System

Abstract

A system is provided.SOLUTION: A system comprising: means for analyzing a consultation content received from a user; means for generating an appropriate advice based on the analysis result; means for acquiring a related product or service based on the consultation content; means for providing the generated advice and the acquired product or service to the user; means for storing the consultation content from the user and the provided advice in a database; and means for improving an analysis algorithm based on the stored data.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, people suffer from a variety of physical and mental ailments and beauty concerns in their daily lives. However, many people tend to ignore these minor ailments, which can later develop into major health problems. Furthermore, busy lives make it difficult for people to visit a doctor, so they often worry alone. Therefore, by providing a forum where people can easily seek advice and receive appropriate advice, it is necessary to promote early care and prevent problems before they occur. [Means for solving the problem]

[0005] The present invention provides a system including: means for analyzing consultation content received from a user; means for generating appropriate advice based on the analysis results; means for acquiring related products and services based on the consultation content; means for providing the generated advice and the acquired products and services to the user; means for storing the consultation content from the user and the provided advice in a database; and means for improving an analysis algorithm based on the stored data. This system allows users to easily receive consultations regarding mind, body, and beauty, and quickly obtain appropriate advice and related product information. Furthermore, the system can improve the accuracy of advice by continuously learning from data.

[0006] "Accept" refers to the act of the system receiving initial input from the user.

[0007] "Consultation content" refers to specific problems or questions about physical or mental health or beauty entered by the user.

[0008] "Analysis" refers to the process of analyzing the input consultation content and identifying the intention and emotion of the content.

[0009] "Advice generation" refers to the process of creating specific suggestions or solutions to provide to users based on the analyzed results.

[0010] "Related products and services" refers to products and support services recommended based on the user's inquiry.

[0011] "Providing" refers to the act of presenting the generated advice and related product information to the user in a visible form.

[0012] "Database" refers to a collection of information within the system that stores records of inquiries from users and advice provided.

[0013] "Analysis algorithm" refers to the calculation method and processing logic used to understand the content of a user's inquiry and generate appropriate advice.

[0014] "Natural language processing engine" refers to a software system that analyzes the consultation content entered by a user and performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition.

[0015] "API" refers to an application program interface that allows systems to connect with external e-commerce sites and health-related services. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

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

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention relates to an AI consultation desk system that allows users to easily consult about mental, physical, and beauty-related matters. We will explain in detail how various servers, terminals, and users interact to implement the system.

[0038] 1. User Registration and Login

[0039] User

[0040] First, the user accesses the system's registration page and enters information such as name, email address, and password. After entering the information, the user clicks the Register button to send the information from the terminal to the server. Once registration is complete, the user enters their email address and password and clicks the Login button to log in to the system.

[0041] Terminal

[0042] The terminal receives the registration information entered by the user and sends it to the server, as well as the login information.

[0043] server

[0044] The server stores the received registration information in a database and generates a user ID. When logging in, it compares the information with the database and sends the authentication result to the terminal.

[0045] 2. Receiving and analyzing consultation details

[0046] User

[0047] After logging in to the system, the user enters the content of the consultation in the text box and clicks the send button.

[0048] Terminal

[0049] The terminal transmits the consultation content input by the user to the server and waits for a response from the server.

[0050] server

[0051] The server passes the received consultation content to the natural language processing engine and executes the following processes:

[0052] 1. Text tokenization

[0053] 2. Part-of-Speech Tagging

[0054] 3. Emotion analysis

[0055] 4. Intention Recognition

[0056] 3. Generating Advice

[0057] server

[0058] Based on the analysis results, the server uses an advice generation engine to generate appropriate advice. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice.

[0059] 4. Introducing related products and services

[0060] server

[0061] Based on the consultation content, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[0062] 5. Providing advice and product links

[0063] server

[0064] The server transmits the generated advice and links to related products and services to the terminal.

[0065] Terminal

[0066] The terminal displays the received advice and links to the user.

[0067] User

[0068] The user can check the advice displayed on the device and, if necessary, click on the provided link to check the product or service in detail and purchase it.

[0069] 6. Data storage and AI training

[0070] server

[0071] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to improve the analysis algorithm and increase the accuracy of the advice.

[0072] Specific examples

[0073] Example 1: Stress consultation

[0074] 1. User: Enter "I've been having trouble sleeping lately because of stress. Please tell me what to do."

[0075] 2. Terminal: Sends the consultation details to the server.

[0076] 3. Server:

[0077] Analysis: Identify stress and sleep problems.

[0078] Advice generation: "We recommend relaxing activities and supplements."

[0079] Get related services: Get links to stress relief related supplements.

[0080] 4. Server: Sends advice and product links to the device.

[0081] 5. Terminal: Display advice and links to the user.

[0082] 6. User: Reads the advice and clicks on the supplement link to purchase.

[0083] Example 2: Skin care consultation

[0084] 1. User: Type "I'm worried about my skin. Please tell me how to take care of it."

[0085] 2. Terminal: Sends the consultation details to the server.

[0086] 3. Server:

[0087] Analysis: Identify skin care problems.

[0088] Advice generated: "We recommend using skin care products that emphasize moisturizing."

[0089] Get related services: Get links to moisturizing skin care products.

[0090] 4. Server: Sends advice and product links to the device.

[0091] 5. Terminal: Display advice and links to the user.

[0092] 6. User: Read the advice and click on the product link to purchase.

[0093] In this way, the system of the present invention allows users to easily consult with the doctor and quickly provides appropriate advice and related product suggestions, thereby greatly improving quality of life and health management.

[0094] The processing flow will be explained below.

[0095] Step 1:

[0096] A user accesses the system's registration page, enters their name, email address, and password, and then clicks the registration button to submit the information.

[0097] Step 2:

[0098] The terminal receives the registration information entered by the user and transmits it to the server.

[0099] Step 3:

[0100] The server stores the received registration information in a database, generates a user ID, and returns a message to the terminal indicating that the user registration was successful.

[0101] Step 4:

[0102] A user visits the login page, enters their email address and password, and clicks the login button.

[0103] Step 5:

[0104] The terminal sends the entered login information to the server.

[0105] Step 6:

[0106] The server checks the information in the database and sends the authentication result to the terminal. If the authentication is successful, the user can access the system.

[0107] Step 7:

[0108] After logging in, the user enters the content they want to consult about in the text box and clicks the send button.

[0109] Step 8:

[0110] The device sends the consultation content and user ID to the server.

[0111] Step 9:

[0112] The server passes the received consultation content to a natural language processing engine and analyzes the content.

[0113] Tokenize the text and split the sentence into words.

[0114] Part-of-speech tagging is performed to identify the part of speech of each word.

[0115] Sentiment analysis is performed to analyze the emotional tone of the entire text.

[0116] Perform intent recognition and identify the main idea of ​​the text.

[0117] Step 10:

[0118] The server generates appropriate advice based on the analysis results using an advice generation engine.

[0119] Based on the analysis results, the relevant knowledge base is referenced.

[0120] Select the appropriate advice template.

[0121] Generate specific advice sentences according to templates.

[0122] Step 11:

[0123] Based on the consultation content, the server retrieves related products and services via the APIs of external e-commerce sites and health-related services.

[0124] Query the EC site API based on the keywords in the inquiry.

[0125] The acquired product information is filtered to select the most suitable product for the user.

[0126] Step 12:

[0127] The server transmits the generated advice and links to related products to the terminal.

[0128] Step 13:

[0129] The device displays the received advice and link to the user.

[0130] Step 14:

[0131] The user checks the advice displayed on the device and, if necessary, clicks on the provided link to check the product or service in detail and purchase it.

[0132] Step 15:

[0133] The server stores the user's consultation details and the advice provided in a database.

[0134] The user ID, consultation content, analysis results, and advice provided are saved as records.

[0135] Step 16:

[0136] The server periodically uses the data to update the AI ​​learning model.

[0137] Extract new training data from the saved database.

[0138] Retrain the AI ​​model to improve accuracy.

[0139] Push the updated AI model into production.

[0140] In this way, the system of the present invention improves the user's quality of life and health management by quickly and efficiently analyzing the content of the user's consultation and providing appropriate advice and related products.

[0141] Example 1

[0142] 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."

[0143] With conventional consultation systems, even if users input their consultation details, it was difficult to provide appropriate advice in a short time. Furthermore, suggestions for related products and services were limited, and the overall system lacked integration, making it difficult to improve the user experience. Furthermore, there was no mechanism for improving the accuracy of the system by utilizing past consultation details and advice. This made it difficult to create a system that users could use with confidence.

[0144] 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.

[0145] In this invention, the server includes means for receiving and validating user registration information and storing it in a database, means for receiving login information and verifying it against the database for authentication, means for tokenizing text, tagging parts of speech, analyzing sentiment, and recognizing intent using a natural language processing engine, means for generating appropriate advice by referencing a knowledge base using an advice generation engine, and means for transmitting and displaying the advice and links to related products and services to the terminal. This makes it possible to provide prompt and appropriate advice in response to the consultation content entered by the user and suggest related products and services. In addition, the analysis algorithm can be improved based on the stored data, improving the accuracy of the entire system and the user experience.

[0146] "Means of receiving user registration information, validating it, and saving it in a database" is a function that obtains personal information and login information entered by the user, checks its accuracy and correctness of format, and then stores it in a database.

[0147] The "means for receiving login information and verifying it against a database" is a function that obtains login information provided by a user and compares it with existing data in a database to identify and authenticate the user.

[0148] A "natural language processing engine" is a software component that analyzes text data and performs information extraction and semantic analysis using techniques for understanding human language.

[0149] "Tokenization" is a natural language processing technique that analyzes sentences and text and breaks them down into smaller units (words and phrases).

[0150] "Part-of-speech tagging" is a technique for identifying the grammatical role (noun, verb, adjective, etc.) of each word in a sentence and assigning it a tag.

[0151] "Sentiment analysis" is a technology that reads the speaker's emotions and intentions from text data and classifies them into emotional categories such as positive, negative, and neutral.

[0152] "Intention recognition" is an analytical technique for understanding the intention and purpose of what a user says and determining the appropriate response or action.

[0153] The "advice generation engine" is a software component that generates optimal advice for the user based on the analyzed data.

[0154] A "knowledge base" is a database that systematically collects and stores specialized information and facts about a particular field, and is an information source that can be referenced by an advice generation engine.

[0155] A "terminal" is an input and output device such as a computer or smartphone that can be directly operated by a user.

[0156] The "means for sending and displaying links to related products and services" is a function for searching for related products and services based on the content of the user's consultation, and sending the link information to the user's terminal for display.

[0157] "Means for improving analysis algorithms based on stored data" refers to a function that stores past user consultation details and advice provided in a database, and uses that data to improve the algorithms in order to enhance the system's analytical capabilities.

[0158] This invention is an AI consultation system that allows users to easily consult about physical, mental, and beauty-related issues. This section explains in detail how various servers, terminals, and users interact to implement the system. The system performs a series of processes: user registration, reception and analysis of consultation details, generation of advice, introduction of related products and services, provision of advice and product links, data storage, and AI learning.

[0159] User Registration and Login

[0160] User

[0161] A user accesses the system's registration page, enters information such as name, email address, and password, and clicks the Register button. The user also enters an email address and password on the login page and clicks the Login button.

