System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving user input; means for analyzing the received user input to identify an emotion and a symptom; means for storing an analysis result in a database; means for generating a personalized response based on a profile of the user; and means for providing the generated response to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, many women suffer from premenstrual syndrome (PMS) and menopausal symptoms, but because they feel it is difficult to seek medical advice, they tend to bear these worries alone. Experiencing periods of mental instability can have a negative impact on their personal and social lives. There is a need for a system that provides these women with someone they can easily talk to and makes their symptoms visible, making it easier to report them to a doctor. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for receiving user input, a means for analyzing the received user input and identifying emotions and symptoms, a means for storing the analysis results in a database, a means for generating personalized responses based on the user's profile, and a means for providing the generated responses to the user. Furthermore, the system provides an environment that is easy for users to use by using a messaging platform as a means for recording and visualizing the user's symptom information and as a means for communication. This can help women better understand their symptoms, reduce their anxiety, and help them seek appropriate medical advice.

[0006] A "means for receiving user input" is a system or device for receiving data, such as text messages or voice inputs, sent by a user through a messaging platform.

[0007] "Means for analyzing received user input and identifying emotions and symptoms" refers to a system or program that uses natural language processing, emotion analysis algorithms, etc. to analyze received user messages and identify the user's emotional state and physical symptoms from their content.

[0008] The "means for saving the analysis results in a database" refers to a system or program for saving the emotional state and symptom data obtained by the analysis means in a database or other storage device.

[0009] "Means for generating personalized responses based on user profile" means a system or program that uses generative AI or other algorithms to generate individualized responses tailored to each user based on stored user profile information and analytical results.

[0010] A "means for providing a generated response to a user" is a system or device for sending and displaying a response message created by a generative AI to a user through a messaging platform.

[0011] "Means for recording and visualizing user symptom information" refers to a system or program for centrally recording symptom data obtained from users and visually displaying it in the form of graphs, charts, etc.

[0012] A "messaging platform" is an online service or application that enables users to communicate in the form of text messages, voice messages, images, videos, etc. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

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

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

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

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

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data, identifies the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life.

[0035] Specifically, this counseling service system consists of the following main functions:

[0036] 1. A means of receiving user input

[0037] It has the function of receiving data such as text messages and voice inputs sent by users through the messaging platform. When a user sends a message through LINE, the device receives the message and performs initial analysis to normalize the message.

[0038] 2. A means of analyzing received user input to identify emotions and symptoms.

[0039] The device sends the received message to a server, which then analyzes the message using a sentiment analysis algorithm and natural language processing model to identify the user's emotional state and symptoms, such as "headache" or "irritability."

[0040] 3. A means of storing the analysis results in a database

[0041] The server sends the analysis results to the database module, which stores the data in the database for further analysis and updating of the user's profile.

[0042] 4. A way to generate personalized responses based on a user's profile

[0043] The server retrieves the user's profile information from a database and uses a generative AI module to generate a personalized response based on the user's emotions and symptoms. For example, if a user sends a message saying, "I've been suffering from severe headaches and irritability lately," the system will generate a response such as, "You may be feeling stressed lately. Why don't you try some relaxation techniques?"

[0044] 5. How to provide generated responses to users

[0045] The server generates a response and sends it to the user's device via the LINE API, where the user receives and displays the message.

[0046] 6. A means of recording and visualizing user symptom information

[0047] The server stores the user's new symptom information in a database, which can later be used to visualize changes in symptoms. The recorded data can be displayed visually in graphs and charts, and can be used when reporting to medical institutions.

[0048] Specific examples

[0049] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the sequence of events is as follows:

[0050] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[0051] 2. Message analysis: The server passes the message to the chat analysis module, which performs sentiment analysis and keyword extraction to identify symptoms such as "headache" or "irritability."

[0052] 3. Result storage: The server stores the analysis results in a database and manages them together with the user's profile information.

[0053] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[0054] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[0055] 6. Information recording: The server stores the user's new symptom information in a database, which can be used later to visualize changes in symptoms.

[0056] This will provide users with an environment where they can easily seek advice, and it is expected that receiving appropriate advice will reduce anxiety and improve their quality of life.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] A user opens the LINE app, types a message, and presses the send button, which sends the user's input through the LINE messaging platform.

[0060] Step 2:

[0061] The device receives a message from the user via the LINE API, performs an initial analysis of the message content, normalizes the message, and converts it into text format.

[0062] Step 3:

[0063] The device sends the normalized message to the server, which receives it and passes it to the analysis module.

[0064] Step 4:

[0065] The server uses an analysis module to analyze the received messages, which uses natural language processing and sentiment analysis algorithms to identify keywords and emotional states (e.g., "headache" or "irritation") within the messages.

[0066] Step 5:

[0067] The server sends the analysis results to a database module, which stores the analysis data in the database, including the user's emotional state and identified symptoms.

[0068] Step 6:

[0069] The server retrieves the user's profile information from a database, including past messaging history and health information.

[0070] Step 7:

[0071] Based on the profile information and analysis results, the server passes the data to a generative AI module to generate personalized responses for the user, such as specific advice or encouraging messages tailored to the user's situation.

[0072] Step 8:

[0073] The server generates a response and sends it to the user's device via the LINE API. The device receives the response message and displays it to the user.

[0074] Step 9:

[0075] The user reads the message and takes appropriate measures based on their situation and mood. If necessary, they can send another message to ask for further advice.

[0076] Step 10:

[0077] The server continuously monitors new messages and changes in the user's condition, and records symptom information in a database, making it possible to visualize the progress and improvement of the user's symptoms.

[0078] Through these steps, users are provided with an environment where they can easily seek advice, and it is expected that the quality of their lives will improve by receiving appropriate advice.

[0079] Example 1

[0080] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0081] Many women today face health problems such as premenstrual syndrome (PMS) and menopausal symptoms, but there are limited environments in which they can easily consult about these symptoms. As a result, there is a need for effective ways to deal with these symptoms. In addition, there is a lack of systems to analyze symptoms and provide appropriate advice, making it a challenge to reduce users' anxiety and improve their quality of life.

[0082] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0083] In this invention, the server includes a means for normalizing the user's input, a means for analyzing the normalized input to identify emotions and symptoms, and a means for storing the analysis results of the identified emotions and symptoms in a database, thereby enabling the server to analyze the user's health status and provide personalized advice.

[0084] "User input" is data such as text messages or voice inputs that a user sends through a messaging application.

[0085] "Normalization" is the process of converting received data into a format that is easier to parse, and includes removing unnecessary whitespace and special characters.

[0086] "Analysis" refers to the process of applying natural language processing and sentiment analysis algorithms to the received data to identify the user's emotional state and symptoms.

[0087] "Emotions and symptoms" refers to specific emotions and physical conditions identified in a user's message, such as "irritability" or "headache."

[0088] A "database" is a structured collection of data that stores analysis results and user profile information and makes them available for later use.

[0089] "Profile information" is data that includes information specific to each user, such as their history, health status, and past messages.

[0090] A "personalized response" is a response message that is individually generated based on the user's profile information and current emotions and symptoms.

[0091] A "messaging application" is a software platform for sending and receiving text and voice messages over the Internet.

[0092] A "server" is a computer system that receives user input, analyzes it, stores the results in a database, and generates and serves a response.

[0093] "User symptom information" means information about physical or mental illness reported by a User through a Message.

[0094] "Visualization" is the process of representing collected data in a visual format, such as a graph or chart, to make it easier to understand.

[0095] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data to identify the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life.

[0096] 1. A means of receiving user input

[0097] user:

[0098] A user opens a messaging application such as LINE, types a message such as "I've been suffering from severe headaches and irritability lately," and sends it. The input data is in text or voice format.

[0099] Device:

[0100] The device receives the user's message via the LINE API. After receiving it, the device performs initial analysis by normalizing the message and converting the data into a format that is easy to parse.

[0101] 2. A means of analyzing received user input to identify emotions and symptoms.

[0102] Device:

[0103] The terminal sends the received message to the server.

[0104] server:

[0105] The server receives the message and performs sentiment analysis and natural language processing (NLP) using libraries such as Python's Natural Language Toolkit (NLTK), TENSORFLOW (registered trademark), and Keras. As a result of the analysis, symptoms such as "headache" or "irritability" are identified.

[0106] 3. A means of storing the analysis results in a database

[0107] server:

[0108] The server stores the analysis results in a database such as MySQL (registered trademark) or PostgreSQL, and then associates this data with the user's profile information.

[0109] 4. A way to generate personalized responses based on a user's profile

[0110] server:

[0111] The server retrieves the user's profile information from a database. Then, it uses a generative AI module (e.g., GPT-3 (registered trademark)) to generate a personalized response based on the user's emotions and symptoms. For example, if a user sends a message saying, "I've been suffering from severe headaches and irritability lately," the server might generate a response such as, "You may be feeling stressed lately. Why don't you try some relaxation techniques?"

[0112] 5. How to provide generated responses to users

[0113] server:

[0114] The server generates a response and sends it to the user's device via the LINE API.

[0115] Device:

[0116] The device displays the response received through the LINE API to the user.

[0117] 6. A means of recording and visualizing user symptom information

[0118] server:

[0119] The server saves new symptom information in a database. Based on the data saved by the server, visual data is generated to visualize changes in symptoms in graphs and charts. This is done using libraries such as JavaScript (registered trademark) D3.js and Python Matplotlib.

[0120] Specific examples

[0121] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the sequence of events is as follows:

[0122] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[0123] 2. Message analysis: The server passes the message to the chat analysis module, which performs sentiment analysis and keyword extraction to identify symptoms such as "headache" or "irritability."

[0124] 3. Result storage: The server stores the analysis results in a database and manages them together with the user's profile information.

[0125] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[0126] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[0127] 6. Information recording: The server stores the user's new symptom information in a database, which can be used later to visualize changes in symptoms.

[0128] This will provide users with an environment where they can easily seek advice, and it is expected that receiving appropriate advice will reduce anxiety and improve their quality of life.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1: Receiving User Input

[0131] user:

[0132] The user opens LINE, types "I've been suffering from severe headaches and irritability lately," and presses the send button. In this process, the user sends a message describing their symptoms and feelings.

[0133] Device:

[0134] The device receives the user's message via the LINE API. The received message data is in text format, and an initial analysis is performed on the device. Specifically, normalization processing is performed, such as removing unnecessary spaces and normalizing special characters. The input for this processing is the user's text message, and the output is the normalized text data.

[0135] Step 2: Message analysis

[0136] Device:

[0137] The terminal sends the normalized message data to the server, which receives it and prepares it for analysis. The input is normalized text data.

[0138] server:

[0139] The server performs sentiment analysis and natural language processing (NLP) using libraries such as Python's Natural Language Toolkit (NLTK), TensorFlow, and Keras. The analysis step involves extracting keywords from the text and tagging it with emotions, ultimately identifying symptoms such as "headache" or "irritability." The input is the received text data, and the output is the analysis results (identified symptoms and emotions).

[0140] Step 3: Save the analysis results

[0141] server:

[0142] The server sends the analyzed results to a database and stores them. Specifically, a database management system such as MySQL or PostgreSQL is used. In this storage process, the analysis results are associated with each user's profile information and stored. The input is the analysis results, and the output is the status of storage in the database.

[0143] Step 4: Generate a response

[0144] server:

[0145] The server retrieves the user's profile information from a database. Next, it uses a generative AI module such as OpenAI's (registered trademark) GPT-3 to generate a response. For example, if the user's symptoms involve "stress," it generates a response such as, "You may have been feeling stressed lately. Why don't you try some ways to relax?" The input is the user's profile information and the analysis results, and the output is the generated response message.

[0146] Step 5: Providing generated responses

[0147] server:

[0148] The server generates a response and sends it to the user's device via the LINE API.

[0149] Device:

[0150] The device displays the reply message received via the LINE API to the user. The input is the generated reply message, and the output is the message displayed on the user's device.

[0151] Step 6: Record and visualize information

[0152] server:

[0153] The server saves new symptom information in a database. The saved symptom information is managed as time-series data, and graphs and charts are generated later to visualize changes in symptoms. Specifically, visual data is created using libraries such as JavaScript's D3.js and Python's Matplotlib. The input is new symptom information, and the output is visualized data.

[0154] In this way, users can easily consult with a specialist and receive appropriate advice, which is expected to reduce anxiety and improve their quality of life.

[0155] (Application example 1)

[0156] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0157] There is a need for a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. However, existing systems make it difficult for users to immediately receive locally appropriate counseling, and there is a lack of means to make it easy to use in physical stores in particular. This makes it difficult for users to receive professional advice, and there are issues with improving their quality of life.

[0158] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0159] In this invention, the server includes means for receiving user input, means for analyzing the received user input and identifying emotions and symptoms, means for storing the analysis results in a database, means for generating a personalized response based on the user's profile, means for providing the generated response to the user, means for the user to access the service by scanning a QR code (registered trademark) in a physical store, and means for providing locally appropriate counseling based on the scanned QR code. This allows users to easily use counseling services even in physical stores, improving their quality of life.

[0160] "User" refers to an individual who uses the System, and is particularly targeted at women who suffer from premenstrual syndrome (PMS) and menopausal symptoms.

[0161] "Input" refers to the text messages and voice data that users send through the messaging platform.

[0162] "Analysis" refers to the process of identifying emotions and symptoms based on input data received from a user.

[0163] "Database" refers to a collection of data for storing analysis results and user profile information.

[0164] "Profile" refers to a data set containing a user's individual information that is used to generate customized responses.

[0165] "Response" refers to advice or information generated based on the analysis results and provided to the user.

[0166] A "QR code" is a two-dimensional code that encodes specific information and can be scanned by devices such as smartphones.

[0167] "Brick and Mortar Store" means a sales or service establishment that a user can physically visit.

[0168] This invention is a system that allows users to access counseling services in physical stores by scanning a QR code, and is aimed at women suffering from premenstrual syndrome (PMS) and menopausal symptoms.

[0169] The server first receives user input, which is sent via a messaging platform as a text message or voice data. The received data is then analyzed to identify emotions and symptoms. This analysis is performed using natural language processing models and sentiment analysis algorithms. Specifically, the Hugging Face sentiment analysis model is used.

[0170] The analysis results are stored in a database, which not only stores the analysis results but also the user's profile information, which is important for later generating personalized responses.

[0171] Based on the user's profile, the server uses a generative AI model to generate personalized responses, which are then provided to the user via messaging platforms such as the LINE API. Additionally, when the user scans a QR code in a physical store, they are provided with counseling services tailored to their local area.

[0172] This system is implemented using a smartphone, a database server, a generative AI model, and a messaging platform (such as the LINE API). A specific example of how a user can use the counseling service is to scan a QR code displayed on a drugstore counter with their smartphone and enter, "I've been suffering from severe PMS symptoms lately." An example of a prompt based on this principle is shown below.

[0173] Prompt Sentence Examples

[0174] USER: I've been suffering from severe PMS symptoms lately.

[0175] BOT: It sounds like you're experiencing PMS symptoms. Why not try some relaxation techniques?

[0176] This allows users to easily use counseling services in physical stores, improving their quality of life.

[0177] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0178] Step 1:

[0179] User scans the QR code

[0180] When customers visit a physical store, they scan a QR code with their smartphone, which launches a dedicated counseling application and loads the customer's profile information.

[0181] Input: QR code data

[0182] Output: Counseling application launches, user profile loads

[0183] Step 2:

[0184] Receive user input

[0185] Users input text and voice data on their smartphones, including symptoms and emotional state.

[0186] Input: User text messages and voice data

[0187] Output: User input data

[0188] Step 3:

[0189] Analyzing the received data

[0190] The device sends the received input data to a server, which then analyzes the data using sentiment analysis algorithms and natural language processing models to identify symptoms such as "headache" or "irritability."

[0191] Input: User-entered data

[0192] Output: Symptom and emotion analysis results

[0193] Step 4:

[0194] Save the analysis results in a database

[0195] The server stores the analysis results in a database and simultaneously updates the user's profile information.

[0196] Input: Analysis results, existing user profile information

[0197] Output: Updated database

[0198] Step 5:

[0199] Generate personalized responses

[0200] The server uses a generative AI model to generate personalized responses based on the user's profile and analysis results.

[0201] Input: User profile information, analysis results

[0202] Output: Personalized reply (text message)

[0203] Step 6:

[0204] Providing a response to the user

[0205] The server sends the generated response to the user via the LINE API or other messaging platform, and the device displays the response.

[0206] Input: Generated response

[0207] Output: Display reply message on user terminal

[0208] Step 7:

[0209] Record additional information in the database

[0210] The server records the new symptom information in a database that can later be used to visualize changes in symptoms.

