the device sends the message to the server via the LINE API. At this time, the user's input is encoded, and an HTTP request is generated to send it to the LINE API.

[0899] Server-side processing

[0900] The server analyzes HTTP requests received through the LINE API and extracts the user's text message. Then, it uses natural language processing (NLP) to analyze the message content and extract the user's concerns and questions. For example, it identifies information such as "I'm feeling down at work" or "I want to know how to cheer myself up."

[0901] The server sends a prompt to the generative AI model based on the analysis results. For example, it might send a prompt like, "The user is asking, 'I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?' Please generate an appropriate answer for this situation." The generative AI model then generates an appropriate answer. For example, it might generate an answer like, "When you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0902] The generated response is sent from the server to the user's device via the LINE API.

[0903] User-side processing again

[0904] The response message received on the user's device is displayed. The user can gain specific advice by reading the message, such as "Message from the counseling service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0905] Specific example

[0906] Let's say a user enters the message "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" into the "Advice Service" on the LINE app and presses the send button. The device sends this message to the server via the LINE API. The server receives the message and extracts the information that the user is "feeling down because of work" and "wants to know how to cheer themselves up." Based on the extracted information, the server sends a prompt to the generative AI: "The user is asking 'I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?' Please generate an appropriate answer for this situation." The generative AI generates an answer such as "When you're feeling down, it's important to set aside time to relax. You can cheer yourself up by getting some moderate exercise or enjoying a hobby," and the server sends this to the user via the LINE API. The user can then check this content on their device and use it as a reference.

[0907] Thus, the system of the present invention provides users with quick and appropriate answers to their worries and questions, contributing to the maintenance of their mental health.

[0908] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0909] Step 1:

[0910] The user launches the SNS application and enters the details of their inquiry.

[0911] The user launches the SNS application on their device and accesses the official account "Trouble Consultation Service." Next, they enter their problem or question into the text box and press the send button.

[0912] Input: User's text message (e.g., "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?")

[0913] Output: The text message is passed to the sender on the terminal side.

[0914] Step 2:

[0915] The device sends a message to the server.

[0916] The device sends the user's input to the server via the LINE API. During this process, the input data is converted to JSON format and sent as an HTTP request.

[0917] Input: User's text message

[0918] Output: HTTP request sent to the server via the LINE API

[0919] Step 3:

[0920] The server receives the message

[0921] The server interprets HTTP requests received through the LINE API and extracts the user's text message. The message data is processed at a specific endpoint within the server.

[0922] Input: HTTP request received from LINE API

[0923] Output: User's text messages are stored on the server.

[0924] Step 4:

[0925] The server analyzes the message content.

[0926] The server uses natural language processing (NLP) techniques to analyze incoming messages. This process involves tokenizing the text and performing sentiment analysis and keyword extraction.

[0927] Input: User's text message

[0928] Output: Analyzed data (e.g., "I'm feeling down at work," "I want to know how to cheer myself up," etc.)

[0929] Step 5:

[0930] The server sends a prompt message to the generative AI model.

[0931] Based on the analysis results, the server generates and sends appropriate prompts to the generative AI model. For example, a prompt might read, "The user is asking, 'I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?' Please generate an appropriate answer to this situation."

[0932] Input: Analyzed data

[0933] Output: Prompt message sent to the generative AI model

[0934] Step 6:

[0935] Generative AI models generate answers.

[0936] The generative AI model receives a prompt and generates an appropriate response. The AI ​​provides the response in natural language and returns it to the server.

[0937] Input: Prompt message

[0938] Output: Response text such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[0939] Step 7:

[0940] The server sends the generated response to the user's device via the LINE API.

[0941] The server uses the LINE API to send the retrieved responses to the user's device. The response data is encoded in JSON format and sent to the device as an HTTP request.

[0942] Input: Generated response text

[0943] Output: HTTP request sent via LINE API

[0944] Step 8:

[0945] The device displays the response message.

[0946] The user's device decodes messages received from the LINE API and displays them on the application's interface. The user can then read and refer to these messages.

[0947] Input: HTTP request received from LINE API

[0948] Output: A message from the counseling service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits.

[0949] (Application Example 1)

[0950] 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."

[0951] In modern society, systems that allow people to anonymously seek advice are important for maintaining mental health, but systems that provide health consultations related to meal delivery are still lacking. Therefore, there is a need for an effective system that provides health-conscious meal options in delivery services. To solve this problem, a new system is needed that can suggest appropriate meals according to the user's health condition and concerns.

[0952] 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.

[0953] In this invention, the server includes means for receiving user input from a terminal, means for analyzing the received user input to extract concerns and questions, means for querying a generative AI based on the extracted concerns and questions to generate an appropriate answer, means for transmitting the generated answer to the terminal, and, if the user input relates to meal delivery, means for generating dietary advice tailored to the user's health condition. This allows users to anonymously consult about meal options tailored to their health condition and individual concerns.

[0954] A "terminal" is an electronic device used to send and receive data via a communication network.

[0955] "User input" refers to information or messages entered by a user using a terminal.

[0956] "Analysis" refers to the process of analyzing received user input and extracting its contents.

[0957] "Concerns and questions" refer to information about problems or matters that users want to discuss or ask about.

[0958] "Generative AI" is an artificial intelligence technology that generates appropriate responses based on input information.

[0959] An "inquiry" is a process that requests a generative AI to generate an answer based on the analysis results.

[0960] "Answer" refers to a response generated by a generative AI to a user's question or concern.

[0961] "Sending" refers to the process of sending the generated response back to the user's device.

[0962] "Meal delivery" is a service that delivers meals ordered from a specific location to a location specified by the user.

[0963] "Health status" refers to the user's physical and mental health status.

[0964] "Dietary advice" refers to suggestions regarding meal menus and eating methods based on the user's health condition.

[0965] A "system" is a configuration in which multiple elements, such as terminals, servers, and generative AI, work together to achieve a function.

[0966] This invention provides a system that allows users to anonymously seek health advice regarding meal delivery. Specific embodiments are described below.

[0967] System program

[0968] The system uses the following hardware and software:

[0969] Hardware: Terminals that receive user input, servers that process data.

[0970] Software: Web framework "Flask", natural language processing library "transformers"

[0971] Program processing

[0972] 1. Receiving user input

[0973] The server receives health consultation messages related to meal delivery entered by the user via their terminal. User input is in text format and may include things like, "I've gained weight recently, do you have any recommendations for healthy menu items?"

[0974] 2. Message Analysis

[0975] The server analyzes received messages using natural language processing. Specifically, it extracts concerns and questions from the message content.

[0976] 3. Generating answers using generative AI

[0977] The server queries a generative AI, such as "GPT-3.5-turbo," based on the extracted concerns and questions, and generates appropriate answers. This includes dietary advice that takes health conditions into consideration.

[0978] 4. Submit your response

[0979] The generated response is sent from the server to the user's device. The user views this response on their device and uses the advice as a reference.

[0980] Specific example

[0981] The following is a concrete example of how this system works.

[0982] User input example

[0983] "I've gained weight recently. Do you have any recommendations for healthy meals?"

[0984] Example answers from a generative AI model

[0985] "If you've recently gained weight, healthy options like salads, grilled chicken, and smoothies are recommended. It's especially important to keep your calorie intake down at night."

[0986] In this way, users can easily receive anonymous dietary advice related to health. This system helps users make healthy choices within meal delivery services and can provide meal options tailored to their individual health conditions and concerns.

[0987] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0988] Step 1:

[0989] Receiving user input

[0990] Users enter and send health consultation messages related to meal delivery through their terminals. This input is in text format and may include phrases such as, "I've gained weight recently, do you have any recommendations for healthy menu options?"

[0991] Input: User consultation message sent from the device.

[0992] Output: User input data sent to the server.

[0993] Step 2:

[0994] Message received

[0995] The server receives user input messages sent from the terminal.

[0996] Input: A text message sent by the user from their device.

[0997] Output: Data of the received user message.