[0162] Terminal

[0163] The device receives the registration information and login information entered by the user, sends it to the server, receives the response from the server, and displays the result to the user.

[0164] server

[0165] The server deserializes the received registration information, validates it, and saves it in the database. In the case of login information, it compares it with the information in the database and sends the authentication result to the terminal.

[0166] Receiving and analyzing consultation details

[0167] User

[0168] After logging in, the user enters the content of the consultation in the text box and clicks the send button.

[0169] Terminal

[0170] The terminal receives the consultation content input by the user and transmits it to the server.

[0171] server

[0172] The server passes the received consultation content to a natural language processing engine (such as SpaCy or NLTK), which performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition.

[0173] Generating Advice

[0174] server

[0175] Based on the analysis results of the natural language processing engine, the server uses an advice generation engine to refer to a knowledge base and generate appropriate advice, such as "We recommend taking deep breaths to relax."

[0176] Introducing related products and services

[0177] server

[0178] Based on the consultation details, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, it uses the Amazon API to search for "relaxation supplements" and retrieves links to relevant products.

[0179] Providing advice and product links

[0180] server

[0181] The server transmits the generated advice and links to related products to the terminal.

[0182] Terminal

[0183] The terminal displays the received advice and links to the user.

[0184] User

[0185] The user checks the advice and links displayed on the device and, if necessary, clicks on the provided link to view more details.

[0186] Data storage and AI training

[0187] server

[0188] The server stores the user's consultation details and the advice provided in a database. The stored data is used to improve the analysis algorithm and for AI training. This data is periodically used to update the analysis algorithm and improve the accuracy of the entire system.

[0189] Specific examples

[0190] Example 1: Stress consultation

[0191] 1. User: Enter "I've been having trouble sleeping lately because of stress. Please tell me what to do."

[0192] 2. Terminal: Sends the consultation details to the server.

[0193] 3. Server:

[0194] Analysis: Identify stress and sleep problems.

[0195] Advice generation: "We recommend relaxing activities and supplements."

[0196] Get related services: Get links to stress relief related supplements.

[0197] 4. Server: Sends advice and product links to the device.

[0198] 5. Terminal: Display advice and links to the user.

[0199] 6. User: Reads the advice and clicks on the supplement link to purchase.

[0200] Example 2: Skin care consultation

[0201] 1. User: Type "I'm worried about my skin. Please tell me how to take care of it."

[0202] 2. Terminal: Sends the consultation details to the server.

[0203] 3. Server:

[0204] Analysis: Identify skin care problems.

[0205] Advice generated: "We recommend using skin care products that emphasize moisturizing."

[0206] Get related services: Get links to moisturizing skin care products.

[0207] 4. Server: Sends advice and product links to the device.

[0208] 5. Terminal: Display advice and links to the user.

[0209] 6. User: Read the advice and click on the product link to purchase.

[0210] Prompt Sentence Examples

[0211] 1. Registration prompt

[0212] Please visit the registration page and enter the required information to complete your registration. Enter your name, email address, and password and click the submit button.

[0213] 2. Consultation prompts

[0214] After logging in, enter the content you would like to discuss in the text box and click the send button. For example, you could enter, "I've been having trouble sleeping lately because of stress. Please tell me what to do."

[0215] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0216] Step 1: User Registration

[0217] (input)

[0218] The user accesses the system's registration page, enters their name, email address, and password, and clicks the registration button.

[0219] (process)

[0220] The device receives the entered registration information and sends it to the server in JSON format.

[0221] The server deserializes the received registration information and performs validation. Validation checks the format and duplication of the input data. The data is then saved to the database. After saving, a user ID is generated and a response indicating successful registration is sent to the terminal.

[0222] (output)

[0223] The user will see a message on their terminal confirming successful registration.

[0224] Step 2: User Login

[0225] (input)

[0226] The user enters their email address and password on the login page and clicks the login button.

[0227] (process)

[0228] The terminal receives the entered login information and sends it to the server.

[0229] The server deserializes the received login information and checks it against the registered information in the database. If authentication is successful, it generates an authentication token and sends it to the device. If authentication fails, it sends an error message.

[0230] (output)

[0231] The user will see a message on the terminal indicating whether the login was successful or unsuccessful.

[0232] Step 3: Enter your consultation details

[0233] (input)

[0234] After logging in, the user enters the content of their problem (e.g., "I can't sleep lately because of stress") into the text box and clicks the send button.

[0235] (process)

[0236] The terminal receives the entered text and sends it to the server.

[0237] The server passes the received text to a natural language processing engine (e.g., SpaCy, NLTK) that performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition, thereby extracting important information and sentiment from the text.

[0238] (output)

[0239] The server stores the analysis results internally and proceeds to the next step.

[0240] Step 4: Generating Advice

[0241] (input)

[0242] The server passes data to the advice generation engine based on the analysis results.

[0243] (process)

[0244] The advice generation engine refers to the knowledge base and generates appropriate advice, such as "We recommend taking deep breaths to relax."

[0245] (output)

[0246] The server keeps the generated advice internally and proceeds to the next step.

[0247] Step 5: Acquire related products and services

[0248] (input)

[0249] The server sends an API request to an external e-commerce site or health-related service to obtain related products or services based on the consultation content (e.g., stress management).

[0250] (process)

[0251] In response to a request, the external service returns information about related products or services (e.g., links to supplements).

[0252] The server receives this and stores it as internal data.

[0253] (output)

[0254] The server internally stores the links to the acquired products and services and proceeds to the next step.

[0255] Step 6: Provide advice and product links

[0256] (input)

[0257] The server integrates the generated advice with the retrieved product links.

[0258] (process)

[0259] The server transmits the consolidated information package to the terminal.

[0260] The terminal receives this and displays it to the user.

[0261] (output)

[0262] Users can view the advice and product links on their device and, if necessary, click on the provided link to view more details.

[0263] Step 7: Store the data and train the AI

[0264] (input)

[0265] The server stores all users' consultation details, advice provided, and user responses in a database.

[0266] (process)

[0267] Periodically, the server analyzes the stored data and performs a learning process to improve the accuracy of the analysis algorithms and advice generation engine.

[0268] (output)

[0269] Improved analysis algorithms and advice generation engines will result in more accurate responses to future users.

[0270] (Application example 1)

[0271] 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."

[0272] Improving the physical and mental health of workers and the working environment has become an important issue in modern factories. However, many workers have few easy access points to discuss their health and concerns, leading to continued stress and health problems. Furthermore, managers have few means of understanding workers' health status in real time, making it difficult to provide appropriate support. A system to resolve these issues and improve the working environment is needed.

[0273] 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.

[0274] In this invention, the server includes: means for analyzing the consultation content received from the user; means for generating appropriate advice based on the analysis results; means for acquiring related products and services based on the consultation content; means for providing the generated advice and the acquired products and services to the user; means for storing the consultation content from the user and the provided advice in a database; means for improving the analysis algorithm based on the stored data; means for receiving the consultation content from the user, the means including a robot that interacts with workers in the factory; and means for displaying the generated advice and the acquired products and services to workers in the factory. This allows workers to easily seek advice about their physical and mental health and work, and further enables the provision of appropriate advice and related products. Furthermore, managers can grasp the health status and consultation content of workers in real time, enabling them to provide prompt and appropriate support.

[0275] "Users" refer to factory workers who seek consultation through this system.

[0276] "Consultation content" is the text of worries and questions about mental and physical health and work that users input into the system.

[0277] The "analysis means" is a process of analyzing the input consultation content using a natural language processing engine, tokenizing the text, tagging parts of speech, analyzing emotions, and recognizing intent.

[0278] The "means for generating advice" is a process for extracting and generating appropriate advice from the knowledge base based on the analysis results.

[0279] "Means of obtaining related products and services" refers to the process of obtaining links to related products and services through the APIs of external e-commerce sites and health-related services based on the content of the consultation.

[0280] "Storage means" refers to the process of recording the user's consultation details and the advice provided in a database.

[0281] "Means for improving the analysis algorithm" refers to the process of periodically improving the analysis algorithm based on the stored data to improve the accuracy of advice.

[0282] A "robot that can interact with factory workers" is a robot that is placed in a factory, accepts inquiries from workers, and has the ability to return analysis results and advice in real time.

[0283] "Means for displaying to workers" refers to the process of visually providing generated advice and links to acquired goods and services to workers through a robot or other device.

[0284] This invention is an AI consultation system that allows factory workers to easily seek advice on mental, physical, and work-related issues. This paper explains in detail how various servers, terminals, and robots interact to implement the system.

[0285] 1. User Registration and Login

[0286] User:

[0287] A factory worker, who is a user, first accesses the system's registration page and enters information such as name, email address, and password. After entering the information, the user clicks the registration button to send the information from the terminal to the server. Once registration is complete, the user enters their email address and password and clicks the login button to log in to the system.

[0288] Device:

[0289] The terminal receives the registration information entered by the user and sends it to the server, as well as the login information.

[0290] server:

[0291] The server stores the received registration information in a database and generates a user ID. When logging in, it compares the information with the database and sends the authentication result to the terminal.

[0292] 2. Receiving and analyzing consultation details

[0293] User:

[0294] After logging in to the system, the user enters the content of the consultation in the text box and clicks the send button.

[0295] Device:

[0296] The terminal transmits the consultation content input by the user to the server and waits for a response from the server.

[0297] server:

[0298] The server passes the received consultation content to the natural language processing engine and executes the following processes:

[0299] 1. Text tokenization

[0300] 2. Part-of-Speech Tagging

[0301] 3. Emotion analysis

[0302] 4. Intention Recognition

[0303] 3. Generating Advice

[0304] server:

[0305] Based on the analysis results, the server uses an advice generation engine to generate appropriate advice. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice.

[0306] 4. Introducing related products and services

[0307] server:

[0308] Based on the consultation content, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[0309] 5. Providing advice and product links

[0310] server:

[0311] The server transmits the generated advice and links to related products and services to the terminal.

[0312] Device:

[0313] The terminal displays the received advice and links to the user.

[0314] 6. Data storage and AI training

[0315] server:

[0316] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to improve the analysis algorithm and increase the accuracy of the advice.

[0317] Specific examples

[0318] Usage example

[0319] Example 1: Stress consultation

[0320] User: Type "I've been having trouble sleeping lately because of stress. What can I do?"

[0321] Terminal: Sends the consultation details to the server.

[0322] server:

[0323] - Analysis: Identify stress and sleep problems.

[0324] - Advice generation: "We recommend relaxing activities and supplements."

[0325] - Get related services: Get links to stress relief related supplements.

[0326] Server: Sends advice and product links to the device.

[0327] Terminal: Display advice and links to users.

[0328] User: Reads the advice, clicks on the supplement link and purchases.

[0329] Example 2: Skin care consultation

[0330] User: Type "I'm worried about my skin. Please tell me how to take care of it."

[0331] Terminal: Sends the consultation details to the server.

[0332] server:

[0333] - Analysis: Identify skin care problems.

[0334] - Advice generation: "We recommend using skin care products that emphasize moisturizing."

[0335] - Get related services: Get links to moisturizing skin care products.

[0336] Server: Sends advice and product links to the device.

[0337] Terminal: Display advice and links to users.

[0338] User: Reads the advice and clicks on the product link to purchase.