[0211] Input: New symptom information

[0212] Output: Updated database

[0213] Through a series of steps, users can easily receive counseling services at a physical store using their smartphone.

[0214] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0215] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data, identifies the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life.

[0216] Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions, allowing for a more accurate understanding of the user's emotional state.

[0217] Specifically, this counseling service system consists of the following main functions:

[0218] 1. A means of receiving user input

[0219] It has the function of receiving data such as text messages and voice inputs sent by users through the messaging platform. When a user sends a message through LINE, the device receives the message and performs initial analysis to normalize the message.

[0220] 2. A means of analyzing received user input to identify emotions and symptoms.

[0221] The device sends the received message to a server, which then analyzes the message using a sentiment analysis algorithm, natural language processing model, and emotion engine to identify the emotional state and symptoms, such as "headache" or "irritability."

[0222] 3. A means of storing the analysis results in a database

[0223] The server sends the analysis results to a database module, which stores the data, including the user's emotional state and identified symptoms.

[0224] 4. A way to generate personalized responses based on a user's profile

[0225] The server retrieves the user's profile information from a database and uses a generative AI module to generate a personalized response based on the user's emotions and symptoms. For example, if a user sends a message saying, "I've been suffering from severe headaches and irritability lately," the system will generate a response such as, "You may be feeling stressed lately. Why don't you try some relaxation techniques?"

[0226] 5. How to provide generated responses to users

[0227] The server generates a response and sends it to the user's device via the LINE API, where the user receives and displays the message.

[0228] 6. A means of recording and visualizing user symptom information

[0229] The server stores the user's new symptom information in a database, which can later be used to visualize changes in symptoms. The recorded data can be displayed visually in graphs and charts, and can be used when reporting to medical institutions.

[0230] 7. Emotion Recognition with Emotion Engine

[0231] The server uses an emotion engine to analyze the user's emotional state in real time from their message and obtain emotional data. The emotion engine analyzes the user's current emotional state and generates an optimal response based on the analysis results. For example, if the user's message contains feelings of anxiety or anger, a calm and comforting message will be generated.

[0232] Specific examples

[0233] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the sequence of events is as follows:

[0234] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[0235] 2. Message analysis: The server passes the message to the chat analysis module, which uses sentiment analysis, keyword extraction, and an emotion engine to identify symptoms such as "headache" or "irritation" and analyze the user's emotional state (e.g., anxiety, anger).

[0236] 3. Result storage: The server stores the analysis results in a database and manages them together with the user's profile information.

[0237] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[0238] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[0239] 6. Information recording: The server stores the user's new symptom information in a database, which can be used later to visualize changes in symptoms.

[0240] Through these steps, users are provided with an environment where they can easily seek advice, and by receiving appropriate advice, they can reduce their anxiety and improve their quality of life. In addition, accurate emotion analysis by the emotion engine allows for more appropriate responses.

[0241] The processing flow will be explained below.

[0242] Step 1:

[0243] A user opens the LINE app, types a message, and presses the send button, which sends the user's input via the LINE messaging platform.

[0244] Step 2:

[0245] The device receives a message from the user via the LINE API. The device initially parses the message content and normalizes it into text format.

[0246] Step 3:

[0247] The device sends the normalized message to the server, which receives it and passes the data to the analysis module.

[0248] Step 4:

[0249] The server uses an analysis module to analyze the received messages, which uses natural language processing, sentiment analysis, and an emotion engine to identify keywords and emotional states (e.g., "headache" or "irritation") in the messages.

[0250] Step 5:

[0251] The server sends the analysis results to a database module, which stores the analysis data in the database, including information on the user's emotional state and identified symptoms.

[0252] Step 6:

[0253] The server retrieves the user's profile information from a database, including past messaging history and health information.

[0254] Step 7:

[0255] Based on the profile information and analysis results, the server passes the data to a generative AI module to generate personalized responses for the user, such as specific advice or encouraging messages tailored to the user's situation.

[0256] Step 8:

[0257] The server generates a response and sends it to the user's device via the LINE API. The device receives the response message and displays it to the user.

[0258] Step 9:

[0259] The user reads the message and takes appropriate measures based on their situation and mood. If necessary, they can send another message to ask for further advice.

[0260] Step 10:

[0261] The server continuously monitors new messages and changes in the user's condition, and records symptom information in a database, making it possible to visualize the progress and improvement of the user's symptoms.

[0262] Step 11:

[0263] The server uses an emotion engine to analyze the user's emotional state in real time from their message. The emotion engine identifies the user's current emotional state (e.g., anxiety, anger) and generates an optimal response based on the analysis results.

[0264] Step 12:

[0265] The server stores the analysis results of the emotion engine in a database and manages the user's emotion history, which is used for subsequent analysis and feedback to the user.

[0266] Example 2

[0267] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0268] The purpose of this invention is to provide a counseling service that women suffering from premenstrual syndrome (PMS) and menopausal symptoms can easily consult. Conventional counseling methods have been insufficient in analyzing symptoms and emotions, and have difficulty providing personalized advice. For this reason, there is a need for a system that can effectively alleviate users' anxiety and improve their quality of life.

[0269] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a user's input, a means for analyzing the received user's input and identifying emotions and symptoms, a means for saving the analysis result in an information store, a means for generating an individual response based on the user's attribute information, and a means for providing the generated response to the user. This makes it possible to accurately analyze the user's emotions and symptoms and provide appropriate advice.

[0270] "User Input" refers to data, such as text messages or voice inputs, that a User sends through a messaging platform.

[0271] "Means for receiving" refers to the functionality of receiving user input via the message sending platform and performing initial analysis to process the data.

[0272] "Means for analyzing" refers to the ability to analyze received user input data and utilize sentiment analysis algorithms and natural language processing models to identify emotions and symptoms.

[0273] "Information store" refers to a memory device such as a database or cloud storage where the server stores analysis results.

[0274] "Attribute information" refers to data that includes a user's profile information, and refers to information for each user that is managed individually.

[0275] "Means for generating individual responses" refers to a function that uses generative AI to generate personalized responses appropriate for the user based on the analysis results and the user's attribute information.

[0276] "Means for providing to users" refers to the function of sending the generated response to the user's device through the message sending platform, allowing the user to receive and check the message.

[0277] A "messaging platform" is a platform that allows users and systems to communicate with each other, such as LINE or SMS.

[0278] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data, identifies the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life. Furthermore, the invention incorporates an emotion engine that recognizes the user's emotions, enabling a more accurate understanding of the user's emotional state.

[0279] The system consists of the following main functions:

[0280] 1. A means of receiving user input

[0281] The device receives data such as text messages and voice input sent by the user through the messaging platform. For example, if a user sends a message via LINE such as "I've been suffering from severe headaches and irritability lately," the device receives the message through the LINE API. After receiving the data, it normalizes it and passes it to the next processing step.

[0282] 2. A means of analyzing received user input to identify emotions and symptoms.

[0283] The device sends the received message to the server, where it analyzes the message using a natural language processing engine and sentiment analysis algorithms. As a result of the analysis, keywords such as "headache" and "irritation" and the user's emotional state (e.g., anxiety, anger) are identified.

[0284] 3. A means of storing the analysis results in an information store

[0285] The server stores the analysis results in a database, which includes the user's emotional state and identified symptoms, and is managed for each user.

[0286] 4. A way to generate personalized responses based on user demographic information

[0287] The server retrieves the user's attribute information from the database and combines it with the analysis results. Generative AI is used to generate the optimal response for the user. For example, it might generate a response along the lines of, "You've probably been feeling stressed lately. Why not try some ways to relax?"

[0288] 5. How to provide generated responses to users

[0289] The server generates a response and sends it to the user's device via the LINE API, where the user can receive and view the message.

[0290] 6. A means of recording and visualizing user symptom information

[0291] The server records the user's new symptom information in a database, which is later used to visualize changes in symptoms. The recorded data is displayed visually in graphs and charts and can be used for reporting to medical institutions.

[0292] 7. Emotion Recognition with Emotion Engine

[0293] The server uses an emotion engine to analyze the user's emotional state in real time from their message and generates an optimal response based on the analysis results. For example, if the user's message contains feelings of anxiety or anger, it will generate a calm and comforting message in response.

[0294] Specific examples

[0295] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the series of processes is as follows:

[0296] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[0297] 2. Message analysis: The server passes the message to the chat analysis module, which uses sentiment analysis, keyword extraction, and an emotion engine to identify symptoms such as "headache" or "irritation" and analyze the user's emotional state (e.g., anxiety, anger).

[0298] 3. Result storage: The server stores the analysis results in an information store and manages them together with the user's profile information.

[0299] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[0300] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[0301] 6. Information recording: The server saves the user's new symptom information in an information store, which can be used later to visualize changes in symptoms.

[0302] Example prompts for generative AI models

[0303] An example of a prompt sentence might be:

[0304] "If a user sends a message saying, 'I've been having a lot of headaches and irritability lately,' analyze the user's emotional state and symptoms and generate an appropriate response."

[0305] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0306] Step 1:

[0307] A user sends a text message through a messaging platform. For example, the user opens the LINE app, types "I've been suffering from severe headaches and irritability lately," and presses the send button.

[0308] Input: A text message from the user.

[0309] Output: Raw text data received via the LINE API.

[0310] Specific behavior: The user types and sends a text message. The device receives this message via the LINE API.

[0311] Step 2:

[0312] Normalizes message data received by the terminal, converting input data into a unified format and preparing it for analysis.

[0313] Input: The received raw text data.

[0314] Output: Normalized text data.

[0315] Specific operation: Preprocesses the text to remove unnecessary spaces and special characters, and converts it into normalized text data.

[0316] Step 3:

[0317] The normalized message data is sent to the server.

[0318] Input: Normalized text data.

[0319] Output: The text data sent to the server.

[0320] Specific operation: The device sends the normalized data to the server via the network.

[0321] Step 4:

[0322] The server analyzes the received message data and uses sentiment analysis algorithms, natural language processing engines, and emotion engines to identify emotional states and symptoms.

[0323] Input: Normalized text data sent to the server.

[0324] Output: Analysis results showing identified emotional states and symptoms.

[0325] How it works: The text analysis engine extracts emotion and symptom keywords from the message, and the emotion engine determines the user's emotional state. For example, it identifies the keywords "headache" and "irritation" and determines the emotional state as "anxiety."

[0326] Step 5:

[0327] The analysis results are saved in a database.

[0328] Input: Emotional state and symptom data obtained as a result of the analysis.

[0329] Output: Analysis result data stored in a database.

[0330] Specific operation: The server adds emotional state and symptom data as records to the database.

[0331] Step 6:

[0332] Generate personalized responses based on user demographic information.

[0333] Input: User attribute information and analysis result data stored in the database.

[0334] Output: The generated reply message.

[0335] How it works: The server retrieves the user's attribute information from a database and uses generative AI to generate an appropriate response, such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?"

[0336] Step 7:

[0337] The generated response is provided to the user.

[0338] Input: The generated reply message.

[0339] Output: The reply message received by the user.

[0340] Specific operation: The server generates a response and sends it to the user's device via the LINE API. The user receives the message in the LINE app and displays it.

[0341] Step 8:

[0342] Record and visualize user symptom information.

[0343] Input: New symptom information.

[0344] Output: Visualized symptom information (graphs and charts).

[0345] What it does: The server stores new symptom information in a database and generates graphs and charts based on this data, allowing users and medical institutions to visually monitor changes in symptoms.

[0346] (Application example 2)

[0347] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0348] Until now, women suffering from premenstrual syndrome (PMS) and menopausal symptoms have had limited access to casual consultation services, making it difficult for them to receive appropriate counseling and advice. Furthermore, there has been no system that can provide appropriate dietary recommendations for health based on emotions and symptoms. Therefore, there is a need for a system that can analyze users' emotional state and symptoms in real time and provide personalized support.

[0349] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user input, means for analyzing the received user input and identifying emotions and symptoms, and means for storing the analysis results in a database. This makes it possible to generate health-related suggestions based on the analysis results of emotions and symptoms.

[0350] "Means for receiving user input" refers to a function for receiving text messages and voice data entered by the user.

[0351] "Means for analyzing received user input and identifying emotions and symptoms" refers to a function for analyzing received messages and identifying the user's emotional state or specific symptoms.

[0352] The "means for saving the analysis results in a database" is a function for saving the analyzed emotional state and symptom data in a database.

[0353] "Means for generating personalized responses based on a user's profile" refers to a function for generating individually appropriate responses using the user's profile information.

[0354] The "means for providing the generated response to the user" is a function for providing the generated response to the user's terminal.

[0355] The "means for generating health-related suggestions based on the results of the emotion and symptom analysis" is a function for making health-related suggestions to the user based on the analyzed emotional state and symptoms.

[0356] This invention is a counseling service system that allows users to easily receive health consultations, and is designed especially for women suffering from premenstrual syndrome (PMS) and menopausal symptoms. The system has the function of making health-related suggestions based on the analysis of emotions and symptoms.

[0357] First, the system receives text messages and voice data from users through a "means for receiving user input." Users access the system using devices such as smartphones and computers via messaging platforms such as LINE.

[0358] The received message is then analyzed by "means for analyzing received user input to identify emotions and symptoms," using a sentiment analysis engine and / or natural language processing models. This process identifies the user's emotional state (e.g., anxiety, anger) and symptoms (e.g., headache, irritability).

[0359] The analysis results are stored in a database by the "means for storing analysis results in a database." This database accumulates the user's emotional state and symptom history.

[0360] The analyzed data is then used to generate a personalized response based on the user's profile. This response is generated automatically using a generative AI model. For example, if a user posts, "I've been having terrible headaches and irritability lately," the system might generate a response such as, "You're probably feeling stressed lately. Why don't you try some relaxation techniques?"

[0361] Additionally, the device includes a "means for generating health-related suggestions based on emotion and symptom analysis," which provides health-related suggestions based on emotion and symptom analysis. This means provides dietary and lifestyle suggestions based on the symptoms reported by the user. For example, the device may suggest, "Try foods containing magnesium (e.g., nuts or dark chocolate) to reduce irritability."

[0362] Finally, the generated response and suggestion are provided to the user by the "means for providing a generated response to a user," so that the user can instantly receive the response and suggestion through the terminal.

[0363] To give a specific example, if a user sends a message on LINE saying, "I've been having really bad PMS lately...", the analysis engine will activate and generate a response such as, "To help ease the irritability caused by PMS, try foods rich in omega-3 fatty acids (such as salmon or almonds)," which will be provided to the user.

[0364] Here are some examples of prompts for generative AI models:

[0365] "Analyze your emotional state and symptoms and provide appropriate dietary suggestions based on them. User emotion: "Anxiety", Symptom: "Irritability" Suggestion: To reduce irritability, try foods rich in omega-3 fatty acids (e.g., salmon, almonds)."

[0366] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0367] Step 1:

[0368] The user uses the device to send a text message via a messaging platform such as LINE. This is the input to report the consultation details and symptoms to the system. The input data includes a message such as, "I've been suffering from severe headaches and irritability lately."

[0369] Step 2:

[0370] The device receives messages from the user using the LINE API. During this reception process, the message is temporarily stored and prepared for transfer to the server. The output data is the user's text message.

[0371] Step 3:

[0372] The server analyzes the received text message. It uses a sentiment analysis engine to extract keywords from the message and process the data to identify the user's emotional state and symptoms. The input is the user's text message, and the output is the user's emotional state (e.g., anxiety) and identified symptoms (e.g., headache, irritability).

[0373] Step 4:

[0374] The server stores the analysis results in a database. In this process, the analyzed emotional state and symptom data are added to the database along with the user's profile information. The input data are the analysis results and the user profile information, and the output is the analysis data stored in the database.

[0375] Step 5:

[0376] The server generates a personalized response based on the user's profile. Here, a generative AI model is used to generate prompts to create an automatic response based on the analysis results. The input is analysis data and profile information, and the output is the generated response. For example, a response to a user's message might be, "You've probably been feeling stressed lately. Why not try some relaxation techniques?"

[0377] Step 6:

[0378] The server generates health-related suggestions based on the emotion and symptom analysis results. A health-related suggestion engine operates and generates dietary and lifestyle suggestions according to the user's condition. The input is the emotion and symptom analysis data, and the output is the generated health-related suggestions. For example, a suggestion might be generated such as, "Try foods containing magnesium (e.g., nuts or dark chocolate) to reduce irritability."

[0379] Step 7:

[0380] The server provides the generated responses and suggestions to the user through the LINE API. In this process, the generated messages are sent to the user's device, where they are received and displayed. The input is the generated responses and health-related suggestions, and the output is the message displayed on the user's device.