[0998] Step 3:

[0999] Message parsing

[1000] The server analyzes received messages using natural language processing (NLP) techniques to extract user concerns and questions. For example, it might extract information such as "I'm gaining weight" or "I'd like recommendations for healthy meals."

[1001] Input: Received user message data.

[1002] Output: Data on concerns and questions extracted through analysis.

[1003] Step 4:

[1004] Generating answers using generative AI

[1005] Based on the analyzed concerns and questions, the server queries a generative AI (e.g., GPT-3.5-turbo) to generate appropriate answers. The generated answers include dietary advice that takes the user's health condition into consideration. For example, "Healthy menu options such as salads, grilled chicken, and smoothies are recommended."

[1006] Input: Data on concerns and questions extracted through analysis.

[1007] Output: Response data generated by a generative AI.

[1008] Step 5:

[1009] Submit your response

[1010] The server sends the generated responses to the user's device, allowing the user to view them on their device. These responses may include advice and recommended meal options.

[1011] Input: Generated response data.

[1012] Output: The response message sent to the user's device.

[1013] Step 6:

[1014] Display and view answers

[1015] The user's device displays the response message received from the server, and the user reads it. For example, they might see a message like, "Healthy menu items such as salads, grilled chicken, and smoothies are recommended."

[1016] Input: The response message sent from the server.

[1017] Output: The advice displayed on the user's terminal.

[1018] 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.

[1019] The present invention aims to provide more accurate advice and support by combining a system for anonymously consulting about users' worries and questions with an emotion engine that recognizes emotions from the user's input messages. Specific embodiments are described below.

[1020] 1. User-side processing

[1021] What the user does first

[1022] The user launches a social networking service application on their device and accesses the official account "Trouble Consultation Service".

[1023] Enter message

[1024] The user enters their worries or questions as a text message and presses the send button. For example, they might type, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?"

[1025] 2. Processing on the terminal side

[1026] Send message

[1027] After the user presses the send button, the device sends the message to the server via the LINE API or similar.

[1028] 3. Server-side processing

[1029] Message received

[1030] The server receives user messages sent from the terminal.

[1031] Message Controller

[1032] The server analyzes the received messages and extracts the user's concerns and questions. Using natural language processing techniques, it identifies information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[1033] Emotion recognition by an emotion engine

[1034] The server uses an emotion engine to recognize the user's emotions based on the extracted information. For example, it identifies emotional states such as "depressed" or "anxious."

[1035] Answer generation by generative AI

[1036] The server queries a generative AI (for example, a text generation model) based on the analysis results and emotion recognition results. The AI ​​generates an appropriate response according to the user's emotional state. For example, it might generate a response such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1037] Submit your response

[1038] The generated response is sent from the server to the user's device via the LINE API.

[1039] 4. User-side processing

[1040] Display and view answers

[1041] The response message received on the user's device is displayed. The user reads something like, "Message from the counseling service: If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1042] Specific example

[1043] Flow from user problem input to resolution

[1044] 1. User-side processing

[1045] A user enters "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" into the "Advice Service" on the LINE app and presses the send button.

[1046] 2. Processing on the terminal side

[1047] The device sends the user's message to the server via the LINE API.

[1048] 3. Server-side processing

[1049] The server receives the message and analyzes its contents. For example, it might extract information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[1050] The server uses an emotion engine to recognize emotions based on the extracted results. For example, it identifies emotional states such as "depressed" or "anxious."

[1051] The server queries the generative AI based on the analysis results and emotion recognition results, and the AI ​​generates a response such as, "If you're feeling down, it's important to set aside time to relax. You can change your mood by getting some moderate exercise or enjoying a hobby."

[1052] The server sends the generated response to the device via the LINE API.

[1053] 4. User-side processing

[1054] The device receives the response message from the server and displays it in the LINE app.

[1055] Users read messages such as, "Message from the advice service: If you're feeling down, it's important to take time to relax. Moderate exercise or enjoying a hobby can help lift your spirits," and then take the advice to heart.

[1056] This format allows users to easily discuss worries and questions they can't talk about with others and receive appropriate advice. Furthermore, by using an emotion engine, more accurate responses tailored to the user's emotional state are provided, which is expected to further promote mental health and reduce the number of suicides.

[1057] The following describes the processing flow.

[1058] Step 1:

[1059] The user launches the LINE app and starts a chat with the official account "Trouble Consultation Service." The user types, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" and presses the send button.

[1060] Step 2:

[1061] The device sends the user's input message to the server via the LINE API. The message is forwarded to the server in the appropriate format.

[1062] Step 3:

[1063] The server receives messages from users via the LINE API. The received messages are stored as they are.

[1064] Step 4:

[1065] The server analyzes the received messages. Using natural language processing techniques, it extracts specific details about the user's concerns and questions. The analysis results include information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[1066] Step 5:

[1067] The server uses an emotion engine based on the information it extracts to recognize the user's emotions. For example, it might identify emotional states such as "depressed" or "anxious."

[1068] Step 6:

[1069] The server queries the generative AI based on the analysis results and emotion recognition results. It sends a specific question to the generative AI, such as, "Please tell me how to deal with a user who is feeling down at work."

[1070] Step 7:

[1071] A generative AI receives inquiries from the server and generates appropriate responses based on the user's emotional state. For example, it might generate a response such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1072] Step 8:

[1073] The server receives the response from the generative AI and converts it into an appropriate message format. The converted message is then sent to the user's device via the LINE API.

[1074] Step 9:

[1075] The device receives a message from the server and displays it. The user's LINE app displays the following message: "Message from the advice service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1076] Step 10:

[1077] Users read messages displayed on their devices and consider actions based on the advice. For example, they might try to change their mood by taking time to relax or getting some moderate exercise.

[1078] (Example 2)

[1079] 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."

[1080] Conventional counseling systems have had difficulty accurately recognizing users' emotions, resulting in answers that do not adequately address the user's psychological state. This has led to decreased user satisfaction and a lack of essential support. This invention aims to solve these problems and provide more accurate answers that are tailored to the user's emotional state.

[1081] 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 user input from a terminal, means for analyzing the received user input to extract the content of worries and questions, means for recognizing the user's emotions using an emotion engine based on the extracted content of worries and questions, means for generating an answer corresponding to the recognized user's emotions, means for querying a generative AI to generate an appropriate answer, and means for transmitting the generated answer to the terminal. This makes it possible to provide more accurate answers that correspond to the user's emotional state.

[1082] "Terminal" refers to a device that a user uses to send input messages.

[1083] "User input" refers to text messages containing concerns or questions sent by users using their devices.

[1084] "Means of receiving" refers to the functions and methods by which a server receives user input transmitted from a terminal.

[1085] "Means of analysis" refers to functions and methods for analyzing the content of received user input and extracting specific information about their concerns and questions.

[1086] An "emotion engine" refers to software or an algorithm that recognizes a user's emotions based on extracted concerns and questions.

[1087] "Means of recognition" refers to functions and methods for identifying a user's emotional state using an emotion engine.

[1088] "Generative AI" refers to artificial intelligence technology that generates appropriate responses in natural language based on input information.

[1089] "Generative means" refers to functions and methods that use generative AI to create answers tailored to the user's concerns and emotional state.

[1090] "Means of transmission" refers to the functions or methods for sending the generated response back to the device.

[1091] This invention aims to provide more accurate advice and support by combining an emotion engine with a system for anonymously consulting users about their worries and questions. The configuration and operation of this system are described in detail below.

[1092] The user launches a social networking service application on their device and accesses the official account "Trouble Consultation Service." They enter their troubles or questions as a text message, for example, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" and then press the send button to send it.

[1093] The terminal sends messages sent by the user to the server via the communication network. The communication protocol used for this is the common HTTP request. Messages are sent using services such as the LINE API.