[0339] In this way, the system of the present invention allows workers to easily seek advice on mental, physical, and work-related issues, and provides appropriate advice in real time and prompt recommendations for related products. The specific hardware used is a conversational robot installed in the factory, and the software used is a server using a natural language processing engine (spaCy or Transformers) and Flask. This is expected to significantly improve the working environment and improve worker health management.

[0340] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0341] Step 1:

[0342] User: Accesses the system's registration page and enters registration information such as name, email address, password, etc. After entering the information, clicks the registration button to send the entered data from the terminal to the server.

[0343] Step 2:

[0344] Terminal: Sends the registration information received from the user to the server and waits for a response from the server.

[0345] Step 3:

[0346] Server: Receives the registration information and stores it in the database. After storing it, it generates a user ID and returns a message to the device indicating that registration is complete.

[0347] Step 4:

[0348] User: After completing registration, log in to the system using your email address and password.

[0349] Step 5:

[0350] Device: Sends login information to the server and begins the authentication process.

[0351] Step 6:

[0352] Server: Compares the login information with the information in the database and sends the authentication result to the terminal.

[0353] Step 7:

[0354] User: After logging in to the system, enter the content of the consultation in the text box and click the send button.

[0355] Step 8:

[0356] Terminal: Sends the consultation content to the server and waits for a response from the server.

[0357] Step 9:

[0358] Server: Passes the received consultation content to the natural language processing engine and performs the following data processing and calculations:

[0359] Text tokenization: Splitting the conversation into words and phrases.

[0360] Part-of-speech tagging: Assigning a part-of-speech label to each word.

[0361] Sentiment analysis: Analyzes the sentiment of the consultation content and assigns sentiment labels such as positive, negative, or neutral.

[0362] Intent recognition: Identify the specific intent of the consultation (e.g., stress, skin care, etc.).

[0363] Step 10:

[0364] Server: Based on the analysis results, the advice generation engine extracts and generates appropriate advice from the knowledge base.

[0365] Step 11:

[0366] Server: To obtain related products and services based on the consultation details, the server calls the APIs of external e-commerce sites and health-related services to obtain related product links.

[0367] Step 12:

[0368] Server: Sends the generated advice and the obtained product link together to the device.

[0369] Step 13:

[0370] Terminal: Display received advice and links to the user.

[0371] Step 14:

[0372] User: Reads the advice and clicks on the provided links to learn more about the product or service and make a purchase if necessary.

[0373] Step 15:

[0374] Server: The content of the consultation and the advice provided are stored in a database and used as material for improving the analysis algorithm. The stored data is periodically analyzed to improve the accuracy of the algorithm.

[0375] 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.

[0376] This invention relates to an AI consultation system that allows users to easily consult about mental, physical, and beauty-related matters. In particular, it features an emotion engine that provides appropriate advice based on the user's emotional state.

[0377] 1. User Registration and Login

[0378] User

[0379] First, the user accesses the system's registration page and enters information such as name, email address, and password. After entering the information, the user clicks the Register button to send the information from the terminal to the server. Once registration is complete, the user enters their email address and password and clicks the Login button to log in to the system.

[0380] Terminal

[0381] The terminal receives the registration information entered by the user and sends it to the server, as well as the login information.

[0382] server

[0383] The server stores the received registration information in a database and generates a user ID. When logging in, it compares the information with the database and sends the authentication result to the terminal.

[0384] 2. Receiving and analyzing consultation details

[0385] User

[0386] After logging in to the system, users enter the content of their consultation in the text box and click the send button. If voice input is available, users can also record and send the content of their consultation by voice.

[0387] Terminal

[0388] The terminal transmits the consultation content input by the user to the server and waits for a response from the server.

[0389] server

[0390] The server passes the received consultation content to a natural language processing engine and an emotion engine, and analyzes the content.

[0391] Tokenize the text and split the sentence into words.

[0392] Part-of-speech tagging is performed to identify the part of speech of each word.

[0393] Sentiment analysis is performed to analyze the emotional tone of the entire text.

[0394] Perform intent recognition and identify the main idea of ​​the text.

[0395] When there is voice input, it performs voice analysis, converts the content into text, and recognizes emotions.

[0396] 3. Generating Advice

[0397] server

[0398] The server generates appropriate advice using an advice generation engine based on the analysis results. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice. The advice is customized based on the user's emotions recognized by the emotion engine.

[0399] 4. Introducing related products and services

[0400] server

[0401] Based on the consultation content, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[0402] 5. Providing advice and product links

[0403] server

[0404] The server transmits the generated advice and links to related products and services to the terminal.

[0405] Terminal

[0406] The terminal displays the received advice and links to the user.

[0407] User

[0408] The user can check the advice displayed on the device and, if necessary, click on the provided link to check the product or service in detail and purchase it.

[0409] 6. Data storage and AI training

[0410] server

[0411] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to update the analysis algorithm and improve the accuracy of the advice.

[0412] Specific examples

[0413] Example 1: Stress consultation

[0414] 1. User: Enter text such as "I've been having trouble sleeping lately because of stress. Please tell me what to do." Or, enter voice such as "I'm so stressed, I don't know what to do."

[0415] 2. Terminal: Sends the consultation details to the server.

[0416] 3. Server:

[0417] Analysis: Identify stress and sleep problems.

[0418] Sentiment analysis: Identifying emotions such as stress and anxiety from user input.

[0419] Advice generation: "We recommend you engage in relaxing activities or take supplements." Furthermore, the advice is customized to match the user's emotional state, such as "First, try taking a deep breath and relaxing."

[0420] Get related services: Get links to stress relief related supplements.

[0421] 4. Server: Sends advice and product links to the device.

[0422] 5. Terminal: Display advice and links to the user.

[0423] 6. User: Reads the advice and clicks on the supplement link to purchase.

[0424] Example 2: Skin care consultation

[0425] 1. User: Enter text such as "I'm worried about my skin condition. Please tell me how to take care of it." Or enter voice such as "My skin condition has been getting worse recently and I don't know what to do."

[0426] 2. Terminal: Sends the consultation details to the server.

[0427] 3. Server:

[0428] Analysis: Identify skin care problems.

[0429] Sentiment analysis: Identifying feelings of confusion or anxiety from user input.

[0430] Advice generation: "We recommend using skin care products that emphasize moisturizing." This can be further customized to ease confusion and anxiety by saying, "Don't worry, we'll recommend effective products."

[0431] Get related services: Get links to moisturizing skin care products.

[0432] 4. Server: Sends advice and product links to the device.

[0433] 5. Terminal: Display advice and links to the user.

[0434] 6. User: Read the advice and click on the product link to purchase.

[0435] In this way, the system of the present invention improves the user's quality of life and health management by quickly and efficiently analyzing the user's consultation content and providing appropriate advice and related products and services that take into account the user's emotional state.

[0436] The processing flow will be explained below.

[0437] Step 1:

[0438] A user accesses the system's registration page, enters their name, email address, and password, and then clicks the registration button to submit the information.

[0439] Step 2:

[0440] The terminal receives the registration information entered by the user and transmits it to the server.

[0441] Step 3:

[0442] The server stores the received registration information in a database, generates a user ID, and returns a message to the terminal indicating that the user registration was successful.

[0443] Step 4:

[0444] A user visits the login page, enters their email address and password, and clicks the login button.

[0445] Step 5:

[0446] The terminal sends the entered login information to the server.

[0447] Step 6:

[0448] The server checks the information in the database and sends the authentication result to the terminal. If the authentication is successful, the user can access the system.

[0449] Step 7:

[0450] After logging in, the user enters the content of the consultation in the text box and clicks the send button. If voice input is available, the user can also record the content of the consultation by voice and send it.

[0451] Step 8:

[0452] The device sends the consultation content and user ID to the server.

[0453] Step 9:

[0454] The server passes the received consultation content to a natural language processing engine and an emotion engine, and analyzes the content.

[0455] Tokenize the text and split the sentence into words.

[0456] Part-of-speech tagging is performed to identify the part of speech of each word.

[0457] Sentiment analysis is performed to identify the user's emotions from the input.

[0458] Perform intent recognition and identify the main idea of ​​the text.

[0459] When there is voice input, it performs voice analysis, converts the content into text, and recognizes emotions.

[0460] Step 10:

[0461] The server generates appropriate advice based on the analysis results using an advice generation engine.

[0462] Based on the analysis results, the relevant knowledge base is referenced.

[0463] Select the appropriate advice template.

[0464] Generate specific advice sentences according to templates.

[0465] Customize the tone and content of your advice based on emotion.

[0466] Step 11:

[0467] Based on the consultation content, the server retrieves related products and services via the APIs of external e-commerce sites and health-related services.

[0468] Query the EC site API based on the keywords in the inquiry.

[0469] The acquired product information is filtered to select the most suitable product for the user.

[0470] Step 12:

[0471] The server transmits the generated advice and links to related products to the terminal.

[0472] Step 13:

[0473] The device displays the received advice and link to the user.

[0474] Step 14:

[0475] The user checks the advice displayed on the device and, if necessary, clicks on the provided link to check the product or service in detail and purchase it.

[0476] Step 15:

[0477] The server stores the user's consultation details and the advice provided in a database.

[0478] The user ID, consultation content, analysis results, and advice provided are saved as records.

[0479] Step 16:

[0480] The server periodically uses the data to update the AI ​​learning model.

[0481] Extract new training data from the saved database.

[0482] Retrain the AI ​​model to improve accuracy.

[0483] Push the updated AI model into production.

[0484] Example 2

[0485] 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."

[0486] Conventional consultation systems have difficulty providing advice that takes into account the user's emotional state, and lack efficient means for acquiring related products and services. As a result, users are unable to obtain appropriate support and information, which has led to problems that cannot contribute to improving their quality of life and health management.

[0487] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing the consultation content received from the user, means for generating appropriate advice based on the analysis result, means for acquiring related products and services based on the consultation content and the user's emotional state, means for providing the generated advice and the acquired products and services to the user, means for storing the consultation content from the user and the provided advice in a database, and means for improving the analysis algorithm based on the stored data and training the AI. This makes it possible to provide appropriate advice that takes the user's emotional state into consideration and efficiently acquire related products and services.

[0488] "User" refers to any individual or entity that uses the System.

[0489] "Consultation content" refers to the content of questions or concerns that users input or send to the system.

[0490] "Analysis" is a general term for information processing carried out to understand the content of a user's inquiry and generate an appropriate response.

[0491] "Advice" refers to suggestions or advice to the user that the system generates based on the analysis results.

[0492] The "emotional state" refers to the user's emotions that are estimated from the consultation content entered by the user.

[0493] "Goods and services" refer to goods and services related to the user's consultation content or emotional state.

[0494] "Database" refers to a system for systematically storing users' consultations, advice, and other related information.

[0495] A "natural language processing engine" refers to software technology for understanding and processing human language.

[0496] "Emotion engine" refers to software technology for analyzing emotions from user input.

[0497] "Tokenization" refers to the process of breaking text into words or other units.

[0498] "Part-of-speech tagging" refers to the process of assigning parts of speech (verbs, nouns, adjectives, etc.) to segmented words.

[0499] "Intention recognition" refers to the process of understanding the gist and purpose of the text entered by the user.

[0500] "API" refers to an interface for connecting with external systems.

[0501] "AI learning" refers to the process by which a system improves itself based on past data.