[0381] Through these steps, users are provided with an environment where they can easily seek advice and receive appropriate advice and suggestions.

[0382] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0383] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0384] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0385] [Second embodiment]

[0386] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0387] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0388] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0389] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0390] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0391] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0392] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0393] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0394] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0395] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0396] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0397] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0398] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data, identifies the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life.

[0399] Specifically, this counseling service system consists of the following main functions:

[0400] 1. A means of receiving user input

[0401] It has the function of receiving data such as text messages and voice inputs sent by users through the messaging platform. When a user sends a message through LINE, the device receives the message and performs initial analysis to normalize the message.

[0402] 2. A means of analyzing received user input to identify emotions and symptoms.

[0403] The device sends the received message to a server, which then analyzes the message using a sentiment analysis algorithm and natural language processing model to identify the user's emotional state and symptoms, such as "headache" or "irritability."

[0404] 3. A means of storing the analysis results in a database

[0405] The server sends the analysis results to the database module, which stores the data in the database for further analysis and updating of the user's profile.

[0406] 4. A way to generate personalized responses based on a user's profile

[0407] The server retrieves the user's profile information from a database and uses a generative AI module to generate a personalized response based on the user's emotions and symptoms. For example, if a user sends a message saying, "I've been suffering from severe headaches and irritability lately," the system will generate a response such as, "You may be feeling stressed lately. Why don't you try some relaxation techniques?"

[0408] 5. How to provide generated responses to users

[0409] The server generates a response and sends it to the user's device via the LINE API, where the user receives and displays the message.

[0410] 6. A means of recording and visualizing user symptom information

[0411] The server stores the user's new symptom information in a database, which can later be used to visualize changes in symptoms. The recorded data can be displayed visually in graphs and charts, and can be used when reporting to medical institutions.

[0412] Specific examples

[0413] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the sequence of events is as follows:

[0414] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[0415] 2. Message analysis: The server passes the message to the chat analysis module, which performs sentiment analysis and keyword extraction to identify symptoms such as "headache" or "irritability."

[0416] 3. Result storage: The server stores the analysis results in a database and manages them together with the user's profile information.

[0417] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[0418] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[0419] 6. Information recording: The server stores the user's new symptom information in a database, which can be used later to visualize changes in symptoms.

[0420] This will provide users with an environment where they can easily seek advice, and it is expected that receiving appropriate advice will reduce anxiety and improve their quality of life.

[0421] The processing flow will be explained below.

[0422] Step 1:

[0423] A user opens the LINE app, types a message, and presses the send button, which sends the user's input through the LINE messaging platform.

[0424] Step 2:

[0425] The device receives a message from the user via the LINE API, performs an initial analysis of the message content, normalizes the message, and converts it into text format.

[0426] Step 3:

[0427] The device sends the normalized message to the server, which receives it and passes it to the analysis module.

[0428] Step 4:

[0429] The server uses an analysis module to analyze the received messages, which uses natural language processing and sentiment analysis algorithms to identify keywords and emotional states (e.g., "headache" or "irritation") within the messages.

[0430] Step 5:

[0431] The server sends the analysis results to a database module, which stores the analysis data in the database, including the user's emotional state and identified symptoms.

[0432] Step 6:

[0433] The server retrieves the user's profile information from a database, including past messaging history and health information.

[0434] Step 7:

[0435] Based on the profile information and analysis results, the server passes the data to a generative AI module to generate personalized responses for the user, such as specific advice or encouraging messages tailored to the user's situation.

[0436] Step 8:

[0437] The server generates a response and sends it to the user's device via the LINE API. The device receives the response message and displays it to the user.

[0438] Step 9:

[0439] The user reads the message and takes appropriate measures based on their situation and mood. If necessary, they can send another message to ask for further advice.

[0440] Step 10:

[0441] The server continuously monitors new messages and changes in the user's condition, and records symptom information in a database, making it possible to visualize the progress and improvement of the user's symptoms.

[0442] Through these steps, users are provided with an environment where they can easily seek advice, and it is expected that the quality of their lives will improve by receiving appropriate advice.

[0443] Example 1

[0444] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0445] Many women today face health problems such as premenstrual syndrome (PMS) and menopausal symptoms, but there are limited environments in which they can easily consult about these symptoms. As a result, there is a need for effective ways to deal with these symptoms. In addition, there is a lack of systems to analyze symptoms and provide appropriate advice, making it a challenge to reduce users' anxiety and improve their quality of life.

[0446] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0447] In this invention, the server includes a means for normalizing the user's input, a means for analyzing the normalized input to identify emotions and symptoms, and a means for storing the analysis results of the identified emotions and symptoms in a database, thereby enabling the server to analyze the user's health status and provide personalized advice.

[0448] "User input" is data such as text messages or voice inputs that a user sends through a messaging application.

[0449] "Normalization" is the process of converting received data into a format that is easier to parse, and includes removing unnecessary whitespace and special characters.

[0450] "Analysis" refers to the process of applying natural language processing and sentiment analysis algorithms to the received data to identify the user's emotional state and symptoms.

[0451] "Emotions and symptoms" refers to specific emotions and physical conditions identified in a user's message, such as "irritability" or "headache."

[0452] A "database" is a structured collection of data that stores analysis results and user profile information and makes them available for later use.

[0453] "Profile information" is data that includes information specific to each user, such as their history, health status, and past messages.

[0454] A "personalized response" is a response message that is individually generated based on the user's profile information and current emotions and symptoms.

[0455] A "messaging application" is a software platform for sending and receiving text and voice messages over the Internet.

[0456] A "server" is a computer system that receives user input, analyzes it, stores the results in a database, and generates and serves a response.

[0457] "User symptom information" means information about physical or mental illness reported by a User through a Message.

[0458] "Visualization" is the process of representing collected data in a visual format, such as a graph or chart, to make it easier to understand.

[0459] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data to identify the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life.

[0460] 1. A means of receiving user input

[0461] user:

[0462] A user opens a messaging application such as LINE, types a message such as "I've been suffering from severe headaches and irritability lately," and sends it. The input data is in text or voice format.

[0463] Device:

[0464] The device receives the user's message via the LINE API. After receiving it, the device performs initial analysis by normalizing the message and converting the data into a format that is easy to parse.

[0465] 2. A means of analyzing received user input to identify emotions and symptoms.

[0466] Device:

[0467] The terminal sends the received message to the server.

[0468] server:

[0469] The server receives the message and performs sentiment analysis and natural language processing (NLP) using libraries such as Python's Natural Language Toolkit (NLTK), TensorFlow, and Keras. As a result of the analysis, symptoms such as "headache" or "irritability" are identified.

[0470] 3. A means of storing the analysis results in a database

[0471] server:

[0472] The server stores the analysis results in a database such as MySQL or PostgreSQL, after which this data is associated with the user's profile information.

[0473] 4. A way to generate personalized responses based on a user's profile

[0474] server:

[0475] The server retrieves the user's profile information from a database. It then uses a generative AI module (e.g., GPT-3) to generate a personalized response based on the user's emotions and symptoms. For example, if a user sends a message saying, "I've been suffering from severe headaches and irritability lately," the server might generate a response such as, "You may be feeling stressed lately. Why don't you try some relaxation techniques?"

[0476] 5. How to provide generated responses to users

[0477] server:

[0478] The server generates a response and sends it to the user's device via the LINE API.

[0479] Device:

[0480] The device displays the response received through the LINE API to the user.

[0481] 6. A means of recording and visualizing user symptom information

[0482] server:

[0483] The server saves new symptom information in a database. Based on the data saved by the server, visual data is generated to visualize changes in symptoms in graphs and charts. This is done using libraries such as JavaScript's D3.js and Python's Matplotlib.

[0484] Specific examples

[0485] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the sequence of events is as follows:

[0486] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[0487] 2. Message analysis: The server passes the message to the chat analysis module, which performs sentiment analysis and keyword extraction to identify symptoms such as "headache" or "irritability."

[0488] 3. Result storage: The server stores the analysis results in a database and manages them together with the user's profile information.

[0489] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[0490] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[0491] 6. Information recording: The server stores the user's new symptom information in a database, which can be used later to visualize changes in symptoms.

[0492] This will provide users with an environment where they can easily seek advice, and it is expected that receiving appropriate advice will reduce anxiety and improve their quality of life.

[0493] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0494] Step 1: Receiving User Input

[0495] user:

[0496] The user opens LINE, types "I've been suffering from severe headaches and irritability lately," and presses the send button. In this process, the user sends a message describing their symptoms and feelings.

[0497] Device:

[0498] The device receives the user's message via the LINE API. The received message data is in text format, and an initial analysis is performed on the device. Specifically, normalization processing is performed, such as removing unnecessary spaces and normalizing special characters. The input for this processing is the user's text message, and the output is the normalized text data.

[0499] Step 2: Message analysis

[0500] Device:

[0501] The terminal sends the normalized message data to the server, which receives it and prepares it for analysis. The input is normalized text data.

[0502] server:

[0503] The server performs sentiment analysis and natural language processing (NLP) using libraries such as Python's Natural Language Toolkit (NLTK), TensorFlow, and Keras. The analysis step involves extracting keywords from the text and tagging it with emotions, ultimately identifying symptoms such as "headache" or "irritability." The input is the received text data, and the output is the analysis results (identified symptoms and emotions).

[0504] Step 3: Save the analysis results

[0505] server:

[0506] The server sends the analyzed results to a database and stores them. Specifically, a database management system such as MySQL or PostgreSQL is used. In this storage process, the analysis results are associated with each user's profile information and stored. The input is the analysis results, and the output is the status of storage in the database.

[0507] Step 4: Generate a response

[0508] server:

[0509] The server retrieves the user's profile information from a database. Next, it uses a generative AI module such as OpenAI's GPT-3 to generate a response. For example, if the user's symptoms involve "stress," it generates a response such as, "You may have been feeling stressed lately. Why don't you try some ways to relax?" The input is the user's profile information and the analysis results, and the output is the generated response message.

[0510] Step 5: Providing generated responses

[0511] server:

[0512] The server generates a response and sends it to the user's device via the LINE API.

[0513] Device:

[0514] The device displays the reply message received via the LINE API to the user. The input is the generated reply message, and the output is the message displayed on the user's device.

[0515] Step 6: Record and visualize information

[0516] server:

[0517] The server saves new symptom information in a database. The saved symptom information is managed as time-series data, and graphs and charts are generated later to visualize changes in symptoms. Specifically, visual data is created using libraries such as JavaScript's D3.js and Python's Matplotlib. The input is new symptom information, and the output is visualized data.

[0518] In this way, users can easily consult with a specialist and receive appropriate advice, which is expected to reduce anxiety and improve their quality of life.

[0519] (Application example 1)

[0520] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0521] There is a need for a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. However, existing systems make it difficult for users to immediately receive locally appropriate counseling, and there is a lack of means to make it easy to use in physical stores in particular. This makes it difficult for users to receive professional advice, and there are issues with improving their quality of life.

[0522] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0523] In this invention, the server includes means for receiving user input, means for analyzing the received user input and identifying emotions and symptoms, means for storing the analysis results in a database, means for generating a personalized response based on the user's profile, means for providing the generated response to the user, means for the user to access the service by scanning a QR code in a physical store, and means for providing locally appropriate counseling based on the scanned QR code. This allows users to easily use counseling services even in physical stores, improving their quality of life.

[0524] "User" refers to an individual who uses the System, and is particularly targeted at women who suffer from premenstrual syndrome (PMS) and menopausal symptoms.

[0525] "Input" refers to the text messages and voice data that users send through the messaging platform.

[0526] "Analysis" refers to the process of identifying emotions and symptoms based on input data received from a user.

[0527] "Database" refers to a collection of data for storing analysis results and user profile information.

[0528] "Profile" refers to a data set containing a user's individual information that is used to generate customized responses.

[0529] "Response" refers to advice or information generated based on the analysis results and provided to the user.

[0530] A "QR code" is a two-dimensional code that encodes specific information and can be scanned by devices such as smartphones.

[0531] "Brick and Mortar Store" means a sales or service establishment that a user can physically visit.

[0532] This invention is a system that allows users to access counseling services in physical stores by scanning a QR code, and is aimed at women suffering from premenstrual syndrome (PMS) and menopausal symptoms.

[0533] The server first receives user input, which is sent via a messaging platform as a text message or voice data. The received data is then analyzed to identify emotions and symptoms. This analysis is performed using natural language processing models and sentiment analysis algorithms. Specifically, the Hugging Face sentiment analysis model is used.

[0534] The analysis results are stored in a database, which not only stores the analysis results but also the user's profile information, which is important for later generating personalized responses.

[0535] Based on the user's profile, the server uses a generative AI model to generate personalized responses, which are then provided to the user via messaging platforms such as the LINE API. Additionally, when the user scans a QR code in a physical store, they are provided with counseling services tailored to their local area.

[0536] This system is implemented using a smartphone, a database server, a generative AI model, and a messaging platform (such as the LINE API). A specific example of how a user can use the counseling service is to scan a QR code displayed on a drugstore counter with their smartphone and enter, "I've been suffering from severe PMS symptoms lately." An example of a prompt based on this principle is shown below.

[0537] Prompt Sentence Examples

[0538] USER: I've been suffering from severe PMS symptoms lately.

[0539] BOT: It sounds like you're experiencing PMS symptoms. Why not try some relaxation techniques?

[0540] This allows users to easily use counseling services in physical stores, improving their quality of life.

[0541] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0542] Step 1:

[0543] User scans the QR code

[0544] When customers visit a physical store, they scan a QR code with their smartphone, which launches a dedicated counseling application and loads the customer's profile information.

[0545] Input: QR code data

[0546] Output: Counseling application launches, user profile loads

[0547] Step 2:

[0548] Receive user input

[0549] Users input text and voice data on their smartphones, including symptoms and emotional state.

[0550] Input: User text messages and voice data

[0551] Output: User input data

[0552] Step 3:

[0553] Analyzing the received data

[0554] The device sends the received input data to a server, which then analyzes the data using sentiment analysis algorithms and natural language processing models to identify symptoms such as "headache" or "irritability."

[0555] Input: User-entered data

[0556] Output: Symptom and emotion analysis results

[0557] Step 4:

[0558] Save the analysis results in a database

[0559] The server stores the analysis results in a database and simultaneously updates the user's profile information.

[0560] Input: Analysis results, existing user profile information

[0561] Output: Updated database

[0562] Step 5:

[0563] Generate personalized responses

[0564] The server uses a generative AI model to generate personalized responses based on the user's profile and analysis results.

[0565] Input: User profile information, analysis results

[0566] Output: Personalized reply (text message)

[0567] Step 6:

[0568] Providing a response to the user

[0569] The server sends the generated response to the user via the LINE API or other messaging platform, and the device displays the response.

[0570] Input: Generated response

[0571] Output: Display reply message on user terminal

[0572] Step 7:

[0573] Record additional information in the database

[0574] The server records the new symptom information in a database that can later be used to visualize changes in symptoms.

[0575] Input: New symptom information

[0576] Output: Updated database

[0577] Through a series of steps, users can easily receive counseling services at a physical store using their smartphone.

[0578] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0579] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data, identifies the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life.

[0580] Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions, allowing for a more accurate understanding of the user's emotional state.

[0581] Specifically, this counseling service system consists of the following main functions:

[0582] 1. A means of receiving user input

[0583] It has the function of receiving data such as text messages and voice inputs sent by users through the messaging platform. When a user sends a message through LINE, the device receives the message and performs initial analysis to normalize the message.

[0584] 2. A means of analyzing received user input to identify emotions and symptoms.

[0585] The device sends the received message to a server, which then analyzes the message using a sentiment analysis algorithm, natural language processing model, and emotion engine to identify the emotional state and symptoms, such as "headache" or "irritability."

[0586] 3. A means of storing the analysis results in a database

[0587] The server sends the analysis results to a database module, which stores the data, including the user's emotional state and identified symptoms.

[0588] 4. A way to generate personalized responses based on a user's profile

[0589] The server retrieves the user's profile information from a database and uses a generative AI module to generate a personalized response based on the user's emotions and symptoms. For example, if a user sends a message saying, "I've been suffering from severe headaches and irritability lately," the system will generate a response such as, "You may be feeling stressed lately. Why don't you try some relaxation techniques?"

[0590] 5. How to provide generated responses to users

[0591] The server generates a response and sends it to the user's device via the LINE API, where the user receives and displays the message.