[1094] The server receives user messages sent from terminals. The received messages are first analyzed using natural language processing (NLP) techniques. For example, natural language processing libraries such as spaCy or BERT are used for this analysis. Through this analysis, information about specific problems or questions is extracted from the message content. For example, information such as "I'm feeling down at work" or "I want to know how to cheer myself up" may be identified.

[1095] Next, the server uses an emotion engine to recognize the user's emotions based on the extracted information. This emotion recognition can utilize emotion recognition software such as IBM Watson, Aylien, or Microsoft Text Analytics API. This allows the server to identify emotional states such as "depressed" or "anxious."

[1096] Based on the emotion recognition results and analysis results, the server queries a generative AI model. For example, OpenAI's GPT-3 is used as the generative AI model. Specific prompt statements are input to this AI model, and an appropriate response is generated. Examples of prompt statements include the following:

[1097] "The user is feeling depressed. The following input is advice regarding the user's depression at work: Moderate exercise and enjoying hobbies can help lift their spirits."

[1098] The generated responses might include something like, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1099] Finally, the server sends the generated response to the device via the LINE API. The device displays the received response message in the LINE app, allowing the user to view it and receive advice.

[1100] As described above, the present invention makes it possible to automatically generate and provide appropriate advice according to the user's emotional state. This allows users to receive more specific and appropriate support for their worries and questions.

[1101] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1102] Step 1:

[1103] The user launches the LINE app and accesses the official account "Trouble Consultation Service".

[1104] Specifically, the user searches for the official account within the LINE app and opens the chat window.

[1105] Step 2:

[1106] The user enters their concerns or questions as a text message and presses the send button.

[1107] Input: A text message entered by the user (e.g., "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?")

[1108] Output: Message sent via LINE API

[1109] At this point, the user presses the "Send" button, sending a text message to the server.

[1110] Step 3:

[1111] The device sends the entered message to the server via the LINE API.

[1112] Input: Text message sent by the user

[1113] Output: Messages are received on the server via the LINE API.

[1114] Specifically, the device sends the message to the server in the format of an HTTP request using the LINE API.

[1115] Step 4:

[1116] The server analyzes the received messages and extracts the content of the concerns and questions.

[1117] Input: Text message received from LINE API

[1118] Output: Specific information about the analyzed worries and questions (e.g., "I'm feeling down about work," "I want to know how to change my mood")

[1119] The server analyzes the received message using a natural language processing library (e.g., spaCy or BERT) and extracts specific information.

[1120] Step 5:

[1121] The server uses an emotion engine to recognize the user's emotions based on the extracted information.

[1122] Input: Information on extracted problems and questions

[1123] Output: Recognized user emotional state (e.g., "depressed," "anxious")

[1124] This involves performing specific actions to identify the user's emotional state using an emotion engine (e.g., IBM Watson, Aylien, Microsoft Text Analytics API).

[1125] Step 6:

[1126] The server inputs prompts into a generative AI model based on the analysis results and emotion recognition results, generating an appropriate response.

[1127] Input: Analysis results and emotion recognition results

[1128] Output: Generated response text (Example: "When you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits.")

[1129] The server generates prompts for a generative AI model (e.g., OpenAI's GPT-3), and an example of a prompt is as follows: "The user is feeling depressed. The following input is advice regarding the user's depression at work. Moderate exercise and enjoying hobbies can help lift your spirits."

[1130] Step 7:

[1131] The server sends the generated response to the device via the LINE API.

[1132] Input: Generated response text

[1133] Output: Message sent to the device via LINE API

[1134] Specifically, the generated response is sent in the format of an HTTP request using the LINE API.

[1135] Step 8:

[1136] The device displays the response message from the server in the LINE app.

[1137] Input: Response message received from the server

[1138] Output: The response text displayed within the LINE app.

[1139] Specifically, the device displays the message received from the LINE API.

[1140] Step 9:

[1141] View the response messages the user has received and get advice.

[1142] Input: Answer text displayed within the LINE app

[1143] Output: Advice the user receives (Example: "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits.")

[1144] Specifically, the user reads messages within the LINE app and uses the appropriate advice as a reference.

[1145] (Application Example 2)

[1146] 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."

[1147] In recent years, with the increasing prevalence of autonomous vehicles and the resulting increase in the amount of time spent inside vehicles, there is a growing need to reduce the stress and anxiety users experience and provide a comfortable in-car environment. However, conventional systems lack the mechanisms to provide appropriate support in real time in response to users' emotional states. In particular, it is crucial to maintain users' mental well-being by appropriately recognizing negative emotions they experience and providing appropriate advice.

[1148] 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.

[1149] In this invention, the server includes means for receiving user input from a terminal, means for analyzing the received user input to extract concerns and questions, means for using an emotion engine to recognize the user's emotions based on the extracted concerns and questions, means for querying a generative AI based on the extracted concerns and questions and the recognized user emotions to generate an appropriate response, and means for transmitting the generated response to the terminal. This makes it possible to sense the stress and anxiety felt by the user in real time within an autonomous vehicle and provide appropriate advice according to their emotions.

[1150] A "terminal" is an electronic device used by a user for input, and includes smartphones, tablets, computers, and other similar devices.

[1151] A "server" is a computer system that receives requests from multiple terminals via a network and provides services or information in response to those requests.

[1152] "User input" refers to text and audio data of concerns and questions provided by users through their devices.

[1153] An "emotion engine" is software or an algorithm that recognizes and analyzes emotions from user input data.

[1154] "Generative AI" refers to artificial intelligence that generates natural language text based on a given prompt, and generally uses natural language processing techniques.

[1155] "Answer" refers to advice and information provided in response to a user's concerns or questions, and is text data generated by a generative AI.

[1156] An "autonomous vehicle" is a vehicle designed to drive automatically without human intervention.

[1157] An "infotainment system" is an in-vehicle system that provides information and entertainment to the vehicle's user, and includes displays, sound systems, and other components.

[1158] A "social networking service" is a platform on the internet that allows users to share information and communicate with each other.

[1159] A "text generation model" is a machine learning model used to generate natural language text, and is based on deep learning technology.

[1160] "Voice input" refers to input data provided by the user via voice, which is converted into text using speech recognition technology.

[1161] This invention is a system that senses the stress and anxiety experienced by users in autonomous vehicles in real time and provides appropriate advice. Specifically, it receives voice or text messages entered by the user in the vehicle, analyzes the user's concerns and questions based on their content, and recognizes their emotions using an emotion engine. Based on this, it generates an appropriate response using a generative AI and outputs it to the infotainment system either by displaying it or by voice.

[1162] The server receives messages entered from the user's terminal. This includes a step where, in the case of voice input, speech recognition technology is used to convert it into text. The server then analyzes the received text message to extract the user's concerns and questions. This analysis uses natural language processing (NLP) techniques to identify the user's intent and requests.

[1163] Next, the server uses an emotion engine to recognize the user's emotions from the analyzed worries and questions. The emotion engine identifies emotional states such as "feeling stressed" or "feeling anxious." This can be done using emotion recognition APIs such as IBM Watson or Microsoft Azure.

[1164] Based on the recognized emotional state and the nature of the problem, the server sends a prompt to a generative AI to generate an appropriate response. For example, a text generation model such as OpenAI's GPT-3 is used as the generative AI. This prompt might take the following format:

[1165] "Please write advice on what to do if a user is experiencing significant stress."

[1166] The responses from the generative AI will be tailored to the user's emotional state. For example, "If you're feeling stressed, take a deep breath and relax. Listening to your favorite music or taking a short walk will refresh your mood."

[1167] The generated responses are sent from the server to the vehicle's infotainment system. The infotainment system presents the responses to the user through a display or audio output device. This reduces stress and anxiety the user feels in the car, resulting in a more comfortable driving experience.

[1168] For example, if a user in a car types "I've been feeling very stressed at work lately," the emotion engine will recognize the user's emotional state as "feeling very stressed" from this text. Then, the generative AI will generate advice such as, "If you're feeling stressed, take a deep breath and relax. Listening to your favorite music or taking a short walk can refresh your mood," and display it on the infotainment system.