[0502] MODE FOR CARRYING OUT THE INVENTION

[0503] This invention relates to an AI consultation system that allows users to easily consult about mental, physical, and beauty-related issues. In particular, it features an emotion engine that provides appropriate advice based on the user's emotional state. Specific implementation methods for this system are described below.

[0504] Overall system configuration

[0505] This system is mainly composed of a server, a terminal, and a user interface.

[0506] User Registration and Login

[0507] A user accesses the system and registers by entering their name, email address, and password. The terminal collects this registration information and sends it to the server. The server saves the registration information in a database and generates a unique user ID. The user then logs in by entering their email address and password. The terminal sends the login information to the server, which verifies the information. If authentication is successful, the server sends the authentication result to the terminal, and the user can log in to the system.

[0508] Receiving and analyzing consultation details

[0509] After logging in, the user enters the content of the consultation into the text box and clicks the send button. Alternatively, the consultation content can be recorded and sent by voice. The device sends the entered text or voice data to the server. The server passes the received consultation content to a natural language processing engine (NLP engine) and emotion engine for analysis. At this time, the following process takes place:

[0510] Text tokenization: Splitting a sentence into words.

[0511] Part-of-speech tagging: Identifying the part of speech of each word.

[0512] Sentiment analysis: Analyze the emotional tone of the entire text.

[0513] Intention recognition: Identifying the main idea of ​​a sentence.

[0514] When there is voice input, it performs voice analysis, converts the content into text, and recognizes emotions.

[0515] Generating Advice

[0516] The server generates appropriate advice using an advice generation engine based on the analysis results. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice. Furthermore, the advice is customized based on the user's emotions recognized by the emotion engine.

[0517] Introducing related products and services

[0518] The server retrieves related products and services via APIs of external commercial transaction sites and health-related services based on the consultation content. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[0519] Providing advice and product links

[0520] The server sends the generated advice and links to related products and services to the terminal. The terminal displays the received advice and links to the user. The user can check the displayed advice and click the provided links to check the details of the products and services and purchase them.

[0521] Data storage and AI training

[0522] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to update the analysis algorithm and improve the accuracy of the advice.

[0523] Specific examples

[0524] Examples of stress consultations

[0525] 1. User: Enter text such as "I've been having trouble sleeping lately because of stress. Please tell me what to do." Or, enter voice such as "I'm so stressed, I don't know what to do."

[0526] 2. Terminal: Sends the consultation details to the server.

[0527] 3. Server:

[0528] Analysis: Identify stress and sleep problems.

[0529] Sentiment analysis: Identifying emotions such as stress and anxiety from user input.

[0530] Advice generation: Generate "We recommend you engage in relaxing activities or take supplements," and customize the advice to "First, try taking a deep breath and relaxing" based on the user's emotional state.

[0531] Get related services: Get links to stress relief related supplements.

[0532] 4. Server: Sends advice and product links to the device.

[0533] 5. Terminal: Display advice and links to the user.

[0534] 6. User: Reads the advice and clicks on the supplement link to purchase.

[0535] Skin care consultation example

[0536] 1. User: Enter text such as "I'm worried about my skin condition. Please tell me how to take care of it." Or enter voice such as "My skin condition has been getting worse recently and I don't know what to do."

[0537] 2. Terminal: Sends the consultation details to the server.

[0538] 3. Server:

[0539] Analysis: Identify skin care problems.

[0540] Sentiment analysis: Identifying feelings of confusion or anxiety from user input.

[0541] Advice generation: Generate "We recommend using skin care products that emphasize hydration," and customize it to "Don't worry, we recommend effective products" to ease confusion and anxiety.

[0542] Get related services: Get links to moisturizing skin care products.

[0543] 4. Server: Sends advice and product links to the device.

[0544] 5. Terminal: Display advice and links to the user.

[0545] 6. User: Reads the advice and clicks on the product link to purchase.

[0546] In this way, the system of the present invention improves the user's quality of life and health management by quickly and efficiently analyzing the user's consultation content and providing appropriate advice and related products and services that take into account the user's emotional state.

[0547] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0548] Step 1: User Registration

[0549] The user accesses the system's registration page and enters their name, email address, and password (input). When they click the Register button, the registration information is sent. The terminal sends the entered registration information to the server using an HTTP POST request (output).

[0550] Step 2: Save user information and generate a user ID

[0551] The server saves the received registration information in a database and generates a unique user ID (data processing). It performs the necessary database operations, and once the saving process is complete, it sends a registration completion notification including the user ID to the terminal (output). The terminal receives the notification and displays it to the user (operation).

[0552] Step 3: User Login

[0553] The user enters their email address and password on the login page and clicks the login button (input). The device sends the login information to the server using an HTTP POST request (output).

[0554] Step 4: Authentication process

[0555] The server compares the received login information with the information in the database and performs authentication processing (data calculation). If authentication is successful, it sends an authentication success message to the terminal and redirects the user to the homepage (output). Similarly, if authentication fails, it sends a failure message (operation).

[0556] Step 5: Enter your consultation details

[0557] After logging in, the user accesses the system's consultation page, enters the content of the consultation in the text box, and clicks the send button (input). Alternatively, the user can record the content of the consultation by voice and send it (action).

[0558] Step 6: Submit your consultation

[0559] The terminal sends the text or voice data entered by the user to the server using an HTTP POST request (output).

[0560] Step 7: Natural Language Processing Analysis

[0561] The server passes the received consultation content to a natural language processing engine for analysis (data calculation). Specifically, it performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition. Based on the analysis results, it proceeds to the next step (output).

[0562] Step 8: Sentiment Analysis

[0563] The server uses an emotion engine to analyze the emotional tone of the received consultation content (data calculation). For example, if the word "stress" appears frequently, it identifies emotions of anxiety and tension (output).

[0564] Step 9: Advice Generation

[0565] The server uses an advice generation engine to generate appropriate advice based on the results of natural language processing and sentiment analysis (data processing). It then references a knowledge base that matches the analyzed content and generates specific advice (output).

[0566] Step 10: Acquire related products and services

[0567] The server obtains related products and services based on the consultation details via APIs of external commercial transaction sites and health-related services (data calculation), and uses the APIs to obtain data and organize the necessary information (output).

[0568] Step 11: Provide advice and product links

[0569] The server sends the generated advice and links to related products and services to the terminal (output), and the terminal displays the received advice and links to the user (operation).

[0570] Step 12: User Verification and Purchase

[0571] The user can check the displayed advice and, if necessary, click on the provided link to check the product or service in detail and purchase it (action).

[0572] Step 13: Save your data

[0573] The server stores the user's consultation details and the advice provided in a database (data processing), which makes it possible to track the data history (output).

[0574] Step 14: Training the AI

[0575] The server periodically analyzes the stored data and uses it to train the AI ​​model (data calculation), and provides feedback based on the analysis results to generate advice for the next time and thereafter (operation).

[0576] The above are the specific processing steps of the program in the system of the present invention. This system makes it possible to quickly and efficiently analyze the content of the user's consultation and provide appropriate advice and related products and services that take into account the user's emotional state.

[0577] (Application example 2)

[0578] 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."

[0579] In modern society, many people are concerned about their physical and mental health and beauty. However, accessing experts who provide appropriate advice and services is not easy, and time and cost are also issues. In particular, there are only a limited number of systems that can provide personalized advice based on the user's emotional state. This has led to a demand for a system that allows users to receive dietary advice tailored to their emotional state and use food delivery options easily and quickly.

[0580] 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 analyzing the consultation content received from the user, means for generating appropriate advice based on the analysis results, means for acquiring related products and services based on the consultation content, means for providing the generated advice and the acquired products and services to the user, means for saving the consultation content from the user and the provided advice in a database, means for improving the analysis algorithm based on the saved data, and means for generating dietary advice based on the user's emotional state and suggesting related food delivery options. This allows the user to receive dietary advice that matches their emotional state and immediately use an appropriate food delivery service.

[0581] "User" refers to an individual or corporation that accesses the system and provides consultations regarding mind, body, and beauty.

[0582] "Consultation content" refers to questions and concerns about mind, body, and beauty that users input into the system.

[0583] "Analysis" refers to the process of analyzing the content of inquiries received from users using a natural language processing engine and a sentiment analysis engine.

[0584] "Advice" refers to specific suggestions or instructions that are generated based on the analysis results and provided to the user.

[0585] "Products and services" refers to related items and services that the system acquires and suggests based on the user's inquiry.

[0586] The "database" is a repository of information for storing the user's inquiries and the advice provided.

[0587] "Analysis algorithm" refers to the computational method the system uses to generate advice based on stored data.

[0588] "Emotional state" refers to the psychological state of the user analyzed by the emotion analysis engine.

[0589] "Dietary advice" refers to dietary suggestions provided based on the user's emotional state.

[0590] "Food Delivery Option" means a delivery service that provides suggested meals based on dietary advice.

[0591] MODE FOR CARRYING OUT THE INVENTION

[0592] A system for realizing the present invention comprises the following components:

[0593] User Registration and Login

[0594] A user first accesses the system and enters basic information such as name, email address, and password on the registration page, which generates a user ID and allows the user to log in to the system using their email address and password.

[0595] The terminal is responsible for transmitting the information entered by the user to the server, and upon successful login, access to the required system functions is granted.

[0596] The server stores user registration information in a database and performs authentication when users log in. This ensures that only authorized users can use the system.

[0597] Receiving and analyzing consultation details

[0598] After logging in, the user can input their concerns about their physical and mental condition or beauty into a text box or by voice input.

[0599] The terminal sends the user's input directly to the server, enabling real-time data processing.

[0600] The server analyzes the consultation using a natural language processing engine and a sentiment analysis engine. Specifically, it performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition. In the case of voice input, the voice data is converted to text and then the same analysis procedures are applied.

[0601] Generating Advice

[0602] The server generates dietary advice based on the analysis results, providing customized advice based on emotional state and suggesting suitable food delivery options.

[0603] Obtaining related products and services

[0604] The server obtains relevant product and service information from external commerce sites and health-related service application program interfaces.

[0605] Providing advice and services

[0606] The server transmits the generated advice and links to related products and services to the terminal.

[0607] The device displays advice and links to the user for immediate reference.

[0608] Storing data and improving AI algorithms

[0609] The server stores the user's consultation details and the advice provided in a database, and periodically uses this data to improve the analysis algorithm.

[0610] Specific examples

[0611] Example 1: Stress consultation

[0612] If a user types, "I've been stressed lately and can't sleep, so please tell me what foods will help me relax," the server receives this and, after performing sentiment analysis, generates advice such as, "I recommend some foods that will help you relax (e.g., lavender tea)." It also suggests relevant food delivery options, such as "lavender tea" from a "healthy cafe."

[0613] Example 2: Skin care consultation

[0614] If a user types, "My skin has been rough lately. Please tell me what kind of skin care products I should use," the server analyzes this and generates advice such as, "I recommend using skin care products that emphasize moisturizing," and also provides links to related skin care products.

[0615] This allows users to receive specific advice that matches their emotional state and use related products and services.

[0616] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0617] Step 1: User Registration

[0618] (User input) The user accesses the system and enters their name, email address, and password.

[0619] (Executed by the terminal) The terminal sends this input information to the server.

[0620] (Executed by the server) The server stores the received registration information in a database, generates a user ID, and returns confirmation of registration completion to the terminal.