[0592] 6. A means of recording and visualizing user symptom information

[0593] The server stores the user's new symptom information in a database, which can later be used to visualize changes in symptoms. The recorded data can be displayed visually in graphs and charts, and can be used when reporting to medical institutions.

[0594] 7. Emotion Recognition with Emotion Engine

[0595] The server uses an emotion engine to analyze the user's emotional state in real time from their message and obtain emotional data. The emotion engine analyzes the user's current emotional state and generates an optimal response based on the analysis results. For example, if the user's message contains feelings of anxiety or anger, a calm and comforting message will be generated.

[0596] Specific examples

[0597] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the sequence of events is as follows:

[0598] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[0599] 2. Message analysis: The server passes the message to the chat analysis module, which uses sentiment analysis, keyword extraction, and an emotion engine to identify symptoms such as "headache" or "irritation" and analyze the user's emotional state (e.g., anxiety, anger).

[0600] 3. Result storage: The server stores the analysis results in a database and manages them together with the user's profile information.

[0601] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[0602] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[0603] 6. Information recording: The server stores the user's new symptom information in a database, which can be used later to visualize changes in symptoms.

[0604] Through these steps, users are provided with an environment where they can easily seek advice, and by receiving appropriate advice, they can reduce their anxiety and improve their quality of life. In addition, accurate emotion analysis by the emotion engine allows for more appropriate responses.

[0605] The processing flow will be explained below.

[0606] Step 1:

[0607] A user opens the LINE app, types a message, and presses the send button, which sends the user's input via the LINE messaging platform.

[0608] Step 2:

[0609] The device receives a message from the user via the LINE API. The device initially parses the message content and normalizes it into text format.

[0610] Step 3:

[0611] The device sends the normalized message to the server, which receives it and passes the data to the analysis module.

[0612] Step 4:

[0613] The server uses an analysis module to analyze the received messages, which uses natural language processing, sentiment analysis, and an emotion engine to identify keywords and emotional states (e.g., "headache" or "irritation") in the messages.

[0614] Step 5:

[0615] The server sends the analysis results to a database module, which stores the analysis data in the database, including information on the user's emotional state and identified symptoms.

[0616] Step 6:

[0617] The server retrieves the user's profile information from a database, including past messaging history and health information.

[0618] Step 7:

[0619] Based on the profile information and analysis results, the server passes the data to a generative AI module to generate personalized responses for the user, such as specific advice or encouraging messages tailored to the user's situation.

[0620] Step 8:

[0621] The server generates a response and sends it to the user's device via the LINE API. The device receives the response message and displays it to the user.

[0622] Step 9:

[0623] The user reads the message and takes appropriate measures based on their situation and mood. If necessary, they can send another message to ask for further advice.

[0624] Step 10:

[0625] The server continuously monitors new messages and changes in the user's condition, and records symptom information in a database, making it possible to visualize the progress and improvement of the user's symptoms.

[0626] Step 11:

[0627] The server uses an emotion engine to analyze the user's emotional state in real time from their message. The emotion engine identifies the user's current emotional state (e.g., anxiety, anger) and generates an optimal response based on the analysis results.

[0628] Step 12:

[0629] The server stores the analysis results of the emotion engine in a database and manages the user's emotion history, which is used for subsequent analysis and feedback to the user.

[0630] Example 2

[0631] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0632] The purpose of this invention is to provide a counseling service that women suffering from premenstrual syndrome (PMS) and menopausal symptoms can easily consult. Conventional counseling methods have been insufficient in analyzing symptoms and emotions, and have difficulty providing personalized advice. For this reason, there is a need for a system that can effectively alleviate users' anxiety and improve their quality of life.

[0633] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a user's input, a means for analyzing the received user's input and identifying emotions and symptoms, a means for saving the analysis result in an information store, a means for generating an individual response based on the user's attribute information, and a means for providing the generated response to the user. This makes it possible to accurately analyze the user's emotions and symptoms and provide appropriate advice.

[0634] "User Input" refers to data, such as text messages or voice inputs, that a User sends through a messaging platform.

[0635] "Means for receiving" refers to the functionality of receiving user input via the message sending platform and performing initial analysis to process the data.

[0636] "Means for analyzing" refers to the ability to analyze received user input data and utilize sentiment analysis algorithms and natural language processing models to identify emotions and symptoms.

[0637] "Information store" refers to a memory device such as a database or cloud storage where the server stores analysis results.

[0638] "Attribute information" refers to data that includes a user's profile information, and refers to information for each user that is managed individually.

[0639] "Means for generating individual responses" refers to a function that uses generative AI to generate personalized responses appropriate for the user based on the analysis results and the user's attribute information.

[0640] "Means for providing to users" refers to the function of sending the generated response to the user's device through the message sending platform, allowing the user to receive and check the message.

[0641] A "messaging platform" is a platform that allows users and systems to communicate with each other, such as LINE or SMS.

[0642] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data, identifies the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life. Furthermore, the invention incorporates an emotion engine that recognizes the user's emotions, enabling a more accurate understanding of the user's emotional state.

[0643] The system consists of the following main functions:

[0644] 1. A means of receiving user input

[0645] The device receives data such as text messages and voice input sent by the user through the messaging platform. For example, if a user sends a message via LINE such as "I've been suffering from severe headaches and irritability lately," the device receives the message through the LINE API. After receiving the data, it normalizes it and passes it to the next processing step.

[0646] 2. A means of analyzing received user input to identify emotions and symptoms.

[0647] The device sends the received message to the server, where it analyzes the message using a natural language processing engine and sentiment analysis algorithms. As a result of the analysis, keywords such as "headache" and "irritation" and the user's emotional state (e.g., anxiety, anger) are identified.

[0648] 3. A means of storing the analysis results in an information store

[0649] The server stores the analysis results in a database, which includes the user's emotional state and identified symptoms, and is managed for each user.

[0650] 4. A way to generate personalized responses based on user demographic information

[0651] The server retrieves the user's attribute information from the database and combines it with the analysis results. Generative AI is used to generate the optimal response for the user. For example, it might generate a response along the lines of, "You've probably been feeling stressed lately. Why not try some ways to relax?"

[0652] 5. How to provide generated responses to users

[0653] The server generates a response and sends it to the user's device via the LINE API, where the user can receive and view the message.

[0654] 6. A means of recording and visualizing user symptom information

[0655] The server records the user's new symptom information in a database, which is later used to visualize changes in symptoms. The recorded data is displayed visually in graphs and charts and can be used for reporting to medical institutions.

[0656] 7. Emotion Recognition with Emotion Engine

[0657] The server uses an emotion engine to analyze the user's emotional state in real time from their message and generates an optimal response based on the analysis results. For example, if the user's message contains feelings of anxiety or anger, it will generate a calm and comforting message in response.

[0658] Specific examples

[0659] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the series of processes is as follows:

[0660] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[0661] 2. Message analysis: The server passes the message to the chat analysis module, which uses sentiment analysis, keyword extraction, and an emotion engine to identify symptoms such as "headache" or "irritation" and analyze the user's emotional state (e.g., anxiety, anger).

[0662] 3. Result storage: The server stores the analysis results in an information store and manages them together with the user's profile information.

[0663] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[0664] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[0665] 6. Information recording: The server saves the user's new symptom information in an information store, which can be used later to visualize changes in symptoms.

[0666] Example prompts for generative AI models

[0667] An example of a prompt sentence might be:

[0668] "If a user sends a message saying, 'I've been having a lot of headaches and irritability lately,' analyze the user's emotional state and symptoms and generate an appropriate response."

[0669] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0670] Step 1:

[0671] A user sends a text message through a messaging platform. For example, the user opens the LINE app, types "I've been suffering from severe headaches and irritability lately," and presses the send button.

[0672] Input: A text message from the user.

[0673] Output: Raw text data received via the LINE API.

[0674] Specific behavior: The user types and sends a text message. The device receives this message via the LINE API.

[0675] Step 2:

[0676] Normalizes message data received by the terminal, converting input data into a unified format and preparing it for analysis.

[0677] Input: The received raw text data.

[0678] Output: Normalized text data.

[0679] Specific operation: Preprocesses the text to remove unnecessary spaces and special characters, and converts it into normalized text data.

[0680] Step 3:

[0681] The normalized message data is sent to the server.

[0682] Input: Normalized text data.

[0683] Output: The text data sent to the server.

[0684] Specific operation: The device sends the normalized data to the server via the network.

[0685] Step 4:

[0686] The server analyzes the received message data and uses sentiment analysis algorithms, natural language processing engines, and emotion engines to identify emotional states and symptoms.

[0687] Input: Normalized text data sent to the server.

[0688] Output: Analysis results showing identified emotional states and symptoms.

[0689] How it works: The text analysis engine extracts emotion and symptom keywords from the message, and the emotion engine determines the user's emotional state. For example, it identifies the keywords "headache" and "irritation" and determines the emotional state as "anxiety."

[0690] Step 5:

[0691] The analysis results are saved in a database.

[0692] Input: Emotional state and symptom data obtained as a result of the analysis.

[0693] Output: Analysis result data stored in a database.

[0694] Specific operation: The server adds emotional state and symptom data as records to the database.

[0695] Step 6:

[0696] Generate personalized responses based on user demographic information.

[0697] Input: User attribute information and analysis result data stored in the database.

[0698] Output: The generated reply message.

[0699] How it works: The server retrieves the user's attribute information from a database and uses generative AI to generate an appropriate response, such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?"

[0700] Step 7:

[0701] The generated response is provided to the user.

[0702] Input: The generated reply message.

[0703] Output: The reply message received by the user.

[0704] Specific operation: The server generates a response and sends it to the user's device via the LINE API. The user receives the message in the LINE app and displays it.

[0705] Step 8:

[0706] Record and visualize user symptom information.

[0707] Input: New symptom information.

[0708] Output: Visualized symptom information (graphs and charts).

[0709] What it does: The server stores new symptom information in a database and generates graphs and charts based on this data, allowing users and medical institutions to visually monitor changes in symptoms.

[0710] (Application example 2)

[0711] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0712] Until now, women suffering from premenstrual syndrome (PMS) and menopausal symptoms have had limited access to casual consultation services, making it difficult for them to receive appropriate counseling and advice. Furthermore, there has been no system that can provide appropriate dietary recommendations for health based on emotions and symptoms. Therefore, there is a need for a system that can analyze users' emotional state and symptoms in real time and provide personalized support.

[0713] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user input, means for analyzing the received user input and identifying emotions and symptoms, and means for storing the analysis results in a database. This makes it possible to generate health-related suggestions based on the analysis results of emotions and symptoms.

[0714] "Means for receiving user input" refers to a function for receiving text messages and voice data entered by the user.

[0715] "Means for analyzing received user input and identifying emotions and symptoms" refers to a function for analyzing received messages and identifying the user's emotional state or specific symptoms.

[0716] The "means for saving the analysis results in a database" is a function for saving the analyzed emotional state and symptom data in a database.

[0717] "Means for generating personalized responses based on a user's profile" refers to a function for generating individually appropriate responses using the user's profile information.

[0718] The "means for providing the generated response to the user" is a function for providing the generated response to the user's terminal.

[0719] The "means for generating health-related suggestions based on the results of the emotion and symptom analysis" is a function for making health-related suggestions to the user based on the analyzed emotional state and symptoms.

[0720] This invention is a counseling service system that allows users to easily receive health consultations, and is designed especially for women suffering from premenstrual syndrome (PMS) and menopausal symptoms. The system has the function of making health-related suggestions based on the analysis of emotions and symptoms.

[0721] First, the system receives text messages and voice data from users through a "means for receiving user input." Users access the system using devices such as smartphones and computers via messaging platforms such as LINE.

[0722] The received message is then analyzed by "means for analyzing received user input to identify emotions and symptoms," using a sentiment analysis engine and / or natural language processing models. This process identifies the user's emotional state (e.g., anxiety, anger) and symptoms (e.g., headache, irritability).

[0723] The analysis results are stored in a database by the "means for storing analysis results in a database." This database accumulates the user's emotional state and symptom history.

[0724] The analyzed data is then used to generate a personalized response based on the user's profile. This response is generated automatically using a generative AI model. For example, if a user posts, "I've been having terrible headaches and irritability lately," the system might generate a response such as, "You're probably feeling stressed lately. Why don't you try some relaxation techniques?"

[0725] Additionally, the device includes a "means for generating health-related suggestions based on emotion and symptom analysis," which provides health-related suggestions based on emotion and symptom analysis. This means provides dietary and lifestyle suggestions based on the symptoms reported by the user. For example, the device may suggest, "Try foods containing magnesium (e.g., nuts or dark chocolate) to reduce irritability."

[0726] Finally, the generated response and suggestion are provided to the user by the "means for providing a generated response to a user," so that the user can instantly receive the response and suggestion through the terminal.

[0727] To give a specific example, if a user sends a message on LINE saying, "I've been having really bad PMS lately...", the analysis engine will activate and generate a response such as, "To help ease the irritability caused by PMS, try foods rich in omega-3 fatty acids (such as salmon or almonds)," which will be provided to the user.

[0728] Here are some examples of prompts for generative AI models:

[0729] "Analyze your emotional state and symptoms and provide appropriate dietary suggestions based on them. User emotion: "Anxiety", Symptom: "Irritability" Suggestion: To reduce irritability, try foods rich in omega-3 fatty acids (e.g., salmon, almonds)."

[0730] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0731] Step 1:

[0732] The user uses the device to send a text message via a messaging platform such as LINE. This is the input to report the consultation details and symptoms to the system. The input data includes a message such as, "I've been suffering from severe headaches and irritability lately."

[0733] Step 2:

[0734] The device receives messages from the user using the LINE API. During this reception process, the message is temporarily stored and prepared for transfer to the server. The output data is the user's text message.

[0735] Step 3:

[0736] The server analyzes the received text message. It uses a sentiment analysis engine to extract keywords from the message and process the data to identify the user's emotional state and symptoms. The input is the user's text message, and the output is the user's emotional state (e.g., anxiety) and identified symptoms (e.g., headache, irritability).

[0737] Step 4:

[0738] The server stores the analysis results in a database. In this process, the analyzed emotional state and symptom data are added to the database along with the user's profile information. The input data are the analysis results and the user profile information, and the output is the analysis data stored in the database.

[0739] Step 5:

[0740] The server generates a personalized response based on the user's profile. Here, a generative AI model is used to generate prompts to create an automatic response based on the analysis results. The input is analysis data and profile information, and the output is the generated response. For example, a response to a user's message might be, "You've probably been feeling stressed lately. Why not try some relaxation techniques?"

[0741] Step 6:

[0742] The server generates health-related suggestions based on the emotion and symptom analysis results. A health-related suggestion engine operates and generates dietary and lifestyle suggestions according to the user's condition. The input is the emotion and symptom analysis data, and the output is the generated health-related suggestions. For example, a suggestion might be generated such as, "Try foods containing magnesium (e.g., nuts or dark chocolate) to reduce irritability."

[0743] Step 7:

[0744] The server provides the generated responses and suggestions to the user through the LINE API. In this process, the generated messages are sent to the user's device, where they are received and displayed. The input is the generated responses and health-related suggestions, and the output is the message displayed on the user's device.

[0745] Through these steps, users are provided with an environment where they can easily seek advice and receive appropriate advice and suggestions.

[0746] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0747] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0748] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0749] [Third embodiment]

[0750] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0751] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0752] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0753] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0754] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0755] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0756] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0757] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0758] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0759] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0760] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0761] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0762] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data, identifies the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life.

[0763] Specifically, this counseling service system consists of the following main functions:

[0764] 1. A means of receiving user input

[0765] It has the function of receiving data such as text messages and voice inputs sent by users through the messaging platform. When a user sends a message through LINE, the device receives the message and performs initial analysis to normalize the message.

[0766] 2. A means of analyzing received user input to identify emotions and symptoms.

[0767] The device sends the received message to a server, which then analyzes the message using a sentiment analysis algorithm and natural language processing model to identify the user's emotional state and symptoms, such as "headache" or "irritability."

[0768] 3. A means of storing the analysis results in a database

[0769] The server sends the analysis results to the database module, which stores the data in the database for further analysis and updating of the user's profile.

[0770] 4. A way to generate personalized responses based on a user's profile

[0771] The server retrieves the user's profile information from a database and uses a generative AI module to generate a personalized response based on the user's emotions and symptoms. For example, if a user sends a message saying, "I've been suffering from severe headaches and irritability lately," the system will generate a response such as, "You may be feeling stressed lately. Why don't you try some relaxation techniques?"

[0772] 5. How to provide generated responses to users

[0773] The server generates a response and sends it to the user's device via the LINE API, where the user receives and displays the message.