[1169] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1170] Step 1:

[1171] The user enters a voice or text message using an input device (e.g., microphone, touchscreen) inside the autonomous vehicle. The user says, "I've been feeling very stressed at work lately." This voice is captured by the microphone or the text is entered.

[1172] Step 2:

[1173] The device converts the input audio data into text using speech recognition software (e.g., Google Speech-to-Text API). The input is audio data, and the output is the converted text data. The text would be "I've been feeling very stressed at work lately."

[1174] Step 3:

[1175] The terminal sends the received text message to the server. The input is text data, and the output is the data sent to the server. The server receives the user's message.

[1176] Step 4:

[1177] The server analyzes received text messages using natural language processing (NLP) techniques (e.g., spaCy) to extract concerns and questions. The input is text data, and the output is the analyzed elements of concerns and questions (e.g., "work," "stress").

[1178] Step 5:

[1179] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) based on the analyzed elements to recognize the user's emotional state. The input is the analyzed elements, and the output is the recognized emotional state (e.g., "stressed").

[1180] Step 6:

[1181] Based on the recognized emotional state and the nature of the problem, the server sends a prompt to a generative AI (e.g., OpenAI GPT-3) to generate an appropriate response. The input is a prompt sentence (e.g., "Please write advice on what to do if the user is feeling very stressed."), and the output is a generated text response (e.g., "If you are feeling stressed, take a deep breath and relax. Listening to your favorite music or taking a short walk can refresh your mood.").

[1182] Step 7:

[1183] The server sends the generated response to the terminal. The input is the generated text response, and the output is the data sent to the terminal.

[1184] Step 8:

[1185] The terminal displays or outputs the received response to the infotainment system. Input is a text response, and output is either displayed on the screen or output as audio from the speaker. The user confirms advice such as, "If you are feeling stressed, take a deep breath and relax."

[1186] Step 9:

[1187] The user attempts relaxation techniques based on the advice provided. The input is the received advice, and the output is the user's actions (e.g., deep breathing, listening to music). Through this process, the user's stress is reduced, and a comfortable in-car environment is provided.

[1188] 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.

[1189] 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.

[1190] 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.

[1191] [Fourth Embodiment]

[1192] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1193] 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.

[1194] 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).

[1195] 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.

[1196] 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.

[1197] 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).

[1198] 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.

[1199] 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.

[1200] 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.

[1201] 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.

[1202] 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.

[1203] 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.

[1204] 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".

[1205] This invention aims to maintain mental health and reduce the number of suicides by providing a system that allows users to anonymously consult about their worries and questions. Specific embodiments are described below.

[1206] 1. User-side processing

[1207] What the user does first

[1208] The user launches a social networking service application on their device and accesses the official account "Trouble Consultation Service".

[1209] Enter message

[1210] The user enters their worries or questions as a text message and presses the send button. For example, they might type, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?"

[1211] 2. Processing on the terminal side

[1212] Send message

[1213] After the user presses the send button, the device sends the message to the server via the LINE API or similar.

[1214] 3. Server-side processing

[1215] Message received

[1216] The server receives user messages sent from the terminal.

[1217] Message Controller

[1218] The server analyzes the received messages and extracts the user's concerns and questions. Using natural language processing techniques, it identifies information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[1219] Answer generation by generative AI

[1220] The server queries a generative AI (for example, a text generation model) based on the analyzed data. The AI ​​generates an appropriate answer to the user's question. For example, it might generate an answer such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1221] Submit your response

[1222] The generated response is sent from the server to the user's device via the LINE API.

[1223] 4. User-side processing

[1224] Display and view answers

[1225] The response message received on the user's device is displayed. The user reads something like, "Message from the counseling service: If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1226] Specific example

[1227] Flow from user problem input to resolution

[1228] 1. User-side processing

[1229] A user enters "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" into the "Advice Service" on the LINE app and presses the send button.

[1230] 2. Processing on the terminal side

[1231] The device sends the user's message to the server via the LINE API.

[1232] 3. Server-side processing

[1233] The server receives the message and analyzes its contents. For example, it might extract information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[1234] The server queries the generative AI based on the extracted results, and the AI ​​generates a response such as, "If you're feeling down, it's important to set aside time to relax. You can change your mood by getting some moderate exercise or enjoying a hobby."

[1235] The server sends the generated response to the device via the LINE API.

[1236] 4. User-side processing

[1237] The device receives the response message from the server and displays it in the LINE app.

[1238] Users read messages such as, "Message from the advice service: If you're feeling down, it's important to take time to relax. Moderate exercise or enjoying a hobby can help lift your spirits," and then take the advice to heart.

[1239] This format allows users to easily discuss problems and questions they can't talk about with others and receive appropriate advice. This is expected to promote mental health and reduce the number of suicides.

[1240] The following describes the processing flow.

[1241] Step 1:

[1242] The user launches the LINE app and starts a chat with the official account "Trouble Consultation Service." The user types, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" and presses the send button.

[1243] Step 2:

[1244] The device sends the user's input message to the server via the LINE API. The message is forwarded to the server in the appropriate format.

[1245] Step 3:

[1246] The server receives messages from users via the LINE API. The received messages are stored as they are.

[1247] Step 4:

[1248] The server analyzes the received messages. Using natural language processing techniques, it extracts specific details about the user's concerns and questions. The analysis results include information such as "I'm feeling down about work" and "I'd like to know how to cheer myself up."

[1249] Step 5:

[1250] The server queries the generative AI based on the analysis results. It sends a specific question to the generative AI (for example, a text generation model), such as "Please tell me how to deal with a user who is feeling down at work."

[1251] Step 6:

[1252] A generative AI receives inquiries from the server and generates appropriate answers to the user's concerns. For example, it might generate answers such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1253] Step 7:

[1254] The server receives the response from the generative AI and converts it into an appropriate message format. The converted message is then sent to the user's device via the LINE API.

[1255] Step 8:

[1256] The device receives a message from the server and displays it. The user's LINE app displays the following message: "Message from the advice service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1257] Step 9:

[1258] Users read messages displayed on their devices and consider actions based on the advice. For example, they might try to change their mood by taking time to relax or getting some moderate exercise.

[1259] (Example 1)

[1260] 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".

[1261] In modern society, many people suffer from mental health problems and have questions, but are often unable to confide in others or receive appropriate advice. As a result, their mental health suffers, and we face the serious problem of an increase in the number of suicides. Therefore, there is a need to provide a system in which users can anonymously and safely discuss their problems and questions and receive appropriate advice.

[1262] 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.

[1263] In this invention, the server includes means for receiving user input from a terminal, means for analyzing the received user input to extract the content of worries and questions, means for sending prompt sentences to a generative AI based on the extracted content of worries and questions to generate an appropriate answer, means for sending the generated answer to the terminal, and means for displaying the generated answer on the user's terminal. This makes it possible for users to anonymously consult about worries and questions they cannot talk about with others and to receive appropriate advice.

[1264] A "device" refers to a device that a user can directly operate, and this includes smartphones, tablets, and personal computers.

[1265] "User input" refers to information such as text messages and voice commands that users send to the system using their terminals.

[1266] A "server" refers to a central control unit that receives user input and performs processing and analysis; this includes databases and application servers.

[1267] "Means of receiving" refers to a function or module that allows the server to receive input sent from the user's terminal.

[1268] "Means of analysis" refers to a function or module that analyzes received user input, understands and interprets its content, and extracts concerns and questions.

[1269] "Means of extraction" refers to a function or module used to extract specific information from analyzed data.

[1270] "Generative AI" refers to artificial intelligence models that generate appropriate output (in this case, an answer) based on input data, and includes models specifically designed for text generation.

[1271] A "prompt sentence" refers to the input sentence presented to a generative AI, which serves as the basis for the AI ​​to generate an appropriate response.

[1272] "Means of transmission" refers to a function or module for sending the generated response from the server to the user's terminal.

[1273] "Means of display" refers to a function or module for visually displaying the response received on the user's device.