[0621] Step 2: Log in

[0622] (User Input) The user attempts to log in by entering their email address and password.

[0623] (Executed by the terminal) The terminal sends the login information to the server.

[0624] (Executed by the server) The server compares the user information with that in the database and sends the authentication result to the terminal.

[0625] Step 3: Enter your consultation details

[0626] (User input) After logging in to the system, the user inputs the details of their mental and physical condition and beauty consultations via text or voice.

[0627] (Executed by the terminal) The terminal sends the entered consultation details to the server.

[0628] (Input) User's consultation content (text or voice)

[0629] (Output) The consultation content is sent to the server

[0630] Step 4: Analysis of consultation content

[0631] (Executed by the server) The server uses a natural language processing engine and a sentiment analysis engine to analyze the consultation content.

[0632] (Data processing) Tokenizing text, tagging parts of speech, analyzing sentiment, and recognizing intent.

[0633] (Input) User's consultation details

[0634] (Output) Analysis results (emotional state and purpose of consultation)

[0635] Step 5: Generating Advice

[0636] (Executed by the server) Based on the analysis results, the server generates customized advice that takes into account the emotional state.

[0637] (Data calculation) Advice is generated by referring to the analysis results and knowledge base.

[0638] (Input) Analysis results (emotional state and purpose of consultation)

[0639] (Output) The generated advice

[0640] Step 6: Acquire related products and services

[0641] (Executed by the server) The server uses APIs of external commerce sites and health-related services to obtain related products and services.

[0642] (Data Calculation) Acquisition of related products and services

[0643] (Input) Advice content

[0644] (Output) Links to related products and services

[0645] Step 7: Providing advice and links

[0646] The server (runs on the server) sends the generated advice and links to the acquired products and services to the terminal.

[0647] (Input) Generated advice and links to related products and services

[0648] (Output) Advice and links sent to your terminal

[0649] Step 8: Display to the User

[0650] (Executed by the terminal) The terminal displays the received advice and links to related products and services to the user.

[0651] (User confirms) The user confirms the advice and links provided.

[0652] (Input) Advice and links sent by the server

[0653] (Output) Visual display to the user

[0654] Step 9: Save and train data

[0655] (Executed by the server) The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and training of the AI ​​system.

[0656] (Input) User's consultation details and advice provided

[0657] (Output) Data saved to the database

[0658] Step 10: Improve the analysis algorithm

[0659] (Executed by the server) The server periodically uses the stored data to update the analysis algorithm and improve the accuracy of the system's advice.

[0660] (Data calculation) Past data analysis and model updates

[0661] (Input) Saved data from the database

[0662] (Output) Updated parsing algorithm

[0663] 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.

[0664] 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.

[0665] 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.

[0666] [Second embodiment]

[0667] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0668] 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.

[0669] 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).

[0670] 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.

[0671] 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.

[0672] 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).

[0673] 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.

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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."

[0679] This invention relates to an AI consultation desk system that allows users to easily consult about mental, physical, and beauty-related matters. We will explain in detail how various servers, terminals, and users interact to implement the system.

[0680] 1. User Registration and Login

[0681] User

[0682] First, the user accesses the system's registration page and enters information such as name, email address, and password. After entering the information, the user clicks the Register button to send the information from the terminal to the server. Once registration is complete, the user enters their email address and password and clicks the Login button to log in to the system.

[0683] Terminal

[0684] The terminal receives the registration information entered by the user and sends it to the server, as well as the login information.

[0685] server

[0686] The server stores the received registration information in a database and generates a user ID. When logging in, it compares the information with the database and sends the authentication result to the terminal.

[0687] 2. Receiving and analyzing consultation details

[0688] User

[0689] After logging in to the system, the user enters the content of the consultation in the text box and clicks the send button.

[0690] Terminal

[0691] The terminal transmits the consultation content input by the user to the server and waits for a response from the server.

[0692] server

[0693] The server passes the received consultation content to the natural language processing engine and executes the following processes:

[0694] 1. Text tokenization

[0695] 2. Part-of-Speech Tagging

[0696] 3. Emotion analysis

[0697] 4. Intention Recognition

[0698] 3. Generating Advice

[0699] server

[0700] Based on the analysis results, the server uses an advice generation engine to generate appropriate advice. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice.

[0701] 4. Introducing related products and services

[0702] server

[0703] Based on the consultation content, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[0704] 5. Providing advice and product links

[0705] server

[0706] The server transmits the generated advice and links to related products and services to the terminal.

[0707] Terminal

[0708] The terminal displays the received advice and links to the user.

[0709] User

[0710] The user can check the advice displayed on the device and, if necessary, click on the provided link to check the product or service in detail and purchase it.

[0711] 6. Data storage and AI training

[0712] server

[0713] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to improve the analysis algorithm and increase the accuracy of the advice.

[0714] Specific examples

[0715] Example 1: Stress consultation

[0716] 1. User: Enter "I've been having trouble sleeping lately because of stress. Please tell me what to do."

[0717] 2. Terminal: Sends the consultation details to the server.

[0718] 3. Server:

[0719] Analysis: Identify stress and sleep problems.

[0720] Advice generation: "We recommend relaxing activities and supplements."

[0721] Get related services: Get links to stress relief related supplements.

[0722] 4. Server: Sends advice and product links to the device.

[0723] 5. Terminal: Display advice and links to the user.

[0724] 6. User: Reads the advice and clicks on the supplement link to purchase.

[0725] Example 2: Skin care consultation

[0726] 1. User: Type "I'm worried about my skin. Please tell me how to take care of it."

[0727] 2. Terminal: Sends the consultation details to the server.

[0728] 3. Server:

[0729] Analysis: Identify skin care problems.

[0730] Advice generated: "We recommend using skin care products that emphasize moisturizing."

[0731] Get related services: Get links to moisturizing skin care products.

[0732] 4. Server: Sends advice and product links to the device.

[0733] 5. Terminal: Display advice and links to the user.

[0734] 6. User: Read the advice and click on the product link to purchase.

[0735] In this way, the system of the present invention allows users to easily consult with the doctor and quickly provides appropriate advice and related product suggestions, thereby greatly improving quality of life and health management.

[0736] The processing flow will be explained below.

[0737] Step 1:

[0738] A user accesses the system's registration page, enters their name, email address, and password, and then clicks the registration button to submit the information.

[0739] Step 2:

[0740] The terminal receives the registration information entered by the user and transmits it to the server.

[0741] Step 3:

[0742] The server stores the received registration information in a database, generates a user ID, and returns a message to the terminal indicating that the user registration was successful.

[0743] Step 4:

[0744] A user visits the login page, enters their email address and password, and clicks the login button.

[0745] Step 5:

[0746] The terminal sends the entered login information to the server.

[0747] Step 6:

[0748] The server checks the information in the database and sends the authentication result to the terminal. If the authentication is successful, the user can access the system.

[0749] Step 7:

[0750] After logging in, the user enters the content they want to consult about in the text box and clicks the send button.

[0751] Step 8:

[0752] The device sends the consultation content and user ID to the server.

[0753] Step 9:

[0754] The server passes the received consultation content to a natural language processing engine and analyzes the content.

[0755] Tokenize the text and split the sentence into words.

[0756] Part-of-speech tagging is performed to identify the part of speech of each word.

[0757] Sentiment analysis is performed to analyze the emotional tone of the entire text.

[0758] Perform intent recognition and identify the main idea of ​​the text.

[0759] Step 10:

[0760] The server generates appropriate advice based on the analysis results using an advice generation engine.

[0761] Based on the analysis results, the relevant knowledge base is referenced.

[0762] Select the appropriate advice template.

[0763] Generate specific advice sentences according to templates.

[0764] Step 11:

[0765] Based on the consultation content, the server retrieves related products and services via the APIs of external e-commerce sites and health-related services.

[0766] Query the EC site API based on the keywords in the inquiry.

[0767] The acquired product information is filtered to select the most suitable product for the user.

[0768] Step 12:

[0769] The server transmits the generated advice and links to related products to the terminal.

[0770] Step 13:

[0771] The device displays the received advice and link to the user.

[0772] Step 14:

[0773] The user checks the advice displayed on the device and, if necessary, clicks on the provided link to check the product or service in detail and purchase it.

[0774] Step 15:

[0775] The server stores the user's consultation details and the advice provided in a database.

[0776] The user ID, consultation content, analysis results, and advice provided are saved as records.

[0777] Step 16:

[0778] The server periodically uses the data to update the AI ​​learning model.

[0779] Extract new training data from the saved database.

[0780] Retrain the AI ​​model to improve accuracy.

[0781] Push the updated AI model into production.

[0782] In this way, the system of the present invention improves the user's quality of life and health management by quickly and efficiently analyzing the content of the user's consultation and providing appropriate advice and related products.

[0783] Example 1

[0784] 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."

[0785] With conventional consultation systems, even if users input their consultation details, it was difficult to provide appropriate advice in a short time. Furthermore, suggestions for related products and services were limited, and the overall system lacked integration, making it difficult to improve the user experience. Furthermore, there was no mechanism for improving the accuracy of the system by utilizing past consultation details and advice. This made it difficult to create a system that users could use with confidence.

[0786] 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.

[0787] In this invention, the server includes means for receiving and validating user registration information and storing it in a database, means for receiving login information and verifying it against the database for authentication, means for tokenizing text, tagging parts of speech, analyzing sentiment, and recognizing intent using a natural language processing engine, means for generating appropriate advice by referencing a knowledge base using an advice generation engine, and means for transmitting and displaying the advice and links to related products and services to the terminal. This makes it possible to provide prompt and appropriate advice in response to the consultation content entered by the user and suggest related products and services. In addition, the analysis algorithm can be improved based on the stored data, improving the accuracy of the entire system and the user experience.

[0788] "Means of receiving user registration information, validating it, and saving it in a database" is a function that obtains personal information and login information entered by the user, checks its accuracy and correctness of format, and then stores it in a database.

[0789] The "means for receiving login information and verifying it against a database" is a function that obtains login information provided by a user and compares it with existing data in a database to identify and authenticate the user.

[0790] A "natural language processing engine" is a software component that analyzes text data and performs information extraction and semantic analysis using techniques for understanding human language.

[0791] "Tokenization" is a natural language processing technique that analyzes sentences and text and breaks them down into smaller units (words and phrases).

[0792] "Part-of-speech tagging" is a technique for identifying the grammatical role (noun, verb, adjective, etc.) of each word in a sentence and assigning it a tag.

[0793] "Sentiment analysis" is a technology that reads the speaker's emotions and intentions from text data and classifies them into emotional categories such as positive, negative, and neutral.

[0794] "Intention recognition" is an analytical technique for understanding the intention and purpose of what a user says and determining the appropriate response or action.

[0795] The "advice generation engine" is a software component that generates optimal advice for the user based on the analyzed data.

[0796] A "knowledge base" is a database that systematically collects and stores specialized information and facts about a particular field, and is an information source that can be referenced by an advice generation engine.

[0797] A "terminal" is an input and output device such as a computer or smartphone that can be directly operated by a user.

[0798] The "means for sending and displaying links to related products and services" is a function for searching for related products and services based on the content of the user's consultation, and sending the link information to the user's terminal for display.