[0774] 6. A means of recording and visualizing user symptom information

[0775] The server stores the user's new symptom information in a database, which can later be used to visualize changes in symptoms. The recorded data can be displayed visually in graphs and charts, and can be used when reporting to medical institutions.

[0776] Specific examples

[0777] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the sequence of events is as follows:

[0778] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[0779] 2. Message analysis: The server passes the message to the chat analysis module, which performs sentiment analysis and keyword extraction to identify symptoms such as "headache" or "irritability."

[0780] 3. Result storage: The server stores the analysis results in a database and manages them together with the user's profile information.

[0781] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[0782] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[0783] 6. Information recording: The server stores the user's new symptom information in a database, which can be used later to visualize changes in symptoms.

[0784] This will provide users with an environment where they can easily seek advice, and it is expected that receiving appropriate advice will reduce anxiety and improve their quality of life.

[0785] The processing flow will be explained below.

[0786] Step 1:

[0787] A user opens the LINE app, types a message, and presses the send button, which sends the user's input through the LINE messaging platform.

[0788] Step 2:

[0789] The device receives a message from the user via the LINE API, performs an initial analysis of the message content, normalizes the message, and converts it into text format.

[0790] Step 3:

[0791] The device sends the normalized message to the server, which receives it and passes it to the analysis module.

[0792] Step 4:

[0793] The server uses an analysis module to analyze the received messages, which uses natural language processing and sentiment analysis algorithms to identify keywords and emotional states (e.g., "headache" or "irritation") within the messages.

[0794] Step 5:

[0795] The server sends the analysis results to a database module, which stores the analysis data in the database, including the user's emotional state and identified symptoms.

[0796] Step 6:

[0797] The server retrieves the user's profile information from a database, including past messaging history and health information.

[0798] Step 7:

[0799] Based on the profile information and analysis results, the server passes the data to a generative AI module to generate personalized responses for the user, such as specific advice or encouraging messages tailored to the user's situation.

[0800] Step 8:

[0801] The server generates a response and sends it to the user's device via the LINE API. The device receives the response message and displays it to the user.

[0802] Step 9:

[0803] The user reads the message and takes appropriate measures based on their situation and mood. If necessary, they can send another message to ask for further advice.

[0804] Step 10:

[0805] The server continuously monitors new messages and changes in the user's condition, and records symptom information in a database, making it possible to visualize the progress and improvement of the user's symptoms.

[0806] Through these steps, users are provided with an environment where they can easily seek advice, and it is expected that the quality of their lives will improve by receiving appropriate advice.

[0807] Example 1

[0808] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0809] Many women today face health problems such as premenstrual syndrome (PMS) and menopausal symptoms, but there are limited environments in which they can easily consult about these symptoms. As a result, there is a need for effective ways to deal with these symptoms. In addition, there is a lack of systems to analyze symptoms and provide appropriate advice, making it a challenge to reduce users' anxiety and improve their quality of life.

[0810] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0811] In this invention, the server includes a means for normalizing the user's input, a means for analyzing the normalized input to identify emotions and symptoms, and a means for storing the analysis results of the identified emotions and symptoms in a database, thereby enabling the server to analyze the user's health status and provide personalized advice.

[0812] "User input" is data such as text messages or voice inputs that a user sends through a messaging application.

[0813] "Normalization" is the process of converting received data into a format that is easier to parse, and includes removing unnecessary whitespace and special characters.

[0814] "Analysis" refers to the process of applying natural language processing and sentiment analysis algorithms to the received data to identify the user's emotional state and symptoms.

[0815] "Emotions and symptoms" refers to specific emotions and physical conditions identified in a user's message, such as "irritability" or "headache."

[0816] A "database" is a structured collection of data that stores analysis results and user profile information and makes them available for later use.

[0817] "Profile information" is data that includes information specific to each user, such as their history, health status, and past messages.

[0818] A "personalized response" is a response message that is individually generated based on the user's profile information and current emotions and symptoms.

[0819] A "messaging application" is a software platform for sending and receiving text and voice messages over the Internet.

[0820] A "server" is a computer system that receives user input, analyzes it, stores the results in a database, and generates and serves a response.

[0821] "User symptom information" means information about physical or mental illness reported by a User through a Message.

[0822] "Visualization" is the process of representing collected data in a visual format, such as a graph or chart, to make it easier to understand.

[0823] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data to identify the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life.

[0824] 1. A means of receiving user input

[0825] user:

[0826] A user opens a messaging application such as LINE, types a message such as "I've been suffering from severe headaches and irritability lately," and sends it. The input data is in text or voice format.

[0827] Device:

[0828] The device receives the user's message via the LINE API. After receiving it, the device performs initial analysis by normalizing the message and converting the data into a format that is easy to parse.

[0829] 2. A means of analyzing received user input to identify emotions and symptoms.

[0830] Device:

[0831] The terminal sends the received message to the server.

[0832] server:

[0833] The server receives the message and performs sentiment analysis and natural language processing (NLP) using libraries such as Python's Natural Language Toolkit (NLTK), TensorFlow, and Keras. As a result of the analysis, symptoms such as "headache" or "irritability" are identified.

[0834] 3. A means of storing the analysis results in a database

[0835] server:

[0836] The server stores the analysis results in a database such as MySQL or PostgreSQL, after which this data is associated with the user's profile information.

[0837] 4. A way to generate personalized responses based on a user's profile

[0838] server:

[0839] The server retrieves the user's profile information from a database. It then uses a generative AI module (e.g., GPT-3) to generate a personalized response based on the user's emotions and symptoms. For example, if a user sends a message saying, "I've been suffering from severe headaches and irritability lately," the server might generate a response such as, "You may be feeling stressed lately. Why don't you try some relaxation techniques?"

[0840] 5. How to provide generated responses to users

[0841] server:

[0842] The server generates a response and sends it to the user's device via the LINE API.

[0843] Device:

[0844] The device displays the response received through the LINE API to the user.

[0845] 6. A means of recording and visualizing user symptom information

[0846] server:

[0847] The server saves new symptom information in a database. Based on the data saved by the server, visual data is generated to visualize changes in symptoms in graphs and charts. This is done using libraries such as JavaScript's D3.js and Python's Matplotlib.

[0848] Specific examples

[0849] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the sequence of events is as follows:

[0850] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[0851] 2. Message analysis: The server passes the message to the chat analysis module, which performs sentiment analysis and keyword extraction to identify symptoms such as "headache" or "irritability."

[0852] 3. Result storage: The server stores the analysis results in a database and manages them together with the user's profile information.

[0853] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[0854] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[0855] 6. Information recording: The server stores the user's new symptom information in a database, which can be used later to visualize changes in symptoms.

[0856] This will provide users with an environment where they can easily seek advice, and it is expected that receiving appropriate advice will reduce anxiety and improve their quality of life.

[0857] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0858] Step 1: Receiving User Input

[0859] user:

[0860] The user opens LINE, types "I've been suffering from severe headaches and irritability lately," and presses the send button. In this process, the user sends a message describing their symptoms and feelings.

[0861] Device:

[0862] The device receives the user's message via the LINE API. The received message data is in text format, and an initial analysis is performed on the device. Specifically, normalization processing is performed, such as removing unnecessary spaces and normalizing special characters. The input for this processing is the user's text message, and the output is the normalized text data.

[0863] Step 2: Message analysis

[0864] Device:

[0865] The terminal sends the normalized message data to the server, which receives it and prepares it for analysis. The input is normalized text data.

[0866] server:

[0867] The server performs sentiment analysis and natural language processing (NLP) using libraries such as Python's Natural Language Toolkit (NLTK), TensorFlow, and Keras. The analysis step involves extracting keywords from the text and tagging it with emotions, ultimately identifying symptoms such as "headache" or "irritability." The input is the received text data, and the output is the analysis results (identified symptoms and emotions).

[0868] Step 3: Save the analysis results

[0869] server:

[0870] The server sends the analyzed results to a database and stores them. Specifically, a database management system such as MySQL or PostgreSQL is used. In this storage process, the analysis results are associated with each user's profile information and stored. The input is the analysis results, and the output is the status of storage in the database.

[0871] Step 4: Generate a response

[0872] server:

[0873] The server retrieves the user's profile information from a database. Next, it uses a generative AI module such as OpenAI's GPT-3 to generate a response. For example, if the user's symptoms involve "stress," it generates a response such as, "You may have been feeling stressed lately. Why don't you try some ways to relax?" The input is the user's profile information and the analysis results, and the output is the generated response message.

[0874] Step 5: Providing generated responses

[0875] server:

[0876] The server generates a response and sends it to the user's device via the LINE API.

[0877] Device:

[0878] The device displays the reply message received via the LINE API to the user. The input is the generated reply message, and the output is the message displayed on the user's device.

[0879] Step 6: Record and visualize information

[0880] server:

[0881] The server saves new symptom information in a database. The saved symptom information is managed as time-series data, and graphs and charts are generated later to visualize changes in symptoms. Specifically, visual data is created using libraries such as JavaScript's D3.js and Python's Matplotlib. The input is new symptom information, and the output is visualized data.

[0882] In this way, users can easily consult with a specialist and receive appropriate advice, which is expected to reduce anxiety and improve their quality of life.

[0883] (Application example 1)

[0884] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0885] There is a need for a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. However, existing systems make it difficult for users to immediately receive locally appropriate counseling, and there is a lack of means to make it easy to use in physical stores in particular. This makes it difficult for users to receive professional advice, and there are issues with improving their quality of life.

[0886] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0887] In this invention, the server includes means for receiving user input, means for analyzing the received user input and identifying emotions and symptoms, means for storing the analysis results in a database, means for generating a personalized response based on the user's profile, means for providing the generated response to the user, means for the user to access the service by scanning a QR code in a physical store, and means for providing locally appropriate counseling based on the scanned QR code. This allows users to easily use counseling services even in physical stores, improving their quality of life.

[0888] "User" refers to an individual who uses the System, and is particularly targeted at women who suffer from premenstrual syndrome (PMS) and menopausal symptoms.

[0889] "Input" refers to the text messages and voice data that users send through the messaging platform.

[0890] "Analysis" refers to the process of identifying emotions and symptoms based on input data received from a user.

[0891] "Database" refers to a collection of data for storing analysis results and user profile information.

[0892] "Profile" refers to a data set containing a user's individual information that is used to generate customized responses.

[0893] "Response" refers to advice or information generated based on the analysis results and provided to the user.

[0894] A "QR code" is a two-dimensional code that encodes specific information and can be scanned by devices such as smartphones.

[0895] "Brick and Mortar Store" means a sales or service establishment that a user can physically visit.

[0896] This invention is a system that allows users to access counseling services in physical stores by scanning a QR code, and is aimed at women suffering from premenstrual syndrome (PMS) and menopausal symptoms.

[0897] The server first receives user input, which is sent via a messaging platform as a text message or voice data. The received data is then analyzed to identify emotions and symptoms. This analysis is performed using natural language processing models and sentiment analysis algorithms. Specifically, the Hugging Face sentiment analysis model is used.

[0898] The analysis results are stored in a database, which not only stores the analysis results but also the user's profile information, which is important for later generating personalized responses.

[0899] Based on the user's profile, the server uses a generative AI model to generate personalized responses, which are then provided to the user via messaging platforms such as the LINE API. Additionally, when the user scans a QR code in a physical store, they are provided with counseling services tailored to their local area.

[0900] This system is implemented using a smartphone, a database server, a generative AI model, and a messaging platform (such as the LINE API). A specific example of how a user can use the counseling service is to scan a QR code displayed on a drugstore counter with their smartphone and enter, "I've been suffering from severe PMS symptoms lately." An example of a prompt based on this principle is shown below.

[0901] Prompt Sentence Examples

[0902] USER: I've been suffering from severe PMS symptoms lately.

[0903] BOT: It sounds like you're experiencing PMS symptoms. Why not try some relaxation techniques?

[0904] This allows users to easily use counseling services in physical stores, improving their quality of life.

[0905] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0906] Step 1:

[0907] User scans the QR code

[0908] When customers visit a physical store, they scan a QR code with their smartphone, which launches a dedicated counseling application and loads the customer's profile information.

[0909] Input: QR code data

[0910] Output: Counseling application launches, user profile loads

[0911] Step 2:

[0912] Receive user input

[0913] Users input text and voice data on their smartphones, including symptoms and emotional state.

[0914] Input: User text messages and voice data

[0915] Output: User input data

[0916] Step 3:

[0917] Analyzing the received data

[0918] The device sends the received input data to a server, which then analyzes the data using sentiment analysis algorithms and natural language processing models to identify symptoms such as "headache" or "irritability."

[0919] Input: User-entered data

[0920] Output: Symptom and emotion analysis results

[0921] Step 4:

[0922] Save the analysis results in a database

[0923] The server stores the analysis results in a database and simultaneously updates the user's profile information.

[0924] Input: Analysis results, existing user profile information

[0925] Output: Updated database

[0926] Step 5:

[0927] Generate personalized responses

[0928] The server uses a generative AI model to generate personalized responses based on the user's profile and analysis results.

[0929] Input: User profile information, analysis results

[0930] Output: Personalized reply (text message)

[0931] Step 6:

[0932] Providing a response to the user

[0933] The server sends the generated response to the user via the LINE API or other messaging platform, and the device displays the response.

[0934] Input: Generated response

[0935] Output: Display reply message on user terminal

[0936] Step 7:

[0937] Record additional information in the database

[0938] The server records the new symptom information in a database that can later be used to visualize changes in symptoms.

[0939] Input: New symptom information

[0940] Output: Updated database

[0941] Through a series of steps, users can easily receive counseling services at a physical store using their smartphone.

[0942] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0943] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data, identifies the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life.

[0944] Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions, allowing for a more accurate understanding of the user's emotional state.

[0945] Specifically, this counseling service system consists of the following main functions:

[0946] 1. A means of receiving user input

[0947] It has the function of receiving data such as text messages and voice inputs sent by users through the messaging platform. When a user sends a message through LINE, the device receives the message and performs initial analysis to normalize the message.

[0948] 2. A means of analyzing received user input to identify emotions and symptoms.

[0949] The device sends the received message to a server, which then analyzes the message using a sentiment analysis algorithm, natural language processing model, and emotion engine to identify the emotional state and symptoms, such as "headache" or "irritability."

[0950] 3. A means of storing the analysis results in a database

[0951] The server sends the analysis results to a database module, which stores the data, including the user's emotional state and identified symptoms.

[0952] 4. A way to generate personalized responses based on a user's profile

[0953] The server retrieves the user's profile information from a database and uses a generative AI module to generate a personalized response based on the user's emotions and symptoms. For example, if a user sends a message saying, "I've been suffering from severe headaches and irritability lately," the system will generate a response such as, "You may be feeling stressed lately. Why don't you try some relaxation techniques?"

[0954] 5. How to provide generated responses to users

[0955] The server generates a response and sends it to the user's device via the LINE API, where the user receives and displays the message.

[0956] 6. A means of recording and visualizing user symptom information

[0957] The server stores the user's new symptom information in a database, which can later be used to visualize changes in symptoms. The recorded data can be displayed visually in graphs and charts, and can be used when reporting to medical institutions.

[0958] 7. Emotion Recognition with Emotion Engine

[0959] The server uses an emotion engine to analyze the user's emotional state in real time from their message and obtain emotional data. The emotion engine analyzes the user's current emotional state and generates an optimal response based on the analysis results. For example, if the user's message contains feelings of anxiety or anger, a calm and comforting message will be generated.

[0960] Specific examples

[0961] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the sequence of events is as follows:

[0962] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[0963] 2. Message analysis: The server passes the message to the chat analysis module, which uses sentiment analysis, keyword extraction, and an emotion engine to identify symptoms such as "headache" or "irritation" and analyze the user's emotional state (e.g., anxiety, anger).

[0964] 3. Result storage: The server stores the analysis results in a database and manages them together with the user's profile information.

[0965] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[0966] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[0967] 6. Information recording: The server stores the user's new symptom information in a database, which can be used later to visualize changes in symptoms.

[0968] Through these steps, users are provided with an environment where they can easily seek advice, and by receiving appropriate advice, they can reduce their anxiety and improve their quality of life. In addition, accurate emotion analysis by the emotion engine allows for more appropriate responses.

[0969] The processing flow will be explained below.

[0970] Step 1:

[0971] A user opens the LINE app, types a message, and presses the send button, which sends the user's input via the LINE messaging platform.

[0972] Step 2:

[0973] The device receives a message from the user via the LINE API. The device initially parses the message content and normalizes it into text format.

[0974] Step 3:

[0975] The device sends the normalized message to the server, which receives it and passes the data to the analysis module.