[1274] "Natural Language Processing (NLP)" refers to techniques for processing and understanding human language, and includes text analysis and sentiment analysis.

[1275] An "appropriate response" refers to a message that is useful to the user's concerns or questions and includes appropriate advice and information relevant to the situation.

[1276] "Social networking services" refer to platforms for people to interact online, and specific examples include SNS apps and messaging services.

[1277] This invention aims to maintain mental health and reduce the number of suicides by providing a system that allows users to anonymously consult about their worries and questions. Specific embodiments are described below.

[1278] Hardware and software to be used

[1279] This system uses the following hardware and software:

[1280] Devices: Smartphones, tablets, PCs, etc.

[1281] Servers: Database server, application server

[1282] Software: Social networking service (SNS) applications (e.g., LINE), natural language processing libraries (e.g., spacy, NLTK), generative AI models (e.g., GPT-3)

[1283] System operation

[1284] The operation of this system is as follows:

[1285] User-side processing

[1286] First, the user launches a social networking application on their device and accesses the official account "Trouble Consultation Service." The user then enters their problem or question as a text message and presses the send button. For example, they might type, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?"

[1287] Terminal-side processing

[1288] After the user presses the send button, the device sends the message to the server via the LINE API. At this time, the user's input is encoded, and an HTTP request is generated to send it to the LINE API.

[1289] Server-side processing

[1290] The server analyzes HTTP requests received through the LINE API and extracts the user's text message. Then, it uses natural language processing (NLP) to analyze the message content and extract the user's concerns and questions. For example, it identifies information such as "I'm feeling down at work" or "I want to know how to cheer myself up."

[1291] The server sends a prompt to the generative AI model based on the analysis results. For example, it might send a prompt like, "The user is asking, 'I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?' Please generate an appropriate answer for this situation." The generative AI model then generates an appropriate answer. For example, it might generate an answer like, "When you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1292] The generated response is sent from the server to the user's device via the LINE API.

[1293] User-side processing again

[1294] The response message received on the user's device is displayed. The user can gain specific advice by reading the message, such as "Message from the counseling service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1295] Specific example

[1296] Let's say a user enters the message "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" into the "Advice Service" on the LINE app and presses the send button. The device sends this message to the server via the LINE API. The server receives the message and extracts the information that the user is "feeling down because of work" and "wants to know how to cheer themselves up." Based on the extracted information, the server sends a prompt to the generative AI: "The user is asking 'I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?' Please generate an appropriate answer for this situation." The generative AI generates an answer such as "When you're feeling down, it's important to set aside time to relax. You can cheer yourself up by getting some moderate exercise or enjoying a hobby," and the server sends this to the user via the LINE API. The user can then check this content on their device and use it as a reference.

[1297] Thus, the system of the present invention provides users with quick and appropriate answers to their worries and questions, contributing to the maintenance of their mental health.

[1298] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1299] Step 1:

[1300] The user launches the SNS application and enters the details of their inquiry.

[1301] The user launches the SNS application on their device and accesses the official account "Trouble Consultation Service." Next, they enter their problem or question into the text box and press the send button.

[1302] Input: User's text message (e.g., "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?")

[1303] Output: The text message is passed to the sender on the terminal side.

[1304] Step 2:

[1305] The device sends a message to the server.

[1306] The device sends the user's input to the server via the LINE API. During this process, the input data is converted to JSON format and sent as an HTTP request.

[1307] Input: User's text message

[1308] Output: HTTP request sent to the server via the LINE API

[1309] Step 3:

[1310] The server receives the message

[1311] The server interprets HTTP requests received through the LINE API and extracts the user's text message. The message data is processed at a specific endpoint within the server.

[1312] Input: HTTP request received from LINE API

[1313] Output: User's text messages are stored on the server.

[1314] Step 4:

[1315] The server analyzes the message content.

[1316] The server uses natural language processing (NLP) techniques to analyze incoming messages. This process involves tokenizing the text and performing sentiment analysis and keyword extraction.

[1317] Input: User's text message

[1318] Output: Analyzed data (e.g., "I'm feeling down at work," "I want to know how to cheer myself up," etc.)

[1319] Step 5:

[1320] The server sends a prompt message to the generative AI model.

[1321] Based on the analysis results, the server generates and sends appropriate prompts to the generative AI model. For example, a prompt might read, "The user is asking, 'I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?' Please generate an appropriate answer to this situation."

[1322] Input: Analyzed data

[1323] Output: Prompt message sent to the generative AI model

[1324] Step 6:

[1325] Generative AI models generate answers.

[1326] The generative AI model receives a prompt and generates an appropriate response. The AI ​​provides the response in natural language and returns it to the server.

[1327] Input: Prompt message

[1328] Output: Response text such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1329] Step 7:

[1330] The server sends the generated response to the user's device via the LINE API.

[1331] The server uses the LINE API to send the retrieved responses to the user's device. The response data is encoded in JSON format and sent to the device as an HTTP request.

[1332] Input: Generated response text

[1333] Output: HTTP request sent via LINE API

[1334] Step 8:

[1335] The device displays the response message.

[1336] The user's device decodes messages received from the LINE API and displays them on the application's interface. The user can then read and refer to these messages.

[1337] Input: HTTP request received from LINE API

[1338] Output: A message from the counseling service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits.

[1339] (Application Example 1)

[1340] 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".

[1341] In modern society, systems that allow people to anonymously seek advice are important for maintaining mental health, but systems that provide health consultations related to meal delivery are still lacking. Therefore, there is a need for an effective system that provides health-conscious meal options in delivery services. To solve this problem, a new system is needed that can suggest appropriate meals according to the user's health condition and concerns.

[1342] 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.

[1343] In this invention, the server includes means for receiving user input from a terminal, means for analyzing the received user input to extract concerns and questions, means for querying a generative AI based on the extracted concerns and questions to generate an appropriate answer, means for transmitting the generated answer to the terminal, and, if the user input relates to meal delivery, means for generating dietary advice tailored to the user's health condition. This allows users to anonymously consult about meal options tailored to their health condition and individual concerns.

[1344] A "terminal" is an electronic device used to send and receive data via a communication network.

[1345] "User input" refers to information or messages entered by a user using a terminal.

[1346] "Analysis" refers to the process of analyzing received user input and extracting its contents.

[1347] "Concerns and questions" refer to information about problems or matters that users want to discuss or ask about.

[1348] "Generative AI" is an artificial intelligence technology that generates appropriate responses based on input information.

[1349] An "inquiry" is a process that requests a generative AI to generate an answer based on the analysis results.

[1350] "Answer" refers to a response generated by a generative AI to a user's question or concern.

[1351] "Sending" refers to the process of sending the generated response back to the user's device.

[1352] "Meal delivery" is a service that delivers meals ordered from a specific location to a location specified by the user.

[1353] "Health status" refers to the user's physical and mental health status.

[1354] "Dietary advice" refers to suggestions regarding meal menus and eating methods based on the user's health condition.

[1355] A "system" is a configuration in which multiple elements, such as terminals, servers, and generative AI, work together to achieve a function.

[1356] This invention provides a system that allows users to anonymously seek health advice regarding meal delivery. Specific embodiments are described below.

[1357] System program

[1358] The system uses the following hardware and software:

[1359] Hardware: Terminals that receive user input, servers that process data.

[1360] Software: Web framework "Flask", natural language processing library "transformers"

[1361] Program processing

[1362] 1. Receiving user input

[1363] The server receives health consultation messages related to meal delivery entered by the user via their terminal. User input is in text format and may include things like, "I've gained weight recently, do you have any recommendations for healthy menu items?"

[1364] 2. Message Analysis

[1365] The server analyzes received messages using natural language processing. Specifically, it extracts concerns and questions from the message content.

[1366] 3. Generating answers using generative AI

[1367] The server queries a generative AI, such as "GPT-3.5-turbo," based on the extracted concerns and questions, and generates appropriate answers. This includes dietary advice that takes health conditions into consideration.