[0799] "Means for improving analysis algorithms based on stored data" refers to a function that stores past user consultation details and advice provided in a database, and uses that data to improve the algorithms in order to enhance the system's analytical capabilities.

[0800] This invention is an AI consultation system that allows users to easily consult about physical, mental, and beauty-related issues. This section explains in detail how various servers, terminals, and users interact to implement the system. The system performs a series of processes: user registration, reception and analysis of consultation details, generation of advice, introduction of related products and services, provision of advice and product links, data storage, and AI learning.

[0801] User Registration and Login

[0802] User

[0803] A user accesses the system's registration page, enters information such as name, email address, and password, and clicks the Register button. The user also enters an email address and password on the login page and clicks the Login button.

[0804] Terminal

[0805] The device receives the registration information and login information entered by the user, sends it to the server, receives the response from the server, and displays the result to the user.

[0806] server

[0807] The server deserializes the received registration information, validates it, and saves it in the database. In the case of login information, it compares it with the information in the database and sends the authentication result to the terminal.

[0808] Receiving and analyzing consultation details

[0809] User

[0810] After logging in, the user enters the content of the consultation in the text box and clicks the send button.

[0811] Terminal

[0812] The terminal receives the consultation content input by the user and transmits it to the server.

[0813] server

[0814] The server passes the received consultation content to a natural language processing engine (such as SpaCy or NLTK), which performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition.

[0815] Generating Advice

[0816] server

[0817] Based on the analysis results of the natural language processing engine, the server uses an advice generation engine to refer to a knowledge base and generate appropriate advice, such as "We recommend taking deep breaths to relax."

[0818] Introducing related products and services

[0819] server

[0820] Based on the consultation details, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, it uses the Amazon API to search for "relaxation supplements" and retrieves links to relevant products.

[0821] Providing advice and product links

[0822] server

[0823] The server transmits the generated advice and links to related products to the terminal.

[0824] Terminal

[0825] The terminal displays the received advice and links to the user.

[0826] User

[0827] The user checks the advice and links displayed on the device and, if necessary, clicks on the provided link to view more details.

[0828] Data storage and AI training

[0829] server

[0830] The server stores the user's consultation details and the advice provided in a database. The stored data is used to improve the analysis algorithm and for AI training. This data is periodically used to update the analysis algorithm and improve the accuracy of the entire system.

[0831] Specific examples

[0832] Example 1: Stress consultation

[0833] 1. User: Enter "I've been having trouble sleeping lately because of stress. Please tell me what to do."

[0834] 2. Terminal: Sends the consultation details to the server.

[0835] 3. Server:

[0836] Analysis: Identify stress and sleep problems.

[0837] Advice generation: "We recommend relaxing activities and supplements."

[0838] Get related services: Get links to stress relief related supplements.

[0839] 4. Server: Sends advice and product links to the device.

[0840] 5. Terminal: Display advice and links to the user.

[0841] 6. User: Reads the advice and clicks on the supplement link to purchase.

[0842] Example 2: Skin care consultation

[0843] 1. User: Type "I'm worried about my skin. Please tell me how to take care of it."

[0844] 2. Terminal: Sends the consultation details to the server.

[0845] 3. Server:

[0846] Analysis: Identify skin care problems.

[0847] Advice generated: "We recommend using skin care products that emphasize moisturizing."

[0848] Get related services: Get links to moisturizing skin care products.

[0849] 4. Server: Sends advice and product links to the device.

[0850] 5. Terminal: Display advice and links to the user.

[0851] 6. User: Read the advice and click on the product link to purchase.

[0852] Prompt Sentence Examples

[0853] 1. Registration prompt

[0854] Please visit the registration page and enter the required information to complete your registration. Enter your name, email address, and password and click the submit button.

[0855] 2. Consultation prompts

[0856] After logging in, enter the content you would like to discuss in the text box and click the send button. For example, you could enter, "I've been having trouble sleeping lately because of stress. Please tell me what to do."

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

[0858] Step 1: User Registration

[0859] (input)

[0860] The user accesses the system's registration page, enters their name, email address, and password, and clicks the registration button.

[0861] (process)

[0862] The device receives the entered registration information and sends it to the server in JSON format.

[0863] The server deserializes the received registration information and performs validation. Validation checks the format and duplication of the input data. The data is then saved to the database. After saving, a user ID is generated and a response indicating successful registration is sent to the terminal.

[0864] (output)

[0865] The user will see a message on their terminal confirming successful registration.

[0866] Step 2: User Login

[0867] (input)

[0868] The user enters their email address and password on the login page and clicks the login button.

[0869] (process)

[0870] The terminal receives the entered login information and sends it to the server.

[0871] The server deserializes the received login information and checks it against the registered information in the database. If authentication is successful, it generates an authentication token and sends it to the device. If authentication fails, it sends an error message.

[0872] (output)

[0873] The user will see a message on the terminal indicating whether the login was successful or unsuccessful.

[0874] Step 3: Enter your consultation details

[0875] (input)

[0876] After logging in, the user enters the content of their problem (e.g., "I can't sleep lately because of stress") into the text box and clicks the send button.

[0877] (process)

[0878] The terminal receives the entered text and sends it to the server.

[0879] The server passes the received text to a natural language processing engine (e.g., SpaCy, NLTK) that performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition, thereby extracting important information and sentiment from the text.

[0880] (output)

[0881] The server stores the analysis results internally and proceeds to the next step.

[0882] Step 4: Generating Advice

[0883] (input)

[0884] The server passes data to the advice generation engine based on the analysis results.

[0885] (process)

[0886] The advice generation engine refers to the knowledge base and generates appropriate advice, such as "We recommend taking deep breaths to relax."

[0887] (output)

[0888] The server keeps the generated advice internally and proceeds to the next step.

[0889] Step 5: Acquire related products and services

[0890] (input)

[0891] The server sends an API request to an external e-commerce site or health-related service to obtain related products or services based on the consultation content (e.g., stress management).

[0892] (process)

[0893] In response to a request, the external service returns information about related products or services (e.g., links to supplements).

[0894] The server receives this and stores it as internal data.

[0895] (output)

[0896] The server internally stores the links to the acquired products and services and proceeds to the next step.

[0897] Step 6: Provide advice and product links

[0898] (input)

[0899] The server integrates the generated advice with the retrieved product links.

[0900] (process)

[0901] The server transmits the consolidated information package to the terminal.

[0902] The terminal receives this and displays it to the user.

[0903] (output)

[0904] Users can view the advice and product links on their device and, if necessary, click on the provided link to view more details.

[0905] Step 7: Store the data and train the AI

[0906] (input)

[0907] The server stores all users' consultation details, advice provided, and user responses in a database.

[0908] (process)

[0909] Periodically, the server analyzes the stored data and performs a learning process to improve the accuracy of the analysis algorithms and advice generation engine.

[0910] (output)

[0911] Improved analysis algorithms and advice generation engines will result in more accurate responses to future users.

[0912] (Application example 1)

[0913] 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."

[0914] Improving the physical and mental health of workers and the working environment has become an important issue in modern factories. However, many workers have few easy access points to discuss their health and concerns, leading to continued stress and health problems. Furthermore, managers have few means of understanding workers' health status in real time, making it difficult to provide appropriate support. A system to resolve these issues and improve the working environment is needed.

[0915] 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.

[0916] In this invention, the server includes: means for analyzing the consultation content received from the user; means for generating appropriate advice based on the analysis results; means for acquiring related products and services based on the consultation content; means for providing the generated advice and the acquired products and services to the user; means for storing the consultation content from the user and the provided advice in a database; means for improving the analysis algorithm based on the stored data; means for receiving the consultation content from the user, the means including a robot that interacts with workers in the factory; and means for displaying the generated advice and the acquired products and services to workers in the factory. This allows workers to easily seek advice about their physical and mental health and work, and further enables the provision of appropriate advice and related products. Furthermore, managers can grasp the health status and consultation content of workers in real time, enabling them to provide prompt and appropriate support.

[0917] "Users" refer to factory workers who seek consultation through this system.

[0918] "Consultation content" is the text of worries and questions about mental and physical health and work that users input into the system.

[0919] The "analysis means" is a process of analyzing the input consultation content using a natural language processing engine, tokenizing the text, tagging parts of speech, analyzing emotions, and recognizing intent.

[0920] The "means for generating advice" is a process for extracting and generating appropriate advice from the knowledge base based on the analysis results.

[0921] "Means of obtaining related products and services" refers to the process of obtaining links to related products and services through the APIs of external e-commerce sites and health-related services based on the content of the consultation.

[0922] "Storage means" refers to the process of recording the user's consultation details and the advice provided in a database.

[0923] "Means for improving the analysis algorithm" refers to the process of periodically improving the analysis algorithm based on the stored data to improve the accuracy of advice.

[0924] A "robot that can interact with factory workers" is a robot that is placed in a factory, accepts inquiries from workers, and has the ability to return analysis results and advice in real time.

[0925] "Means for displaying to workers" refers to the process of visually providing generated advice and links to acquired goods and services to workers through a robot or other device.

[0926] This invention is an AI consultation system that allows factory workers to easily seek advice on mental, physical, and work-related issues. This paper explains in detail how various servers, terminals, and robots interact to implement the system.

[0927] 1. User Registration and Login

[0928] User:

[0929] A factory worker, who is a user, first accesses the system's registration page and enters information such as name, email address, and password. After entering the information, the user clicks the registration button to send the information from the terminal to the server. Once registration is complete, the user enters their email address and password and clicks the login button to log in to the system.

[0930] Device:

[0931] The terminal receives the registration information entered by the user and sends it to the server, as well as the login information.

[0932] server:

[0933] The server stores the received registration information in a database and generates a user ID. When logging in, it compares the information with the database and sends the authentication result to the terminal.

[0934] 2. Receiving and analyzing consultation details

[0935] User:

[0936] After logging in to the system, the user enters the content of the consultation in the text box and clicks the send button.

[0937] Device:

[0938] The terminal transmits the consultation content input by the user to the server and waits for a response from the server.

[0939] server:

[0940] The server passes the received consultation content to the natural language processing engine and executes the following processes:

[0941] 1. Text tokenization

[0942] 2. Part-of-Speech Tagging

[0943] 3. Emotion analysis

[0944] 4. Intention Recognition

[0945] 3. Generating Advice

[0946] server:

[0947] Based on the analysis results, the server uses an advice generation engine to generate appropriate advice. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice.

[0948] 4. Introducing related products and services

[0949] server:

[0950] Based on the consultation content, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[0951] 5. Providing advice and product links

[0952] server:

[0953] The server transmits the generated advice and links to related products and services to the terminal.

[0954] Device:

[0955] The terminal displays the received advice and links to the user.

[0956] 6. Data storage and AI training

[0957] server:

[0958] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to improve the analysis algorithm and increase the accuracy of the advice.

[0959] Specific examples

[0960] Usage example

[0961] Example 1: Stress consultation

[0962] User: Type "I've been having trouble sleeping lately because of stress. What can I do?"

[0963] Terminal: Sends the consultation details to the server.

[0964] server:

[0965] - Analysis: Identify stress and sleep problems.

[0966] - Advice generation: "We recommend relaxing activities and supplements."

[0967] - Get related services: Get links to stress relief related supplements.

[0968] Server: Sends advice and product links to the device.

[0969] Terminal: Display advice and links to users.