[0976] Step 4:

[0977] The server uses an analysis module to analyze the received messages, which uses natural language processing, sentiment analysis, and an emotion engine to identify keywords and emotional states (e.g., "headache" or "irritation") in the messages.

[0978] Step 5:

[0979] The server sends the analysis results to a database module, which stores the analysis data in the database, including information on the user's emotional state and identified symptoms.

[0980] Step 6:

[0981] The server retrieves the user's profile information from a database, including past messaging history and health information.

[0982] Step 7:

[0983] Based on the profile information and analysis results, the server passes the data to a generative AI module to generate personalized responses for the user, such as specific advice or encouraging messages tailored to the user's situation.

[0984] Step 8:

[0985] The server generates a response and sends it to the user's device via the LINE API. The device receives the response message and displays it to the user.

[0986] Step 9:

[0987] The user reads the message and takes appropriate measures based on their situation and mood. If necessary, they can send another message to ask for further advice.

[0988] Step 10:

[0989] The server continuously monitors new messages and changes in the user's condition, and records symptom information in a database, making it possible to visualize the progress and improvement of the user's symptoms.

[0990] Step 11:

[0991] The server uses an emotion engine to analyze the user's emotional state in real time from their message. The emotion engine identifies the user's current emotional state (e.g., anxiety, anger) and generates an optimal response based on the analysis results.

[0992] Step 12:

[0993] The server stores the analysis results of the emotion engine in a database and manages the user's emotion history, which is used for subsequent analysis and feedback to the user.

[0994] Example 2

[0995] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0996] The purpose of this invention is to provide a counseling service that women suffering from premenstrual syndrome (PMS) and menopausal symptoms can easily consult. Conventional counseling methods have been insufficient in analyzing symptoms and emotions, and have difficulty providing personalized advice. For this reason, there is a need for a system that can effectively alleviate users' anxiety and improve their quality of life.

[0997] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a user's input, a means for analyzing the received user's input and identifying emotions and symptoms, a means for saving the analysis result in an information store, a means for generating an individual response based on the user's attribute information, and a means for providing the generated response to the user. This makes it possible to accurately analyze the user's emotions and symptoms and provide appropriate advice.

[0998] "User Input" refers to data, such as text messages or voice inputs, that a User sends through a messaging platform.

[0999] "Means for receiving" refers to the functionality of receiving user input via the message sending platform and performing initial analysis to process the data.

[1000] "Means for analyzing" refers to the ability to analyze received user input data and utilize sentiment analysis algorithms and natural language processing models to identify emotions and symptoms.

[1001] "Information store" refers to a memory device such as a database or cloud storage where the server stores analysis results.

[1002] "Attribute information" refers to data that includes a user's profile information, and refers to information for each user that is managed individually.

[1003] "Means for generating individual responses" refers to a function that uses generative AI to generate personalized responses appropriate for the user based on the analysis results and the user's attribute information.

[1004] "Means for providing to users" refers to the function of sending the generated response to the user's device through the message sending platform, allowing the user to receive and check the message.

[1005] A "messaging platform" is a platform that allows users and systems to communicate with each other, such as LINE or SMS.

[1006] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data, identifies the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life. Furthermore, the invention incorporates an emotion engine that recognizes the user's emotions, enabling a more accurate understanding of the user's emotional state.

[1007] The system consists of the following main functions:

[1008] 1. A means of receiving user input

[1009] The device receives data such as text messages and voice input sent by the user through the messaging platform. For example, if a user sends a message via LINE such as "I've been suffering from severe headaches and irritability lately," the device receives the message through the LINE API. After receiving the data, it normalizes it and passes it to the next processing step.

[1010] 2. A means of analyzing received user input to identify emotions and symptoms.

[1011] The device sends the received message to the server, where it analyzes the message using a natural language processing engine and sentiment analysis algorithms. As a result of the analysis, keywords such as "headache" and "irritation" and the user's emotional state (e.g., anxiety, anger) are identified.

[1012] 3. A means of storing the analysis results in an information store

[1013] The server stores the analysis results in a database, which includes the user's emotional state and identified symptoms, and is managed for each user.

[1014] 4. A way to generate personalized responses based on user demographic information

[1015] The server retrieves the user's attribute information from the database and combines it with the analysis results. Generative AI is used to generate the optimal response for the user. For example, it might generate a response along the lines of, "You've probably been feeling stressed lately. Why not try some ways to relax?"

[1016] 5. How to provide generated responses to users

[1017] The server generates a response and sends it to the user's device via the LINE API, where the user can receive and view the message.

[1018] 6. A means of recording and visualizing user symptom information

[1019] The server records the user's new symptom information in a database, which is later used to visualize changes in symptoms. The recorded data is displayed visually in graphs and charts and can be used for reporting to medical institutions.

[1020] 7. Emotion Recognition with Emotion Engine

[1021] The server uses an emotion engine to analyze the user's emotional state in real time from their message and generates an optimal response based on the analysis results. For example, if the user's message contains feelings of anxiety or anger, it will generate a calm and comforting message in response.

[1022] Specific examples

[1023] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the series of processes is as follows:

[1024] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[1025] 2. Message analysis: The server passes the message to the chat analysis module, which uses sentiment analysis, keyword extraction, and an emotion engine to identify symptoms such as "headache" or "irritation" and analyze the user's emotional state (e.g., anxiety, anger).

[1026] 3. Result storage: The server stores the analysis results in an information store and manages them together with the user's profile information.

[1027] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[1028] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[1029] 6. Information recording: The server saves the user's new symptom information in an information store, which can be used later to visualize changes in symptoms.

[1030] Example prompts for generative AI models

[1031] An example of a prompt sentence might be:

[1032] "If a user sends a message saying, 'I've been having a lot of headaches and irritability lately,' analyze the user's emotional state and symptoms and generate an appropriate response."

[1033] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1034] Step 1:

[1035] A user sends a text message through a messaging platform. For example, the user opens the LINE app, types "I've been suffering from severe headaches and irritability lately," and presses the send button.

[1036] Input: A text message from the user.

[1037] Output: Raw text data received via the LINE API.

[1038] Specific behavior: The user types and sends a text message. The device receives this message via the LINE API.

[1039] Step 2:

[1040] Normalizes message data received by the terminal, converting input data into a unified format and preparing it for analysis.

[1041] Input: The received raw text data.

[1042] Output: Normalized text data.

[1043] Specific operation: Preprocesses the text to remove unnecessary spaces and special characters, and converts it into normalized text data.

[1044] Step 3:

[1045] The normalized message data is sent to the server.

[1046] Input: Normalized text data.

[1047] Output: The text data sent to the server.

[1048] Specific operation: The device sends the normalized data to the server via the network.

[1049] Step 4:

[1050] The server analyzes the received message data and uses sentiment analysis algorithms, natural language processing engines, and emotion engines to identify emotional states and symptoms.

[1051] Input: Normalized text data sent to the server.

[1052] Output: Analysis results showing identified emotional states and symptoms.

[1053] How it works: The text analysis engine extracts emotion and symptom keywords from the message, and the emotion engine determines the user's emotional state. For example, it identifies the keywords "headache" and "irritation" and determines the emotional state as "anxiety."

[1054] Step 5:

[1055] The analysis results are saved in a database.

[1056] Input: Emotional state and symptom data obtained as a result of the analysis.

[1057] Output: Analysis result data stored in a database.

[1058] Specific operation: The server adds emotional state and symptom data as records to the database.

[1059] Step 6:

[1060] Generate personalized responses based on user demographic information.

[1061] Input: User attribute information and analysis result data stored in the database.

[1062] Output: The generated reply message.

[1063] How it works: The server retrieves the user's attribute information from a database and uses generative AI to generate an appropriate response, such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?"

[1064] Step 7:

[1065] The generated response is provided to the user.

[1066] Input: The generated reply message.

[1067] Output: The reply message received by the user.

[1068] Specific operation: The server generates a response and sends it to the user's device via the LINE API. The user receives the message in the LINE app and displays it.

[1069] Step 8:

[1070] Record and visualize user symptom information.

[1071] Input: New symptom information.

[1072] Output: Visualized symptom information (graphs and charts).

[1073] What it does: The server stores new symptom information in a database and generates graphs and charts based on this data, allowing users and medical institutions to visually monitor changes in symptoms.

[1074] (Application example 2)

[1075] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1076] Until now, women suffering from premenstrual syndrome (PMS) and menopausal symptoms have had limited access to casual consultation services, making it difficult for them to receive appropriate counseling and advice. Furthermore, there has been no system that can provide appropriate dietary recommendations for health based on emotions and symptoms. Therefore, there is a need for a system that can analyze users' emotional state and symptoms in real time and provide personalized support.

[1077] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user input, means for analyzing the received user input and identifying emotions and symptoms, and means for storing the analysis results in a database. This makes it possible to generate health-related suggestions based on the analysis results of emotions and symptoms.

[1078] "Means for receiving user input" refers to a function for receiving text messages and voice data entered by the user.

[1079] "Means for analyzing received user input and identifying emotions and symptoms" refers to a function for analyzing received messages and identifying the user's emotional state or specific symptoms.

[1080] The "means for saving the analysis results in a database" is a function for saving the analyzed emotional state and symptom data in a database.

[1081] "Means for generating personalized responses based on a user's profile" refers to a function for generating individually appropriate responses using the user's profile information.

[1082] The "means for providing the generated response to the user" is a function for providing the generated response to the user's terminal.

[1083] The "means for generating health-related suggestions based on the results of the emotion and symptom analysis" is a function for making health-related suggestions to the user based on the analyzed emotional state and symptoms.

[1084] This invention is a counseling service system that allows users to easily receive health consultations, and is designed especially for women suffering from premenstrual syndrome (PMS) and menopausal symptoms. The system has the function of making health-related suggestions based on the analysis of emotions and symptoms.

[1085] First, the system receives text messages and voice data from users through a "means for receiving user input." Users access the system using devices such as smartphones and computers via messaging platforms such as LINE.

[1086] The received message is then analyzed by "means for analyzing received user input to identify emotions and symptoms," using a sentiment analysis engine and / or natural language processing models. This process identifies the user's emotional state (e.g., anxiety, anger) and symptoms (e.g., headache, irritability).

[1087] The analysis results are stored in a database by the "means for storing analysis results in a database." This database accumulates the user's emotional state and symptom history.

[1088] The analyzed data is then used to generate a personalized response based on the user's profile. This response is generated automatically using a generative AI model. For example, if a user posts, "I've been having terrible headaches and irritability lately," the system might generate a response such as, "You're probably feeling stressed lately. Why don't you try some relaxation techniques?"

[1089] Additionally, the device includes a "means for generating health-related suggestions based on emotion and symptom analysis," which provides health-related suggestions based on emotion and symptom analysis. This means provides dietary and lifestyle suggestions based on the symptoms reported by the user. For example, the device may suggest, "Try foods containing magnesium (e.g., nuts or dark chocolate) to reduce irritability."

[1090] Finally, the generated response and suggestion are provided to the user by the "means for providing a generated response to a user," so that the user can instantly receive the response and suggestion through the terminal.

[1091] To give a specific example, if a user sends a message on LINE saying, "I've been having really bad PMS lately...", the analysis engine will activate and generate a response such as, "To help ease the irritability caused by PMS, try foods rich in omega-3 fatty acids (such as salmon or almonds)," which will be provided to the user.

[1092] Here are some examples of prompts for generative AI models:

[1093] "Analyze your emotional state and symptoms and provide appropriate dietary suggestions based on them. User emotion: "Anxiety", Symptom: "Irritability" Suggestion: To reduce irritability, try foods rich in omega-3 fatty acids (e.g., salmon, almonds)."

[1094] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1095] Step 1:

[1096] The user uses the device to send a text message via a messaging platform such as LINE. This is the input to report the consultation details and symptoms to the system. The input data includes a message such as, "I've been suffering from severe headaches and irritability lately."

[1097] Step 2:

[1098] The device receives messages from the user using the LINE API. During this reception process, the message is temporarily stored and prepared for transfer to the server. The output data is the user's text message.

[1099] Step 3:

[1100] The server analyzes the received text message. It uses a sentiment analysis engine to extract keywords from the message and process the data to identify the user's emotional state and symptoms. The input is the user's text message, and the output is the user's emotional state (e.g., anxiety) and identified symptoms (e.g., headache, irritability).

[1101] Step 4:

[1102] The server stores the analysis results in a database. In this process, the analyzed emotional state and symptom data are added to the database along with the user's profile information. The input data are the analysis results and the user profile information, and the output is the analysis data stored in the database.

[1103] Step 5:

[1104] The server generates a personalized response based on the user's profile. Here, a generative AI model is used to generate prompts to create an automatic response based on the analysis results. The input is analysis data and profile information, and the output is the generated response. For example, a response to a user's message might be, "You've probably been feeling stressed lately. Why not try some relaxation techniques?"

[1105] Step 6:

[1106] The server generates health-related suggestions based on the emotion and symptom analysis results. A health-related suggestion engine operates and generates dietary and lifestyle suggestions according to the user's condition. The input is the emotion and symptom analysis data, and the output is the generated health-related suggestions. For example, a suggestion might be generated such as, "Try foods containing magnesium (e.g., nuts or dark chocolate) to reduce irritability."

[1107] Step 7:

[1108] The server provides the generated responses and suggestions to the user through the LINE API. In this process, the generated messages are sent to the user's device, where they are received and displayed. The input is the generated responses and health-related suggestions, and the output is the message displayed on the user's device.

[1109] Through these steps, users are provided with an environment where they can easily seek advice and receive appropriate advice and suggestions.

[1110] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1112] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1113] [Fourth embodiment]

[1114] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1115] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1117] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1118] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1121] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1122] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1123] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1125] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1126] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1127] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data, identifies the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life.

[1128] Specifically, this counseling service system consists of the following main functions:

[1129] 1. A means of receiving user input

[1130] It has the function of receiving data such as text messages and voice inputs sent by users through the messaging platform. When a user sends a message through LINE, the device receives the message and performs initial analysis to normalize the message.

[1131] 2. A means of analyzing received user input to identify emotions and symptoms.

[1132] The device sends the received message to a server, which then analyzes the message using a sentiment analysis algorithm and natural language processing model to identify the user's emotional state and symptoms, such as "headache" or "irritability."

[1133] 3. A means of storing the analysis results in a database

[1134] The server sends the analysis results to the database module, which stores the data in the database for further analysis and updating of the user's profile.

[1135] 4. A way to generate personalized responses based on a user's profile

[1136] The server retrieves the user's profile information from a database and uses a generative AI module to generate a personalized response based on the user's emotions and symptoms. For example, if a user sends a message saying, "I've been suffering from severe headaches and irritability lately," the system will generate a response such as, "You may be feeling stressed lately. Why don't you try some relaxation techniques?"

[1137] 5. How to provide generated responses to users

[1138] The server generates a response and sends it to the user's device via the LINE API, where the user receives and displays the message.

[1139] 6. A means of recording and visualizing user symptom information

[1140] The server stores the user's new symptom information in a database, which can later be used to visualize changes in symptoms. The recorded data can be displayed visually in graphs and charts, and can be used when reporting to medical institutions.

[1141] Specific examples

[1142] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the sequence of events is as follows:

[1143] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[1144] 2. Message analysis: The server passes the message to the chat analysis module, which performs sentiment analysis and keyword extraction to identify symptoms such as "headache" or "irritability."

[1145] 3. Result storage: The server stores the analysis results in a database and manages them together with the user's profile information.

[1146] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[1147] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[1148] 6. Information recording: The server stores the user's new symptom information in a database, which can be used later to visualize changes in symptoms.

[1149] This will provide users with an environment where they can easily seek advice, and it is expected that receiving appropriate advice will reduce anxiety and improve their quality of life.

[1150] The processing flow will be explained below.

[1151] Step 1:

[1152] A user opens the LINE app, types a message, and presses the send button, which sends the user's input through the LINE messaging platform.

[1153] Step 2:

[1154] The device receives a message from the user via the LINE API, performs an initial analysis of the message content, normalizes the message, and converts it into text format.

[1155] Step 3:

[1156] The device sends the normalized message to the server, which receives it and passes it to the analysis module.

[1157] Step 4:

[1158] The server uses an analysis module to analyze the received messages, which uses natural language processing and sentiment analysis algorithms to identify keywords and emotional states (e.g., "headache" or "irritation") within the messages.

[1159] Step 5:

[1160] The server sends the analysis results to a database module, which stores the analysis data in the database, including the user's emotional state and identified symptoms.