[1368] 4. Submit your response

[1369] The generated response is sent from the server to the user's device. The user views this response on their device and uses the advice as a reference.

[1370] Specific example

[1371] The following is a concrete example of how this system works.

[1372] User input example

[1373] "I've gained weight recently. Do you have any recommendations for healthy meals?"

[1374] Example answers from a generative AI model

[1375] "If you've recently gained weight, healthy options like salads, grilled chicken, and smoothies are recommended. It's especially important to keep your calorie intake down at night."

[1376] In this way, users can easily receive anonymous dietary advice related to health. This system helps users make healthy choices within meal delivery services and can provide meal options tailored to their individual health conditions and concerns.

[1377] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1378] Step 1:

[1379] Receiving user input

[1380] Users enter and send health consultation messages related to meal delivery through their terminals. This input is in text format and may include phrases such as, "I've gained weight recently, do you have any recommendations for healthy menu options?"

[1381] Input: User consultation message sent from the device.

[1382] Output: User input data sent to the server.

[1383] Step 2:

[1384] Message received

[1385] The server receives user input messages sent from the terminal.

[1386] Input: A text message sent by the user from their device.

[1387] Output: Data of the received user message.

[1388] Step 3:

[1389] Message parsing

[1390] The server analyzes received messages using natural language processing (NLP) techniques to extract user concerns and questions. For example, it might extract information such as "I'm gaining weight" or "I'd like recommendations for healthy meals."

[1391] Input: Received user message data.

[1392] Output: Data on concerns and questions extracted through analysis.

[1393] Step 4:

[1394] Generating answers using generative AI

[1395] Based on the analyzed concerns and questions, the server queries a generative AI (e.g., GPT-3.5-turbo) to generate appropriate answers. The generated answers include dietary advice that takes the user's health condition into consideration. For example, "Healthy menu options such as salads, grilled chicken, and smoothies are recommended."

[1396] Input: Data on concerns and questions extracted through analysis.

[1397] Output: Response data generated by a generative AI.

[1398] Step 5:

[1399] Submit your response

[1400] The server sends the generated responses to the user's device, allowing the user to view them on their device. These responses may include advice and recommended meal options.

[1401] Input: Generated response data.

[1402] Output: The response message sent to the user's device.

[1403] Step 6:

[1404] Display and view answers

[1405] The user's device displays the response message received from the server, and the user reads it. For example, they might see a message like, "Healthy menu items such as salads, grilled chicken, and smoothies are recommended."

[1406] Input: The response message sent from the server.

[1407] Output: The advice displayed on the user's terminal.

[1408] 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.

[1409] The present invention aims to provide more accurate advice and support by combining a system for anonymously consulting about users' worries and questions with an emotion engine that recognizes emotions from the user's input messages. Specific embodiments are described below.

[1410] 1. User-side processing

[1411] What the user does first

[1412] The user launches a social networking service application on their device and accesses the official account "Trouble Consultation Service".

[1413] Enter message

[1414] The user enters their worries or questions as a text message and presses the send button. For example, they might type, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?"

[1415] 2. Processing on the terminal side

[1416] Send message

[1417] After the user presses the send button, the device sends the message to the server via the LINE API or similar.

[1418] 3. Server-side processing

[1419] Message received

[1420] The server receives user messages sent from the terminal.

[1421] Message Controller

[1422] The server analyzes the received messages and extracts the user's concerns and questions. Using natural language processing techniques, it identifies information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[1423] Emotion recognition by an emotion engine

[1424] The server uses an emotion engine to recognize the user's emotions based on the extracted information. For example, it identifies emotional states such as "depressed" or "anxious."

[1425] Answer generation by generative AI

[1426] The server queries a generative AI (for example, a text generation model) based on the analysis results and emotion recognition results. The AI ​​generates an appropriate response according to the user's emotional state. For example, it might generate a response such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1427] Submit your response

[1428] The generated response is sent from the server to the user's device via the LINE API.

[1429] 4. User-side processing

[1430] Display and view answers

[1431] The response message received on the user's device is displayed. The user reads something like, "Message from the counseling service: If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1432] Specific example

[1433] Flow from user problem input to resolution

[1434] 1. User-side processing

[1435] A user enters "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" into the "Advice Service" on the LINE app and presses the send button.

[1436] 2. Processing on the terminal side

[1437] The device sends the user's message to the server via the LINE API.

[1438] 3. Server-side processing

[1439] The server receives the message and analyzes its contents. For example, it might extract information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[1440] The server uses an emotion engine to recognize emotions based on the extracted results. For example, it identifies emotional states such as "depressed" or "anxious."

[1441] The server queries the generative AI based on the analysis results and emotion recognition results, and the AI ​​generates a response such as, "If you're feeling down, it's important to set aside time to relax. You can change your mood by getting some moderate exercise or enjoying a hobby."

[1442] The server sends the generated response to the device via the LINE API.

[1443] 4. User-side processing

[1444] The device receives the response message from the server and displays it in the LINE app.

[1445] Users read messages such as, "Message from the advice service: If you're feeling down, it's important to take time to relax. Moderate exercise or enjoying a hobby can help lift your spirits," and then take the advice to heart.

[1446] This format allows users to easily discuss worries and questions they can't talk about with others and receive appropriate advice. Furthermore, by using an emotion engine, more accurate responses tailored to the user's emotional state are provided, which is expected to further promote mental health and reduce the number of suicides.

[1447] The following describes the processing flow.

[1448] Step 1:

[1449] The user launches the LINE app and starts a chat with the official account "Trouble Consultation Service." The user types, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" and presses the send button.

[1450] Step 2:

[1451] The device sends the user's input message to the server via the LINE API. The message is forwarded to the server in the appropriate format.

[1452] Step 3:

[1453] The server receives messages from users via the LINE API. The received messages are stored as they are.

[1454] Step 4:

[1455] The server analyzes the received messages. Using natural language processing techniques, it extracts specific details about the user's concerns and questions. The analysis results include information such as "I'm feeling down about work" or "I want to know how to cheer myself up."

[1456] Step 5:

[1457] The server uses an emotion engine based on the information it extracts to recognize the user's emotions. For example, it might identify emotional states such as "depressed" or "anxious."

[1458] Step 6:

[1459] The server queries the generative AI based on the analysis results and emotion recognition results. It sends a specific question to the generative AI, such as, "Please tell me how to deal with a user who is feeling down at work."

[1460] Step 7:

[1461] A generative AI receives inquiries from the server and generates appropriate responses based on the user's emotional state. For example, it might generate a response such as, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1462] Step 8:

[1463] The server receives the response from the generative AI and converts it into an appropriate message format. The converted message is then sent to the user's device via the LINE API.

[1464] Step 9:

[1465] The device receives a message from the server and displays it. The user's LINE app displays the following message: "Message from the advice service: If you are feeling down, it is important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1466] Step 10:

[1467] Users read messages displayed on their devices and consider actions based on the advice. For example, they might try to change their mood by taking time to relax or getting some moderate exercise.

[1468] (Example 2)

[1469] 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".

[1470] Conventional counseling systems have had difficulty accurately recognizing users' emotions, resulting in answers that do not adequately address the user's psychological state. This has led to decreased user satisfaction and a lack of essential support. This invention aims to solve these problems and provide more accurate answers that are tailored to the user's emotional state.

[1471] 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 user input from a terminal, means for analyzing the received user input to extract the content of worries and questions, means for recognizing the user's emotions using an emotion engine based on the extracted content of worries and questions, means for generating an answer corresponding to the recognized user's emotions, means for querying a generative AI to generate an appropriate answer, and means for transmitting the generated answer to the terminal. This makes it possible to provide more accurate answers that correspond to the user's emotional state.

[1472] "Terminal" refers to a device that a user uses to send input messages.

[1473] "User input" refers to text messages containing concerns or questions sent by users using their devices.

[1474] "Means of receiving" refers to the functions and methods by which a server receives user input transmitted from a terminal.