[0970] User: Reads the advice, clicks on the supplement link and purchases.

[0971] Example 2: Skin care consultation

[0972] User: Type "I'm worried about my skin. Please tell me how to take care of it."

[0973] Terminal: Sends the consultation details to the server.

[0974] server:

[0975] - Analysis: Identify skin care problems.

[0976] - Advice generation: "We recommend using skin care products that emphasize moisturizing."

[0977] - Get related services: Get links to moisturizing skin care products.

[0978] Server: Sends advice and product links to the device.

[0979] Terminal: Display advice and links to users.

[0980] User: Reads the advice and clicks on the product link to purchase.

[0981] In this way, the system of the present invention allows workers to easily seek advice on mental, physical, and work-related issues, and provides appropriate advice in real time and prompt recommendations for related products. The specific hardware used is a conversational robot installed in the factory, and the software used is a server using a natural language processing engine (spaCy or Transformers) and Flask. This is expected to significantly improve the working environment and improve worker health management.

[0982] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0983] Step 1:

[0984] User: Accesses the system's registration page and enters registration information such as name, email address, password, etc. After entering the information, clicks the registration button to send the entered data from the terminal to the server.

[0985] Step 2:

[0986] Terminal: Sends the registration information received from the user to the server and waits for a response from the server.

[0987] Step 3:

[0988] Server: Receives the registration information and stores it in the database. After storing it, it generates a user ID and returns a message to the device indicating that registration is complete.

[0989] Step 4:

[0990] User: After completing registration, log in to the system using your email address and password.

[0991] Step 5:

[0992] Device: Sends login information to the server and begins the authentication process.

[0993] Step 6:

[0994] Server: Compares the login information with the information in the database and sends the authentication result to the terminal.

[0995] Step 7:

[0996] User: After logging in to the system, enter the content of the consultation in the text box and click the send button.

[0997] Step 8:

[0998] Terminal: Sends the consultation content to the server and waits for a response from the server.

[0999] Step 9:

[1000] Server: Passes the received consultation content to the natural language processing engine and performs the following data processing and calculations:

[1001] Text tokenization: Splitting the conversation into words and phrases.

[1002] Part-of-speech tagging: Assigning a part-of-speech label to each word.

[1003] Sentiment analysis: Analyzes the sentiment of the consultation content and assigns sentiment labels such as positive, negative, or neutral.

[1004] Intent recognition: Identify the specific intent of the consultation (e.g., stress, skin care, etc.).

[1005] Step 10:

[1006] Server: Based on the analysis results, the advice generation engine extracts and generates appropriate advice from the knowledge base.

[1007] Step 11:

[1008] Server: To obtain related products and services based on the consultation details, the server calls the APIs of external e-commerce sites and health-related services to obtain related product links.

[1009] Step 12:

[1010] Server: Sends the generated advice and the obtained product link together to the device.

[1011] Step 13:

[1012] Terminal: Display received advice and links to the user.

[1013] Step 14:

[1014] User: Reads the advice and clicks on the provided links to learn more about the product or service and make a purchase if necessary.

[1015] Step 15:

[1016] Server: The content of the consultation and the advice provided are stored in a database and used as material for improving the analysis algorithm. The stored data is periodically analyzed to improve the accuracy of the algorithm.

[1017] 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.

[1018] This invention relates to an AI consultation system that allows users to easily consult about mental, physical, and beauty-related matters. In particular, it features an emotion engine that provides appropriate advice based on the user's emotional state.

[1019] 1. User Registration and Login

[1020] User

[1021] First, the user accesses the system's registration page and enters information such as name, email address, and password. After entering the information, the user clicks the Register button to send the information from the terminal to the server. Once registration is complete, the user enters their email address and password and clicks the Login button to log in to the system.

[1022] Terminal

[1023] The terminal receives the registration information entered by the user and sends it to the server, as well as the login information.

[1024] server

[1025] The server stores the received registration information in a database and generates a user ID. When logging in, it compares the information with the database and sends the authentication result to the terminal.

[1026] 2. Receiving and analyzing consultation details

[1027] User

[1028] After logging in to the system, users enter the content of their consultation in the text box and click the send button. If voice input is available, users can also record and send the content of their consultation by voice.

[1029] Terminal

[1030] The terminal transmits the consultation content input by the user to the server and waits for a response from the server.

[1031] server

[1032] The server passes the received consultation content to a natural language processing engine and an emotion engine, and analyzes the content.

[1033] Tokenize the text and split the sentence into words.

[1034] Part-of-speech tagging is performed to identify the part of speech of each word.

[1035] Sentiment analysis is performed to analyze the emotional tone of the entire text.

[1036] Perform intent recognition and identify the main idea of ​​the text.

[1037] When there is voice input, it performs voice analysis, converts the content into text, and recognizes emotions.

[1038] 3. Generating Advice

[1039] server

[1040] The server generates appropriate advice using an advice generation engine based on the analysis results. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice. The advice is customized based on the user's emotions recognized by the emotion engine.

[1041] 4. Introducing related products and services

[1042] server

[1043] Based on the consultation content, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[1044] 5. Providing advice and product links

[1045] server

[1046] The server transmits the generated advice and links to related products and services to the terminal.

[1047] Terminal

[1048] The terminal displays the received advice and links to the user.

[1049] User

[1050] The user can check the advice displayed on the device and, if necessary, click on the provided link to check the product or service in detail and purchase it.

[1051] 6. Data storage and AI training

[1052] server

[1053] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to update the analysis algorithm and improve the accuracy of the advice.

[1054] Specific examples

[1055] Example 1: Stress consultation

[1056] 1. User: Enter text such as "I've been having trouble sleeping lately because of stress. Please tell me what to do." Or, enter voice such as "I'm so stressed, I don't know what to do."

[1057] 2. Terminal: Sends the consultation details to the server.

[1058] 3. Server:

[1059] Analysis: Identify stress and sleep problems.

[1060] Sentiment analysis: Identifying emotions such as stress and anxiety from user input.

[1061] Advice generation: "We recommend you engage in relaxing activities or take supplements." Furthermore, the advice is customized to match the user's emotional state, such as "First, try taking a deep breath and relaxing."

[1062] Get related services: Get links to stress relief related supplements.

[1063] 4. Server: Sends advice and product links to the device.

[1064] 5. Terminal: Display advice and links to the user.

[1065] 6. User: Reads the advice and clicks on the supplement link to purchase.

[1066] Example 2: Skin care consultation

[1067] 1. User: Enter text such as "I'm worried about my skin condition. Please tell me how to take care of it." Or enter voice such as "My skin condition has been getting worse recently and I don't know what to do."

[1068] 2. Terminal: Sends the consultation details to the server.

[1069] 3. Server:

[1070] Analysis: Identify skin care problems.

[1071] Sentiment analysis: Identifying feelings of confusion or anxiety from user input.

[1072] Advice generation: "We recommend using skin care products that emphasize moisturizing." This can be further customized to ease confusion and anxiety by saying, "Don't worry, we'll recommend effective products."

[1073] Get related services: Get links to moisturizing skin care products.

[1074] 4. Server: Sends advice and product links to the device.

[1075] 5. Terminal: Display advice and links to the user.

[1076] 6. User: Read the advice and click on the product link to purchase.

[1077] In this way, the system of the present invention improves the user's quality of life and health management by quickly and efficiently analyzing the user's consultation content and providing appropriate advice and related products and services that take into account the user's emotional state.

[1078] The processing flow will be explained below.

[1079] Step 1:

[1080] A user accesses the system's registration page, enters their name, email address, and password, and then clicks the registration button to submit the information.

[1081] Step 2:

[1082] The terminal receives the registration information entered by the user and transmits it to the server.

[1083] Step 3:

[1084] The server stores the received registration information in a database, generates a user ID, and returns a message to the terminal indicating that the user registration was successful.

[1085] Step 4:

[1086] A user visits the login page, enters their email address and password, and clicks the login button.

[1087] Step 5:

[1088] The terminal sends the entered login information to the server.

[1089] Step 6:

[1090] The server checks the information in the database and sends the authentication result to the terminal. If the authentication is successful, the user can access the system.

[1091] Step 7:

[1092] After logging in, the user enters the content of the consultation in the text box and clicks the send button. If voice input is available, the user can also record the content of the consultation by voice and send it.

[1093] Step 8:

[1094] The device sends the consultation content and user ID to the server.

[1095] Step 9:

[1096] The server passes the received consultation content to a natural language processing engine and an emotion engine, and analyzes the content.

[1097] Tokenize the text and split the sentence into words.

[1098] Part-of-speech tagging is performed to identify the part of speech of each word.

[1099] Sentiment analysis is performed to identify the user's emotions from the input.

[1100] Perform intent recognition and identify the main idea of ​​the text.

[1101] When there is voice input, it performs voice analysis, converts the content into text, and recognizes emotions.

[1102] Step 10:

[1103] The server generates appropriate advice based on the analysis results using an advice generation engine.

[1104] Based on the analysis results, the relevant knowledge base is referenced.

[1105] Select the appropriate advice template.

[1106] Generate specific advice sentences according to templates.

[1107] Customize the tone and content of your advice based on emotion.

[1108] Step 11:

[1109] Based on the consultation content, the server retrieves related products and services via the APIs of external e-commerce sites and health-related services.

[1110] Query the EC site API based on the keywords in the inquiry.

[1111] The acquired product information is filtered to select the most suitable product for the user.

[1112] Step 12:

[1113] The server transmits the generated advice and links to related products to the terminal.

[1114] Step 13:

[1115] The device displays the received advice and link to the user.

[1116] Step 14:

[1117] The user checks the advice displayed on the device and, if necessary, clicks on the provided link to check the product or service in detail and purchase it.

[1118] Step 15:

[1119] The server stores the user's consultation details and the advice provided in a database.

[1120] The user ID, consultation content, analysis results, and advice provided are saved as records.

[1121] Step 16:

[1122] The server periodically uses the data to update the AI ​​learning model.

[1123] Extract new training data from the saved database.

[1124] Retrain the AI ​​model to improve accuracy.

[1125] Push the updated AI model into production.

[1126] Example 2

[1127] 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."

[1128] Conventional consultation systems have difficulty providing advice that takes into account the user's emotional state, and lack efficient means for acquiring related products and services. As a result, users are unable to obtain appropriate support and information, which has led to problems that cannot contribute to improving their quality of life and health management.

[1129] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing the consultation content received from the user, means for generating appropriate advice based on the analysis result, means for acquiring related products and services based on the consultation content and the user's emotional state, means for providing the generated advice and the acquired products and services to the user, means for storing the consultation content from the user and the provided advice in a database, and means for improving the analysis algorithm based on the stored data and training the AI. This makes it possible to provide appropriate advice that takes the user's emotional state into consideration and efficiently acquire related products and services.

[1130] "User" refers to any individual or entity that uses the System.

[1131] "Consultation content" refers to the content of questions or concerns that users input or send to the system.

[1132] "Analysis" is a general term for information processing carried out to understand the content of a user's inquiry and generate an appropriate response.

[1133] "Advice" refers to suggestions or advice to the user that the system generates based on the analysis results.

[1134] The "emotional state" refers to the user's emotions that are estimated from the consultation content entered by the user.

[1135] "Goods and services" refer to goods and services related to the user's consultation content or emotional state.