[1161] Step 6:

[1162] The server retrieves the user's profile information from a database, including past messaging history and health information.

[1163] Step 7:

[1164] Based on the profile information and analysis results, the server passes the data to a generative AI module to generate personalized responses for the user, such as specific advice or encouraging messages tailored to the user's situation.

[1165] Step 8:

[1166] The server generates a response and sends it to the user's device via the LINE API. The device receives the response message and displays it to the user.

[1167] Step 9:

[1168] The user reads the message and takes appropriate measures based on their situation and mood. If necessary, they can send another message to ask for further advice.

[1169] Step 10:

[1170] The server continuously monitors new messages and changes in the user's condition, and records symptom information in a database, making it possible to visualize the progress and improvement of the user's symptoms.

[1171] Through these steps, users are provided with an environment where they can easily seek advice, and it is expected that the quality of their lives will improve by receiving appropriate advice.

[1172] Example 1

[1173] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1174] Many women today face health problems such as premenstrual syndrome (PMS) and menopausal symptoms, but there are limited environments in which they can easily consult about these symptoms. As a result, there is a need for effective ways to deal with these symptoms. In addition, there is a lack of systems to analyze symptoms and provide appropriate advice, making it a challenge to reduce users' anxiety and improve their quality of life.

[1175] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1176] In this invention, the server includes a means for normalizing the user's input, a means for analyzing the normalized input to identify emotions and symptoms, and a means for storing the analysis results of the identified emotions and symptoms in a database, thereby enabling the server to analyze the user's health status and provide personalized advice.

[1177] "User input" is data such as text messages or voice inputs that a user sends through a messaging application.

[1178] "Normalization" is the process of converting received data into a format that is easier to parse, and includes removing unnecessary whitespace and special characters.

[1179] "Analysis" refers to the process of applying natural language processing and sentiment analysis algorithms to the received data to identify the user's emotional state and symptoms.

[1180] "Emotions and symptoms" refers to specific emotions and physical conditions identified in a user's message, such as "irritability" or "headache."

[1181] A "database" is a structured collection of data that stores analysis results and user profile information and makes them available for later use.

[1182] "Profile information" is data that includes information specific to each user, such as their history, health status, and past messages.

[1183] A "personalized response" is a response message that is individually generated based on the user's profile information and current emotions and symptoms.

[1184] A "messaging application" is a software platform for sending and receiving text and voice messages over the Internet.

[1185] A "server" is a computer system that receives user input, analyzes it, stores the results in a database, and generates and serves a response.

[1186] "User symptom information" means information about physical or mental illness reported by a User through a Message.

[1187] "Visualization" is the process of representing collected data in a visual format, such as a graph or chart, to make it easier to understand.

[1188] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data to identify the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life.

[1189] 1. A means of receiving user input

[1190] user:

[1191] A user opens a messaging application such as LINE, types a message such as "I've been suffering from severe headaches and irritability lately," and sends it. The input data is in text or voice format.

[1192] Device:

[1193] The device receives the user's message via the LINE API. After receiving it, the device performs initial analysis by normalizing the message and converting the data into a format that is easy to parse.

[1194] 2. A means of analyzing received user input to identify emotions and symptoms.

[1195] Device:

[1196] The terminal sends the received message to the server.

[1197] server:

[1198] The server receives the message and performs sentiment analysis and natural language processing (NLP) using libraries such as Python's Natural Language Toolkit (NLTK), TensorFlow, and Keras. As a result of the analysis, symptoms such as "headache" or "irritability" are identified.

[1199] 3. A means of storing the analysis results in a database

[1200] server:

[1201] The server stores the analysis results in a database such as MySQL or PostgreSQL, after which this data is associated with the user's profile information.

[1202] 4. A way to generate personalized responses based on a user's profile

[1203] server:

[1204] The server retrieves the user's profile information from a database. It then uses a generative AI module (e.g., GPT-3) to generate a personalized response based on the user's emotions and symptoms. For example, if a user sends a message saying, "I've been suffering from severe headaches and irritability lately," the server might generate a response such as, "You may be feeling stressed lately. Why don't you try some relaxation techniques?"

[1205] 5. How to provide generated responses to users

[1206] server:

[1207] The server generates a response and sends it to the user's device via the LINE API.

[1208] Device:

[1209] The device displays the response received through the LINE API to the user.

[1210] 6. A means of recording and visualizing user symptom information

[1211] server:

[1212] The server saves new symptom information in a database. Based on the data saved by the server, visual data is generated to visualize changes in symptoms in graphs and charts. This is done using libraries such as JavaScript's D3.js and Python's Matplotlib.

[1213] Specific examples

[1214] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the sequence of events is as follows:

[1215] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[1216] 2. Message analysis: The server passes the message to the chat analysis module, which performs sentiment analysis and keyword extraction to identify symptoms such as "headache" or "irritability."

[1217] 3. Result storage: The server stores the analysis results in a database and manages them together with the user's profile information.

[1218] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[1219] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[1220] 6. Information recording: The server stores the user's new symptom information in a database, which can be used later to visualize changes in symptoms.

[1221] This will provide users with an environment where they can easily seek advice, and it is expected that receiving appropriate advice will reduce anxiety and improve their quality of life.

[1222] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1223] Step 1: Receiving User Input

[1224] user:

[1225] The user opens LINE, types "I've been suffering from severe headaches and irritability lately," and presses the send button. In this process, the user sends a message describing their symptoms and feelings.

[1226] Device:

[1227] The device receives the user's message via the LINE API. The received message data is in text format, and an initial analysis is performed on the device. Specifically, normalization processing is performed, such as removing unnecessary spaces and normalizing special characters. The input for this processing is the user's text message, and the output is the normalized text data.

[1228] Step 2: Message analysis

[1229] Device:

[1230] The terminal sends the normalized message data to the server, which receives it and prepares it for analysis. The input is normalized text data.

[1231] server:

[1232] The server performs sentiment analysis and natural language processing (NLP) using libraries such as Python's Natural Language Toolkit (NLTK), TensorFlow, and Keras. The analysis step involves extracting keywords from the text and tagging it with emotions, ultimately identifying symptoms such as "headache" or "irritability." The input is the received text data, and the output is the analysis results (identified symptoms and emotions).

[1233] Step 3: Save the analysis results

[1234] server:

[1235] The server sends the analyzed results to a database and stores them. Specifically, a database management system such as MySQL or PostgreSQL is used. In this storage process, the analysis results are associated with each user's profile information and stored. The input is the analysis results, and the output is the status of storage in the database.

[1236] Step 4: Generate a response

[1237] server:

[1238] The server retrieves the user's profile information from a database. Next, it uses a generative AI module such as OpenAI's GPT-3 to generate a response. For example, if the user's symptoms involve "stress," it generates a response such as, "You may have been feeling stressed lately. Why don't you try some ways to relax?" The input is the user's profile information and the analysis results, and the output is the generated response message.

[1239] Step 5: Providing generated responses

[1240] server:

[1241] The server generates a response and sends it to the user's device via the LINE API.

[1242] Device:

[1243] The device displays the reply message received via the LINE API to the user. The input is the generated reply message, and the output is the message displayed on the user's device.

[1244] Step 6: Record and visualize information

[1245] server:

[1246] The server saves new symptom information in a database. The saved symptom information is managed as time-series data, and graphs and charts are generated later to visualize changes in symptoms. Specifically, visual data is created using libraries such as JavaScript's D3.js and Python's Matplotlib. The input is new symptom information, and the output is visualized data.

[1247] In this way, users can easily consult with a specialist and receive appropriate advice, which is expected to reduce anxiety and improve their quality of life.

[1248] (Application example 1)

[1249] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1250] There is a need for a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. However, existing systems make it difficult for users to immediately receive locally appropriate counseling, and there is a lack of means to make it easy to use in physical stores in particular. This makes it difficult for users to receive professional advice, and there are issues with improving their quality of life.

[1251] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1252] In this invention, the server includes means for receiving user input, means for analyzing the received user input and identifying emotions and symptoms, means for storing the analysis results in a database, means for generating a personalized response based on the user's profile, means for providing the generated response to the user, means for the user to access the service by scanning a QR code in a physical store, and means for providing locally appropriate counseling based on the scanned QR code. This allows users to easily use counseling services even in physical stores, improving their quality of life.

[1253] "User" refers to an individual who uses the System, and is particularly targeted at women who suffer from premenstrual syndrome (PMS) and menopausal symptoms.

[1254] "Input" refers to the text messages and voice data that users send through the messaging platform.

[1255] "Analysis" refers to the process of identifying emotions and symptoms based on input data received from a user.

[1256] "Database" refers to a collection of data for storing analysis results and user profile information.

[1257] "Profile" refers to a data set containing a user's individual information that is used to generate customized responses.

[1258] "Response" refers to advice or information generated based on the analysis results and provided to the user.

[1259] A "QR code" is a two-dimensional code that encodes specific information and can be scanned by devices such as smartphones.

[1260] "Brick and Mortar Store" means a sales or service establishment that a user can physically visit.

[1261] This invention is a system that allows users to access counseling services in physical stores by scanning a QR code, and is aimed at women suffering from premenstrual syndrome (PMS) and menopausal symptoms.

[1262] The server first receives user input, which is sent via a messaging platform as a text message or voice data. The received data is then analyzed to identify emotions and symptoms. This analysis is performed using natural language processing models and sentiment analysis algorithms. Specifically, the Hugging Face sentiment analysis model is used.

[1263] The analysis results are stored in a database, which not only stores the analysis results but also the user's profile information, which is important for later generating personalized responses.

[1264] Based on the user's profile, the server uses a generative AI model to generate personalized responses, which are then provided to the user via messaging platforms such as the LINE API. Additionally, when the user scans a QR code in a physical store, they are provided with counseling services tailored to their local area.

[1265] This system is implemented using a smartphone, a database server, a generative AI model, and a messaging platform (such as the LINE API). A specific example of how a user can use the counseling service is to scan a QR code displayed on a drugstore counter with their smartphone and enter, "I've been suffering from severe PMS symptoms lately." An example of a prompt based on this principle is shown below.

[1266] Prompt Sentence Examples

[1267] USER: I've been suffering from severe PMS symptoms lately.

[1268] BOT: It sounds like you're experiencing PMS symptoms. Why not try some relaxation techniques?

[1269] This allows users to easily use counseling services in physical stores, improving their quality of life.

[1270] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1271] Step 1:

[1272] User scans the QR code

[1273] When customers visit a physical store, they scan a QR code with their smartphone, which launches a dedicated counseling application and loads the customer's profile information.

[1274] Input: QR code data

[1275] Output: Counseling application launches, user profile loads

[1276] Step 2:

[1277] Receive user input

[1278] Users input text and voice data on their smartphones, including symptoms and emotional state.

[1279] Input: User text messages and voice data

[1280] Output: User input data

[1281] Step 3:

[1282] Analyzing the received data

[1283] The device sends the received input data to a server, which then analyzes the data using sentiment analysis algorithms and natural language processing models to identify symptoms such as "headache" or "irritability."

[1284] Input: User-entered data

[1285] Output: Symptom and emotion analysis results

[1286] Step 4:

[1287] Save the analysis results in a database

[1288] The server stores the analysis results in a database and simultaneously updates the user's profile information.

[1289] Input: Analysis results, existing user profile information

[1290] Output: Updated database

[1291] Step 5:

[1292] Generate personalized responses

[1293] The server uses a generative AI model to generate personalized responses based on the user's profile and analysis results.

[1294] Input: User profile information, analysis results

[1295] Output: Personalized reply (text message)

[1296] Step 6:

[1297] Providing a response to the user

[1298] The server sends the generated response to the user via the LINE API or other messaging platform, and the device displays the response.

[1299] Input: Generated response

[1300] Output: Display reply message on user terminal

[1301] Step 7:

[1302] Record additional information in the database

[1303] The server records the new symptom information in a database that can later be used to visualize changes in symptoms.

[1304] Input: New symptom information

[1305] Output: Updated database

[1306] Through a series of steps, users can easily receive counseling services at a physical store using their smartphone.

[1307] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1308] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data, identifies the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life.

[1309] Furthermore, the present invention incorporates an emotion engine that recognizes the user's emotions, allowing for a more accurate understanding of the user's emotional state.

[1310] Specifically, this counseling service system consists of the following main functions:

[1311] 1. A means of receiving user input

[1312] It has the function of receiving data such as text messages and voice inputs sent by users through the messaging platform. When a user sends a message through LINE, the device receives the message and performs initial analysis to normalize the message.

[1313] 2. A means of analyzing received user input to identify emotions and symptoms.

[1314] The device sends the received message to a server, which then analyzes the message using a sentiment analysis algorithm, natural language processing model, and emotion engine to identify the emotional state and symptoms, such as "headache" or "irritability."

[1315] 3. A means of storing the analysis results in a database

[1316] The server sends the analysis results to a database module, which stores the data, including the user's emotional state and identified symptoms.

[1317] 4. A way to generate personalized responses based on a user's profile

[1318] The server retrieves the user's profile information from a database and uses a generative AI module to generate a personalized response based on the user's emotions and symptoms. For example, if a user sends a message saying, "I've been suffering from severe headaches and irritability lately," the system will generate a response such as, "You may be feeling stressed lately. Why don't you try some relaxation techniques?"

[1319] 5. How to provide generated responses to users

[1320] The server generates a response and sends it to the user's device via the LINE API, where the user receives and displays the message.

[1321] 6. A means of recording and visualizing user symptom information

[1322] The server stores the user's new symptom information in a database, which can later be used to visualize changes in symptoms. The recorded data can be displayed visually in graphs and charts, and can be used when reporting to medical institutions.

[1323] 7. Emotion Recognition with Emotion Engine

[1324] The server uses an emotion engine to analyze the user's emotional state in real time from their message and obtain emotional data. The emotion engine analyzes the user's current emotional state and generates an optimal response based on the analysis results. For example, if the user's message contains feelings of anxiety or anger, a calm and comforting message will be generated.

[1325] Specific examples

[1326] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the sequence of events is as follows:

[1327] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[1328] 2. Message analysis: The server passes the message to the chat analysis module, which uses sentiment analysis, keyword extraction, and an emotion engine to identify symptoms such as "headache" or "irritation" and analyze the user's emotional state (e.g., anxiety, anger).

[1329] 3. Result storage: The server stores the analysis results in a database and manages them together with the user's profile information.

[1330] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[1331] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[1332] 6. Information recording: The server stores the user's new symptom information in a database, which can be used later to visualize changes in symptoms.

[1333] Through these steps, users are provided with an environment where they can easily seek advice, and by receiving appropriate advice, they can reduce their anxiety and improve their quality of life. In addition, accurate emotion analysis by the emotion engine allows for more appropriate responses.

[1334] The processing flow will be explained below.

[1335] Step 1:

[1336] A user opens the LINE app, types a message, and presses the send button, which sends the user's input via the LINE messaging platform.

[1337] Step 2:

[1338] The device receives a message from the user via the LINE API. The device initially parses the message content and normalizes it into text format.

[1339] Step 3:

[1340] The device sends the normalized message to the server, which receives it and passes the data to the analysis module.

[1341] Step 4:

[1342] The server uses an analysis module to analyze the received messages, which uses natural language processing, sentiment analysis, and an emotion engine to identify keywords and emotional states (e.g., "headache" or "irritation") in the messages.

[1343] Step 5:

[1344] The server sends the analysis results to a database module, which stores the analysis data in the database, including information on the user's emotional state and identified symptoms.

[1345] Step 6:

[1346] The server retrieves the user's profile information from a database, including past messaging history and health information.

[1347] Step 7:

[1348] Based on the profile information and analysis results, the server passes the data to a generative AI module to generate personalized responses for the user, such as specific advice or encouraging messages tailored to the user's situation.

[1349] Step 8:

[1350] The server generates a response and sends it to the user's device via the LINE API. The device receives the response message and displays it to the user.

[1351] Step 9:

[1352] The user reads the message and takes appropriate measures based on their situation and mood. If necessary, they can send another message to ask for further advice.

[1353] Step 10:

[1354] The server continuously monitors new messages and changes in the user's condition, and records symptom information in a database, making it possible to visualize the progress and improvement of the user's symptoms.

[1355] Step 11:

[1356] The server uses an emotion engine to analyze the user's emotional state in real time from their message. The emotion engine identifies the user's current emotional state (e.g., anxiety, anger) and generates an optimal response based on the analysis results.

[1357] Step 12:

[1358] The server stores the analysis results of the emotion engine in a database and manages the user's emotion history, which is used for subsequent analysis and feedback to the user.