[1475] "Means of analysis" refers to functions and methods for analyzing the content of received user input and extracting specific information about their concerns and questions.

[1476] An "emotion engine" refers to software or an algorithm that recognizes a user's emotions based on extracted concerns and questions.

[1477] "Means of recognition" refers to functions and methods for identifying a user's emotional state using an emotion engine.

[1478] "Generative AI" refers to artificial intelligence technology that generates appropriate responses in natural language based on input information.

[1479] "Generative means" refers to functions and methods that use generative AI to create answers tailored to the user's concerns and emotional state.

[1480] "Means of transmission" refers to the functions or methods for sending the generated response back to the device.

[1481] This invention aims to provide more accurate advice and support by combining an emotion engine with a system for anonymously consulting users about their worries and questions. The configuration and operation of this system are described in detail below.

[1482] The user launches a social networking service application on their device and accesses the official account "Trouble Consultation Service." They enter their troubles or questions as a text message, for example, "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?" and then press the send button to send it.

[1483] The terminal sends messages sent by the user to the server via the communication network. The communication protocol used for this is the common HTTP request. Messages are sent using services such as the LINE API.

[1484] The server receives user messages sent from terminals. The received messages are first analyzed using natural language processing (NLP) techniques. For example, natural language processing libraries such as spaCy or BERT are used for this analysis. Through this analysis, information about specific problems or questions is extracted from the message content. For example, information such as "I'm feeling down at work" or "I want to know how to cheer myself up" may be identified.

[1485] Next, the server uses an emotion engine to recognize the user's emotions based on the extracted information. This emotion recognition can utilize emotion recognition software such as IBM Watson, Aylien, or Microsoft Text Analytics API. This allows the server to identify emotional states such as "depressed" or "anxious."

[1486] Based on the emotion recognition results and analysis results, the server queries a generative AI model. For example, OpenAI's GPT-3 is used as the generative AI model. Specific prompt statements are input to this AI model, and an appropriate response is generated. Examples of prompt statements include the following:

[1487] "The user is feeling depressed. The following input is advice regarding the user's depression at work: Moderate exercise and enjoying hobbies can help lift their spirits."

[1488] The generated responses might include something like, "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits."

[1489] Finally, the server sends the generated response to the device via the LINE API. The device displays the received response message in the LINE app, allowing the user to view it and receive advice.

[1490] As described above, the present invention makes it possible to automatically generate and provide appropriate advice according to the user's emotional state. This allows users to receive more specific and appropriate support for their worries and questions.

[1491] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1492] Step 1:

[1493] The user launches the LINE app and accesses the official account "Trouble Consultation Service".

[1494] Specifically, the user searches for the official account within the LINE app and opens the chat window.

[1495] Step 2:

[1496] The user enters their concerns or questions as a text message and presses the send button.

[1497] Input: A text message entered by the user (e.g., "I've been feeling down lately because things haven't been going well at work. What can I do to cheer myself up?")

[1498] Output: Message sent via LINE API

[1499] At this point, the user presses the "Send" button, sending a text message to the server.

[1500] Step 3:

[1501] The device sends the entered message to the server via the LINE API.

[1502] Input: Text message sent by the user

[1503] Output: Messages are received on the server via the LINE API.

[1504] Specifically, the device sends the message to the server in the format of an HTTP request using the LINE API.

[1505] Step 4:

[1506] The server analyzes the received messages and extracts the content of the concerns and questions.

[1507] Input: Text message received from LINE API

[1508] Output: Specific information about the analyzed worries and questions (e.g., "I'm feeling down about work," "I want to know how to change my mood")

[1509] The server analyzes the received message using a natural language processing library (e.g., spaCy or BERT) and extracts specific information.

[1510] Step 5:

[1511] The server uses an emotion engine to recognize the user's emotions based on the extracted information.

[1512] Input: Information on extracted problems and questions

[1513] Output: Recognized user emotional state (e.g., "depressed," "anxious")

[1514] This involves performing specific actions to identify the user's emotional state using an emotion engine (e.g., IBM Watson, Aylien, Microsoft Text Analytics API).

[1515] Step 6:

[1516] The server inputs prompts into a generative AI model based on the analysis results and emotion recognition results, generating an appropriate response.

[1517] Input: Analysis results and emotion recognition results

[1518] Output: Generated response text (Example: "When you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits.")

[1519] The server generates prompts for a generative AI model (e.g., OpenAI's GPT-3), and an example of a prompt is as follows: "The user is feeling depressed. The following input is advice regarding the user's depression at work. Moderate exercise and enjoying hobbies can help lift your spirits."

[1520] Step 7:

[1521] The server sends the generated response to the device via the LINE API.

[1522] Input: Generated response text

[1523] Output: Message sent to the device via LINE API

[1524] Specifically, the generated response is sent in the format of an HTTP request using the LINE API.

[1525] Step 8:

[1526] The device displays the response message from the server in the LINE app.

[1527] Input: Response message received from the server

[1528] Output: The response text displayed within the LINE app.

[1529] Specifically, the device displays the message received from the LINE API.

[1530] Step 9:

[1531] View the response messages the user has received and get advice.

[1532] Input: Answer text displayed within the LINE app

[1533] Output: Advice the user receives (Example: "If you're feeling down, it's important to set aside time to relax. Moderate exercise or enjoying a hobby can help lift your spirits.")

[1534] Specifically, the user reads messages within the LINE app and uses the appropriate advice as a reference.

[1535] (Application Example 2)

[1536] 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".

[1537] In recent years, with the increasing prevalence of autonomous vehicles and the resulting increase in the amount of time spent inside vehicles, there is a growing need to reduce the stress and anxiety users experience and provide a comfortable in-car environment. However, conventional systems lack the mechanisms to provide appropriate support in real time in response to users' emotional states. In particular, it is crucial to maintain users' mental well-being by appropriately recognizing negative emotions they experience and providing appropriate advice.

[1538] 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.

[1539] In this invention, the server includes means for receiving user input from a terminal, means for analyzing the received user input to extract concerns and questions, means for using an emotion engine to recognize the user's emotions based on the extracted concerns and questions, means for querying a generative AI based on the extracted concerns and questions and the recognized user emotions to generate an appropriate response, and means for transmitting the generated response to the terminal. This makes it possible to sense the stress and anxiety felt by the user in real time within an autonomous vehicle and provide appropriate advice according to their emotions.

[1540] A "terminal" is an electronic device used by a user for input, and includes smartphones, tablets, computers, and other similar devices.

[1541] A "server" is a computer system that receives requests from multiple terminals via a network and provides services or information in response to those requests.

[1542] "User input" refers to text and audio data of concerns and questions provided by users through their devices.

[1543] An "emotion engine" is software or an algorithm that recognizes and analyzes emotions from user input data.

[1544] "Generative AI" refers to artificial intelligence that generates natural language text based on a given prompt, and generally uses natural language processing techniques.

[1545] "Answer" refers to advice and information provided in response to a user's concerns or questions, and is text data generated by a generative AI.

[1546] An "autonomous vehicle" is a vehicle designed to drive automatically without human intervention.

[1547] An "infotainment system" is an in-vehicle system that provides information and entertainment to the vehicle's user, and includes displays, sound systems, and other components.

[1548] A "social networking service" is a platform on the internet that allows users to share information and communicate with each other.

[1549] A "text generation model" is a machine learning model used to generate natural language text, and is based on deep learning technology.

[1550] "Voice input" refers to input data provided by the user via voice, which is converted into text using speech recognition technology.

[1551] This invention is a system that senses the stress and anxiety experienced by users in autonomous vehicles in real time and provides appropriate advice. Specifically, it receives voice or text messages entered by the user in the vehicle, analyzes the user's concerns and questions based on their content, and recognizes their emotions using an emotion engine. Based on this, it generates an appropriate response using a generative AI and outputs it to the infotainment system either by displaying it or by voice.