[1359] Example 2

[1360] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1361] The purpose of this invention is to provide a counseling service that women suffering from premenstrual syndrome (PMS) and menopausal symptoms can easily consult. Conventional counseling methods have been insufficient in analyzing symptoms and emotions, and have difficulty providing personalized advice. For this reason, there is a need for a system that can effectively alleviate users' anxiety and improve their quality of life.

[1362] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a user's input, a means for analyzing the received user's input and identifying emotions and symptoms, a means for saving the analysis result in an information store, a means for generating an individual response based on the user's attribute information, and a means for providing the generated response to the user. This makes it possible to accurately analyze the user's emotions and symptoms and provide appropriate advice.

[1363] "User Input" refers to data, such as text messages or voice inputs, that a User sends through a messaging platform.

[1364] "Means for receiving" refers to the functionality of receiving user input via the message sending platform and performing initial analysis to process the data.

[1365] "Means for analyzing" refers to the ability to analyze received user input data and utilize sentiment analysis algorithms and natural language processing models to identify emotions and symptoms.

[1366] "Information store" refers to a memory device such as a database or cloud storage where the server stores analysis results.

[1367] "Attribute information" refers to data that includes a user's profile information, and refers to information for each user that is managed individually.

[1368] "Means for generating individual responses" refers to a function that uses generative AI to generate personalized responses appropriate for the user based on the analysis results and the user's attribute information.

[1369] "Means for providing to users" refers to the function of sending the generated response to the user's device through the message sending platform, allowing the user to receive and check the message.

[1370] A "messaging platform" is a platform that allows users and systems to communicate with each other, such as LINE or SMS.

[1371] This invention is a counseling service system that allows women suffering from premenstrual syndrome (PMS) and menopausal symptoms to easily seek advice. The system receives data sent by users via a messaging platform, analyzes the data, identifies the user's emotional state and symptoms, and provides appropriate responses, thereby reducing the user's anxiety and improving their quality of life. Furthermore, the invention incorporates an emotion engine that recognizes the user's emotions, enabling a more accurate understanding of the user's emotional state.

[1372] The system consists of the following main functions:

[1373] 1. A means of receiving user input

[1374] The device receives data such as text messages and voice input sent by the user through the messaging platform. For example, if a user sends a message via LINE such as "I've been suffering from severe headaches and irritability lately," the device receives the message through the LINE API. After receiving the data, it normalizes it and passes it to the next processing step.

[1375] 2. A means of analyzing received user input to identify emotions and symptoms.

[1376] The device sends the received message to the server, where it analyzes the message using a natural language processing engine and sentiment analysis algorithms. As a result of the analysis, keywords such as "headache" and "irritation" and the user's emotional state (e.g., anxiety, anger) are identified.

[1377] 3. A means of storing the analysis results in an information store

[1378] The server stores the analysis results in a database, which includes the user's emotional state and identified symptoms, and is managed for each user.

[1379] 4. A way to generate personalized responses based on user demographic information

[1380] The server retrieves the user's attribute information from the database and combines it with the analysis results. Generative AI is used to generate the optimal response for the user. For example, it might generate a response along the lines of, "You've probably been feeling stressed lately. Why not try some ways to relax?"

[1381] 5. How to provide generated responses to users

[1382] The server generates a response and sends it to the user's device via the LINE API, where the user can receive and view the message.

[1383] 6. A means of recording and visualizing user symptom information

[1384] The server records the user's new symptom information in a database, which is later used to visualize changes in symptoms. The recorded data is displayed visually in graphs and charts and can be used for reporting to medical institutions.

[1385] 7. Emotion Recognition with Emotion Engine

[1386] The server uses an emotion engine to analyze the user's emotional state in real time from their message and generates an optimal response based on the analysis results. For example, if the user's message contains feelings of anxiety or anger, it will generate a calm and comforting message in response.

[1387] Specific examples

[1388] For example, if a user types and sends a message to LINE saying, "I've been suffering from severe headaches and irritability lately," the series of processes is as follows:

[1389] 1. Receiving input: The user types and sends the message "I've been suffering from severe headaches and irritability lately." The device receives the message via the LINE API.

[1390] 2. Message analysis: The server passes the message to the chat analysis module, which uses sentiment analysis, keyword extraction, and an emotion engine to identify symptoms such as "headache" or "irritation" and analyze the user's emotional state (e.g., anxiety, anger).

[1391] 3. Result storage: The server stores the analysis results in an information store and manages them together with the user's profile information.

[1392] 4. Response generation: The server uses generative AI to generate a response based on the user's profile information and analysis results. A response such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?" might be generated.

[1393] 5. Providing a reply: The server generates a reply and sends it to the user via the LINE API, and the user's device receives and displays the message.

[1394] 6. Information recording: The server saves the user's new symptom information in an information store, which can be used later to visualize changes in symptoms.

[1395] Example prompts for generative AI models

[1396] An example of a prompt sentence might be:

[1397] "If a user sends a message saying, 'I've been having a lot of headaches and irritability lately,' analyze the user's emotional state and symptoms and generate an appropriate response."

[1398] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1399] Step 1:

[1400] A user sends a text message through a messaging platform. For example, the user opens the LINE app, types "I've been suffering from severe headaches and irritability lately," and presses the send button.

[1401] Input: A text message from the user.

[1402] Output: Raw text data received via the LINE API.

[1403] Specific behavior: The user types and sends a text message. The device receives this message via the LINE API.

[1404] Step 2:

[1405] Normalizes message data received by the terminal, converting input data into a unified format and preparing it for analysis.

[1406] Input: The received raw text data.

[1407] Output: Normalized text data.

[1408] Specific operation: Preprocesses the text to remove unnecessary spaces and special characters, and converts it into normalized text data.

[1409] Step 3:

[1410] The normalized message data is sent to the server.

[1411] Input: Normalized text data.

[1412] Output: The text data sent to the server.

[1413] Specific operation: The device sends the normalized data to the server via the network.

[1414] Step 4:

[1415] The server analyzes the received message data and uses sentiment analysis algorithms, natural language processing engines, and emotion engines to identify emotional states and symptoms.

[1416] Input: Normalized text data sent to the server.

[1417] Output: Analysis results showing identified emotional states and symptoms.

[1418] How it works: The text analysis engine extracts emotion and symptom keywords from the message, and the emotion engine determines the user's emotional state. For example, it identifies the keywords "headache" and "irritation" and determines the emotional state as "anxiety."

[1419] Step 5:

[1420] The analysis results are saved in a database.

[1421] Input: Emotional state and symptom data obtained as a result of the analysis.

[1422] Output: Analysis result data stored in a database.

[1423] Specific operation: The server adds emotional state and symptom data as records to the database.

[1424] Step 6:

[1425] Generate personalized responses based on user demographic information.

[1426] Input: User attribute information and analysis result data stored in the database.

[1427] Output: The generated reply message.

[1428] How it works: The server retrieves the user's attribute information from a database and uses generative AI to generate an appropriate response, such as, "You've probably been feeling stressed lately. Why don't you try some relaxation techniques?"

[1429] Step 7:

[1430] The generated response is provided to the user.

[1431] Input: The generated reply message.

[1432] Output: The reply message received by the user.

[1433] Specific operation: The server generates a response and sends it to the user's device via the LINE API. The user receives the message in the LINE app and displays it.

[1434] Step 8:

[1435] Record and visualize user symptom information.

[1436] Input: New symptom information.

[1437] Output: Visualized symptom information (graphs and charts).

[1438] What it does: The server stores new symptom information in a database and generates graphs and charts based on this data, allowing users and medical institutions to visually monitor changes in symptoms.

[1439] (Application example 2)

[1440] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1441] Until now, women suffering from premenstrual syndrome (PMS) and menopausal symptoms have had limited access to casual consultation services, making it difficult for them to receive appropriate counseling and advice. Furthermore, there has been no system that can provide appropriate dietary recommendations for health based on emotions and symptoms. Therefore, there is a need for a system that can analyze users' emotional state and symptoms in real time and provide personalized support.

[1442] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving a user input, means for analyzing the received user input and identifying emotions and symptoms, and means for storing the analysis results in a database. This makes it possible to generate health-related suggestions based on the analysis results of emotions and symptoms.

[1443] "Means for receiving user input" refers to a function for receiving text messages and voice data entered by the user.

[1444] "Means for analyzing received user input and identifying emotions and symptoms" refers to a function for analyzing received messages and identifying the user's emotional state or specific symptoms.

[1445] The "means for saving the analysis results in a database" is a function for saving the analyzed emotional state and symptom data in a database.

[1446] "Means for generating personalized responses based on a user's profile" refers to a function for generating individually appropriate responses using the user's profile information.

[1447] The "means for providing the generated response to the user" is a function for providing the generated response to the user's terminal.

[1448] The "means for generating health-related suggestions based on the results of the emotion and symptom analysis" is a function for making health-related suggestions to the user based on the analyzed emotional state and symptoms.

[1449] This invention is a counseling service system that allows users to easily receive health consultations, and is designed especially for women suffering from premenstrual syndrome (PMS) and menopausal symptoms. The system has the function of making health-related suggestions based on the analysis of emotions and symptoms.

[1450] First, the system receives text messages and voice data from users through a "means for receiving user input." Users access the system using devices such as smartphones and computers via messaging platforms such as LINE.

[1451] The received message is then analyzed by "means for analyzing received user input to identify emotions and symptoms," using a sentiment analysis engine and / or natural language processing models. This process identifies the user's emotional state (e.g., anxiety, anger) and symptoms (e.g., headache, irritability).

[1452] The analysis results are stored in a database by the "means for storing analysis results in a database." This database accumulates the user's emotional state and symptom history.

[1453] The analyzed data is then used to generate a personalized response based on the user's profile. This response is generated automatically using a generative AI model. For example, if a user posts, "I've been having terrible headaches and irritability lately," the system might generate a response such as, "You're probably feeling stressed lately. Why don't you try some relaxation techniques?"

[1454] Additionally, the device includes a "means for generating health-related suggestions based on emotion and symptom analysis," which provides health-related suggestions based on emotion and symptom analysis. This means provides dietary and lifestyle suggestions based on the symptoms reported by the user. For example, the device may suggest, "Try foods containing magnesium (e.g., nuts or dark chocolate) to reduce irritability."

[1455] Finally, the generated response and suggestion are provided to the user by the "means for providing a generated response to a user," so that the user can instantly receive the response and suggestion through the terminal.

[1456] To give a specific example, if a user sends a message on LINE saying, "I've been having really bad PMS lately...", the analysis engine will activate and generate a response such as, "To help ease the irritability caused by PMS, try foods rich in omega-3 fatty acids (such as salmon or almonds)," which will be provided to the user.

[1457] Here are some examples of prompts for generative AI models:

[1458] "Analyze your emotional state and symptoms and provide appropriate dietary suggestions based on them. User emotion: "Anxiety", Symptom: "Irritability" Suggestion: To reduce irritability, try foods rich in omega-3 fatty acids (e.g., salmon, almonds)."

[1459] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1460] Step 1:

[1461] The user uses the device to send a text message via a messaging platform such as LINE. This is the input to report the consultation details and symptoms to the system. The input data includes a message such as, "I've been suffering from severe headaches and irritability lately."

[1462] Step 2:

[1463] The device receives messages from the user using the LINE API. During this reception process, the message is temporarily stored and prepared for transfer to the server. The output data is the user's text message.

[1464] Step 3:

[1465] The server analyzes the received text message. It uses a sentiment analysis engine to extract keywords from the message and process the data to identify the user's emotional state and symptoms. The input is the user's text message, and the output is the user's emotional state (e.g., anxiety) and identified symptoms (e.g., headache, irritability).

[1466] Step 4:

[1467] The server stores the analysis results in a database. In this process, the analyzed emotional state and symptom data are added to the database along with the user's profile information. The input data are the analysis results and the user profile information, and the output is the analysis data stored in the database.

[1468] Step 5:

[1469] The server generates a personalized response based on the user's profile. Here, a generative AI model is used to generate prompts to create an automatic response based on the analysis results. The input is analysis data and profile information, and the output is the generated response. For example, a response to a user's message might be, "You've probably been feeling stressed lately. Why not try some relaxation techniques?"

[1470] Step 6:

[1471] The server generates health-related suggestions based on the emotion and symptom analysis results. A health-related suggestion engine operates and generates dietary and lifestyle suggestions according to the user's condition. The input is the emotion and symptom analysis data, and the output is the generated health-related suggestions. For example, a suggestion might be generated such as, "Try foods containing magnesium (e.g., nuts or dark chocolate) to reduce irritability."

[1472] Step 7:

[1473] The server provides the generated responses and suggestions to the user through the LINE API. In this process, the generated messages are sent to the user's device, where they are received and displayed. The input is the generated responses and health-related suggestions, and the output is the message displayed on the user's device.

[1474] Through these steps, users are provided with an environment where they can easily seek advice and receive appropriate advice and suggestions.

[1475] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1476] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1477] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1478] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1479] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1480] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1481] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1482] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1483] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1484] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1485] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1486] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1487] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1488] 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.

[1489] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1490] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1491] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1492] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1493] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1494] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1495] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1496] The following is further disclosed regarding the above embodiment.

[1497] (Claim 1)

[1498] means for receiving user input;

[1499] means for analyzing the received user input to identify emotions and symptoms;

[1500] a means for storing the analysis results in a database;

[1501] a means for generating personalized responses based on a user profile;

[1502] a means for providing the generated response to the user;

[1503] A system including:

[1504] (Claim 2)

[1505] 10. The system of claim 1, further comprising means for recording and visualizing a user's symptom information.

[1506] (Claim 3)

[1507] 10. The system of claim 1, wherein the system uses a messaging platform as a means of communication.

[1508] "Example 1"

[1509] (Claim 1)

[1510] means for receiving user input;

[1511] a means for normalizing received user input; and

[1512] means for analyzing the normalized input and identifying emotions and symptoms;

[1513] means for storing the analysis of the identified emotions and symptoms in a database;

[1514] means for retrieving user profile information from a database and generating a personalized response based on the retrieved information;

[1515] a means for providing the generated response to the user;

[1516] A system including:

[1517] (Claim 2)

[1518] 10. The system of claim 1, further comprising means for recording a user's symptom information and providing data for later visualization of symptom changes.

[1519] (Claim 3)

[1520] 10. The system of claim 1, wherein the system uses a messaging application as a means of communication.

[1521] "Application Example 1"

[1522] (Claim 1)

[1523] means for receiving user input;

[1524] means for analyzing the received user input to identify emotions and symptoms;

[1525] a means for storing the analysis results in a database;

[1526] a means for generating personalized responses based on a user profile;

[1527] a means for providing the generated response to the user;

[1528] A way for users to scan a QR code in a physical store and access it.

[1529] A means to provide locally tailored counselling based on scanned QR codes;

[1530] A system including:

[1531] (Claim 2)

[1532] 10. The system of claim 1, further comprising means for recording and visualizing a user's symptom information.

[1533] (Claim 3)

[1534] 10. The system of claim 1, wherein the system uses a messaging platform as a means of communication.

[1535] "Example 2: Combining Emotion Engines"

[1536] (Claim 1)

[1537] means for receiving user input;

[1538] means for analyzing the received user input to identify emotions and symptoms;

[1539] a means for storing the analysis results in an information store;

[1540] A means for generating a personalized response based on user attribute information;

[1541] a means for providing the generated response to the user;

[1542] A system including:

[1543] (Claim 2)

[1544] 10. The system of claim 1, further comprising means for recording and visualizing a user's symptom information.

[1545] (Claim 3)

[1546] 10. The system of claim 1, wherein the communication means uses a messaging platform.

[1547] "Application example 2 when combining emotion engines"

[1548] (Claim 1)

[1549] means for receiving user input;

[1550] means for analyzing the received user input to identify emotions and symptoms;

[1551] a means for storing the analysis results in a database;

[1552] a means for generating personalized responses based on a user profile;

[1553] a means for providing the generated response to the user;

[1554] means for generating health-related recommendations based on the emotion and symptom analysis results;

[1555] A system including:

[1556] (Claim 2)

[1557] 10. The system of claim 1, further comprising means for recording and visualizing a user's symptom information.

[1558] (Claim 3)

[1559] 10. The system of claim 1, wherein the system uses a messaging platform as a means of communication. [Explanation of symbols]

[1560] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving user input; means for analyzing the received user input to identify emotions and symptoms; a means for storing the analysis results in a database; a means for generating personalized responses based on a user profile; a means for providing the generated response to the user; A system including:

2. The system of claim 1 , further comprising means for recording and visualizing a user's symptom information.

3. The system of claim 1 , wherein the system uses a messaging platform as a means of communication.

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