[1552] The server receives messages entered from the user's terminal. This includes a step where, in the case of voice input, speech recognition technology is used to convert it into text. The server then analyzes the received text message to extract the user's concerns and questions. This analysis uses natural language processing (NLP) techniques to identify the user's intent and requests.

[1553] Next, the server uses an emotion engine to recognize the user's emotions from the analyzed worries and questions. The emotion engine identifies emotional states such as "feeling stressed" or "feeling anxious." This can be done using emotion recognition APIs such as IBM Watson or Microsoft Azure.

[1554] Based on the recognized emotional state and the nature of the problem, the server sends a prompt to a generative AI to generate an appropriate response. For example, a text generation model such as OpenAI's GPT-3 is used as the generative AI. This prompt might take the following format:

[1555] "Please write advice on what to do if a user is experiencing significant stress."

[1556] The responses from the generative AI will be tailored to the user's emotional state. For example, "If you're feeling stressed, take a deep breath and relax. Listening to your favorite music or taking a short walk will refresh your mood."

[1557] The generated responses are sent from the server to the vehicle's infotainment system. The infotainment system presents the responses to the user through a display or audio output device. This reduces stress and anxiety the user feels in the car, resulting in a more comfortable driving experience.

[1558] For example, if a user in a car types "I've been feeling very stressed at work lately," the emotion engine will recognize the user's emotional state as "feeling very stressed" from this text. Then, the generative AI will generate advice such as, "If you're feeling stressed, take a deep breath and relax. Listening to your favorite music or taking a short walk can refresh your mood," and display it on the infotainment system.

[1559] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1560] Step 1:

[1561] The user enters a voice or text message using an input device (e.g., microphone, touchscreen) inside the autonomous vehicle. The user says, "I've been feeling very stressed at work lately." This voice is captured by the microphone or the text is entered.

[1562] Step 2:

[1563] The device converts the input audio data into text using speech recognition software (e.g., Google Speech-to-Text API). The input is audio data, and the output is the converted text data. The text would be "I've been feeling very stressed at work lately."

[1564] Step 3:

[1565] The terminal sends the received text message to the server. The input is text data, and the output is the data sent to the server. The server receives the user's message.

[1566] Step 4:

[1567] The server analyzes received text messages using natural language processing (NLP) techniques (e.g., spaCy) to extract concerns and questions. The input is text data, and the output is the analyzed elements of concerns and questions (e.g., "work," "stress").

[1568] Step 5:

[1569] The server uses an emotion engine (e.g., IBM Watson Tone Analyzer) based on the analyzed elements to recognize the user's emotional state. The input is the analyzed elements, and the output is the recognized emotional state (e.g., "stressed").

[1570] Step 6:

[1571] Based on the recognized emotional state and the nature of the problem, the server sends a prompt to a generative AI (e.g., OpenAI GPT-3) to generate an appropriate response. The input is a prompt sentence (e.g., "Please write advice on what to do if the user is feeling very stressed."), and the output is a generated text response (e.g., "If you are feeling stressed, take a deep breath and relax. Listening to your favorite music or taking a short walk can refresh your mood.").

[1572] Step 7:

[1573] The server sends the generated response to the terminal. The input is the generated text response, and the output is the data sent to the terminal.

[1574] Step 8:

[1575] The terminal displays or outputs the received response to the infotainment system. Input is a text response, and output is either displayed on the screen or output as audio from the speaker. The user confirms advice such as, "If you are feeling stressed, take a deep breath and relax."

[1576] Step 9:

[1577] The user attempts relaxation techniques based on the advice provided. The input is the received advice, and the output is the user's actions (e.g., deep breathing, listening to music). Through this process, the user's stress is reduced, and a comfortable in-car environment is provided.

[1578] 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.

[1579] 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.

[1580] 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.

[1581] 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.

[1582] 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] 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."

[1587] 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.

[1588] 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.

[1589] 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.

[1590] 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.

[1591] 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.

[1592] 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.

[1593] 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.

[1594] 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.

[1595] 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.

[1596] 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.

[1597] 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.

[1598] 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.

[1599] The following is further disclosed regarding the embodiments described above.

[1600] (Claim 1)

[1601] A means of receiving user input from a terminal,

[1602] A method for analyzing received user input to extract concerns and questions,

[1603] A means of querying a generative AI based on extracted problems and questions to generate appropriate answers,

[1604] A means of sending the generated response to the terminal,

[1605] A system that includes this.

[1606] (Claim 2)

[1607] The system according to claim 1, wherein user input is in the format of a message from a social networking service.

[1608] (Claim 3)

[1609] The system according to claim 1, which uses a text generation model as a generative AI.

[1610] "Example 1"

[1611] (Claim 1)

[1612] A means of receiving user input from a terminal,

[1613] A method for analyzing received user input to extract concerns and questions,

[1614] A means of sending prompt sentences to a generative AI based on extracted problems and questions, and generating appropriate answers,

[1615] A means of sending the generated response to the terminal,

[1616] A means of displaying the answer generated on the user's device,

[1617] A system that includes this.

[1618] (Claim 2)

[1619] The system according to claim 1, wherein user input is in the format of a message from a social networking service.

[1620] (Claim 3)

[1621] The system according to claim 1, which uses a text generation model as a generative AI.

[1622] "Application Example 1"

[1623] (Claim 1)

[1624] A means of receiving user input from a terminal,

[1625] A method for analyzing received user input to extract concerns and questions,

[1626] A means of querying a generative AI based on extracted problems and questions to generate appropriate answers,

[1627] A means of sending the generated response to the terminal,

[1628] If user input relates to meal delivery, a means for generating dietary advice tailored to the user's health condition,

[1629] A system that includes this.

[1630] (Claim 2)

[1631] The system according to claim 1, wherein user input is in the format of a message from a social networking service.

[1632] (Claim 3)

[1633] The system according to claim 1, which uses a text generation model as a generative AI.

[1634] "Example 2 of combining an emotion engine"

[1635] (Claim 1)

[1636] A means of receiving user input from a terminal,

[1637] A method for analyzing received user input to extract concerns and questions,

[1638] A means of querying a generative AI based on extracted problems and questions to generate appropriate answers,

[1639] A means of sending the generated response to the terminal,

[1640] A means of recognizing the user's emotions using an emotion engine based on the extracted concerns and questions,

[1641] A means of generating responses that correspond to the recognized emotions of the user,

[1642] A system that includes this.

[1643] (Claim 2)

[1644] The system according to claim 1, wherein user input is in the format of a message for a communication network service.

[1645] (Claim 3)

[1646] The system according to claim 1, which uses a generative text model as a generative AI.

[1647] "Application example 2 when combining with an emotional engine"

[1648] (Claim 1)

[1649] A means of receiving user input from a terminal,

[1650] A method for analyzing received user input to extract concerns and questions,

[1651] Based on the extracted concerns and questions, an emotion engine is used to recognize the user's emotions,

[1652] A means of querying a generative AI based on extracted concerns and questions, as well as the recognized user's emotions, to generate an appropriate response,

[1653] A means of sending the generated response to the terminal,

[1654] A system that includes this.

[1655] (Claim 2)

[1656] The system according to claim 1, wherein user input is in the format of a message from a social networking service.

[1657] (Claim 3)

[1658] The system according to claim 1, which uses a text generation model as a generative AI.

[1659] (Claim 4)

[1660] The system according to claim 1, wherein user input is in the form of voice or text within an autonomous vehicle.

[1661] (Claim 5)

[1662] The system according to claim 4, further comprising means for displaying or outputting a response generated based on the emotion recognition results of the emotion engine to the in-vehicle infotainment system. [Explanation of Symbols]

[1663] 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 user input from a terminal, A method for analyzing received user input to extract concerns and questions, A means of querying a generative AI based on extracted problems and questions to generate appropriate answers, A means of sending the generated response to the terminal, A system that includes this.

2. The system according to claim 1, wherein user input is in the format of a message from a social networking service.

3. The system according to claim 1, which uses a text generation model as a generative AI.

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