[1136] "Database" refers to a system for systematically storing users' consultations, advice, and other related information.

[1137] A "natural language processing engine" refers to software technology for understanding and processing human language.

[1138] "Emotion engine" refers to software technology for analyzing emotions from user input.

[1139] "Tokenization" refers to the process of breaking text into words or other units.

[1140] "Part-of-speech tagging" refers to the process of assigning parts of speech (verbs, nouns, adjectives, etc.) to segmented words.

[1141] "Intention recognition" refers to the process of understanding the gist and purpose of the text entered by the user.

[1142] "API" refers to an interface for connecting with external systems.

[1143] "AI learning" refers to the process by which a system improves itself based on past data.

[1144] MODE FOR CARRYING OUT THE INVENTION

[1145] This invention relates to an AI consultation system that allows users to easily consult about mental, physical, and beauty-related issues. In particular, it features an emotion engine that provides appropriate advice based on the user's emotional state. Specific implementation methods for this system are described below.

[1146] Overall system configuration

[1147] This system is mainly composed of a server, a terminal, and a user interface.

[1148] User Registration and Login

[1149] A user accesses the system and registers by entering their name, email address, and password. The terminal collects this registration information and sends it to the server. The server saves the registration information in a database and generates a unique user ID. The user then logs in by entering their email address and password. The terminal sends the login information to the server, which verifies the information. If authentication is successful, the server sends the authentication result to the terminal, and the user can log in to the system.

[1150] Receiving and analyzing consultation details

[1151] After logging in, the user enters the content of the consultation into the text box and clicks the send button. Alternatively, the consultation content can be recorded and sent by voice. The device sends the entered text or voice data to the server. The server passes the received consultation content to a natural language processing engine (NLP engine) and emotion engine for analysis. At this time, the following process takes place:

[1152] Text tokenization: Splitting a sentence into words.

[1153] Part-of-speech tagging: Identifying the part of speech of each word.

[1154] Sentiment analysis: Analyze the emotional tone of the entire text.

[1155] Intention recognition: Identifying the main idea of ​​a sentence.

[1156] When there is voice input, it performs voice analysis, converts the content into text, and recognizes emotions.

[1157] Generating Advice

[1158] The server generates appropriate advice using an advice generation engine based on the analysis results. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice. Furthermore, the advice is customized based on the user's emotions recognized by the emotion engine.

[1159] Introducing related products and services

[1160] The server retrieves related products and services via APIs of external commercial transaction sites and health-related services based on the consultation content. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[1161] Providing advice and product links

[1162] The server sends the generated advice and links to related products and services to the terminal. The terminal displays the received advice and links to the user. The user can check the displayed advice and click the provided links to check the details of the products and services and purchase them.

[1163] Data storage and AI training

[1164] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to update the analysis algorithm and improve the accuracy of the advice.

[1165] Specific examples

[1166] Examples of stress consultations

[1167] 1. User: Enter text such as "I've been having trouble sleeping lately because of stress. Please tell me what to do." Or, enter voice such as "I'm so stressed, I don't know what to do."

[1168] 2. Terminal: Sends the consultation details to the server.

[1169] 3. Server:

[1170] Analysis: Identify stress and sleep problems.

[1171] Sentiment analysis: Identifying emotions such as stress and anxiety from user input.

[1172] Advice generation: Generate "We recommend you engage in relaxing activities or take supplements," and customize the advice to "First, try taking a deep breath and relaxing" based on the user's emotional state.

[1173] Get related services: Get links to stress relief related supplements.

[1174] 4. Server: Sends advice and product links to the device.

[1175] 5. Terminal: Display advice and links to the user.

[1176] 6. User: Reads the advice and clicks on the supplement link to purchase.

[1177] Skin care consultation example

[1178] 1. User: Enter text such as "I'm worried about my skin condition. Please tell me how to take care of it." Or enter voice such as "My skin condition has been getting worse recently and I don't know what to do."

[1179] 2. Terminal: Sends the consultation details to the server.

[1180] 3. Server:

[1181] Analysis: Identify skin care problems.

[1182] Sentiment analysis: Identifying feelings of confusion or anxiety from user input.

[1183] Advice generation: Generate "We recommend using skin care products that emphasize hydration," and customize it to "Don't worry, we recommend effective products" to ease confusion and anxiety.

[1184] Get related services: Get links to moisturizing skin care products.

[1185] 4. Server: Sends advice and product links to the device.

[1186] 5. Terminal: Display advice and links to the user.

[1187] 6. User: Reads the advice and clicks on the product link to purchase.

[1188] In this way, the system of the present invention improves the user's quality of life and health management by quickly and efficiently analyzing the user's consultation content and providing appropriate advice and related products and services that take into account the user's emotional state.

[1189] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1190] Step 1: User Registration

[1191] The user accesses the system's registration page and enters their name, email address, and password (input). When they click the Register button, the registration information is sent. The terminal sends the entered registration information to the server using an HTTP POST request (output).

[1192] Step 2: Save user information and generate a user ID

[1193] The server saves the received registration information in a database and generates a unique user ID (data processing). It performs the necessary database operations, and once the saving process is complete, it sends a registration completion notification including the user ID to the terminal (output). The terminal receives the notification and displays it to the user (operation).

[1194] Step 3: User Login

[1195] The user enters their email address and password on the login page and clicks the login button (input). The device sends the login information to the server using an HTTP POST request (output).

[1196] Step 4: Authentication process

[1197] The server compares the received login information with the information in the database and performs authentication processing (data calculation). If authentication is successful, it sends an authentication success message to the terminal and redirects the user to the homepage (output). Similarly, if authentication fails, it sends a failure message (operation).

[1198] Step 5: Enter your consultation details

[1199] After logging in, the user accesses the system's consultation page, enters the content of the consultation in the text box, and clicks the send button (input). Alternatively, the user can record the content of the consultation by voice and send it (action).

[1200] Step 6: Submit your consultation

[1201] The terminal sends the text or voice data entered by the user to the server using an HTTP POST request (output).

[1202] Step 7: Natural Language Processing Analysis

[1203] The server passes the received consultation content to a natural language processing engine for analysis (data calculation). Specifically, it performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition. Based on the analysis results, it proceeds to the next step (output).

[1204] Step 8: Sentiment Analysis

[1205] The server uses an emotion engine to analyze the emotional tone of the received consultation content (data calculation). For example, if the word "stress" appears frequently, it identifies emotions of anxiety and tension (output).

[1206] Step 9: Advice Generation

[1207] The server uses an advice generation engine to generate appropriate advice based on the results of natural language processing and sentiment analysis (data processing). It then references a knowledge base that matches the analyzed content and generates specific advice (output).

[1208] Step 10: Acquire related products and services

[1209] The server obtains related products and services based on the consultation details via APIs of external commercial transaction sites and health-related services (data calculation), and uses the APIs to obtain data and organize the necessary information (output).

[1210] Step 11: Provide advice and product links

[1211] The server sends the generated advice and links to related products and services to the terminal (output), and the terminal displays the received advice and links to the user (operation).

[1212] Step 12: User Verification and Purchase

[1213] The user can check the displayed advice and, if necessary, click on the provided link to check the product or service in detail and purchase it (action).

[1214] Step 13: Save your data

[1215] The server stores the user's consultation details and the advice provided in a database (data processing), which makes it possible to track the data history (output).

[1216] Step 14: Training the AI

[1217] The server periodically analyzes the stored data and uses it to train the AI ​​model (data calculation), and provides feedback based on the analysis results to generate advice for the next time and thereafter (operation).

[1218] The above are the specific processing steps of the program in the system of the present invention. This system makes it possible to quickly and efficiently analyze the content of the user's consultation and provide appropriate advice and related products and services that take into account the user's emotional state.

[1219] (Application example 2)

[1220] 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."

[1221] In modern society, many people are concerned about their physical and mental health and beauty. However, accessing experts who provide appropriate advice and services is not easy, and time and cost are also issues. In particular, there are only a limited number of systems that can provide personalized advice based on the user's emotional state. This has led to a demand for a system that allows users to receive dietary advice tailored to their emotional state and use food delivery options easily and quickly.

[1222] 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 analyzing the consultation content received from the user, means for generating appropriate advice based on the analysis results, means for acquiring related products and services based on the consultation content, means for providing the generated advice and the acquired products and services to the user, means for saving the consultation content from the user and the provided advice in a database, means for improving the analysis algorithm based on the saved data, and means for generating dietary advice based on the user's emotional state and suggesting related food delivery options. This allows the user to receive dietary advice that matches their emotional state and immediately use an appropriate food delivery service.

[1223] "User" refers to an individual or corporation that accesses the system and provides consultations regarding mind, body, and beauty.

[1224] "Consultation content" refers to questions and concerns about mind, body, and beauty that users input into the system.

[1225] "Analysis" refers to the process of analyzing the content of inquiries received from users using a natural language processing engine and a sentiment analysis engine.

[1226] "Advice" refers to specific suggestions or instructions that are generated based on the analysis results and provided to the user.

[1227] "Products and services" refers to related items and services that the system acquires and suggests based on the user's inquiry.

[1228] The "database" is a repository of information for storing the user's inquiries and the advice provided.

[1229] "Analysis algorithm" refers to the computational method the system uses to generate advice based on stored data.

[1230] "Emotional state" refers to the psychological state of the user analyzed by the emotion analysis engine.

[1231] "Dietary advice" refers to dietary suggestions provided based on the user's emotional state.

[1232] "Food Delivery Option" means a delivery service that provides suggested meals based on dietary advice.

[1233] MODE FOR CARRYING OUT THE INVENTION

[1234] A system for realizing the present invention comprises the following components:

[1235] User Registration and Login

[1236] A user first accesses the system and enters basic information such as name, email address, and password on the registration page, which generates a user ID and allows the user to log in to the system using their email address and password.

[1237] The terminal is responsible for transmitting the information entered by the user to the server, and upon successful login, access to the required system functions is granted.

[1238] The server stores user registration information in a database and performs authentication when users log in. This ensures that only authorized users can use the system.

[1239] Receiving and analyzing consultation details

[1240] After logging in, the user can input their concerns about their physical and mental condition or beauty into a text box or by voice input.

[1241] The terminal sends the user's input directly to the server, enabling real-time data processing.

[1242] The server analyzes the consultation using a natural language processing engine and a sentiment analysis engine. Specifically, it performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition. In the case of voice input, the voice data is converted to text and then the same analysis procedures are applied.

[1243] Generating Advice

[1244] The server generates dietary advice based on the analysis results, providing customized advice based on emotional state and suggesting suitable food delivery options.

[1245] Obtaining related products and services

[1246] The server obtains relevant product and service information from external commerce sites and health-related service application program interfaces.

[1247] Providing advice and services

[1248] The server transmits the generated advice and links to related products and services to the terminal.

[1249] The device displays advice and links to the user for immediate reference.

[1250] Storing data and improving AI algorithms

[1251] The server stores the user's consultation details and the advice provided in a database, and periodically uses this data to improve the analysis algorithm.

[1252] Specific examples

[1253] Example 1: Stress consultation

[1254] If a user types, "I've been stressed lately and can't sleep, so please tell me what foods will help me relax," the server receives this and, after performing sentiment analysis, generates advice such as, "I recommend some foods that will help you relax (e.g., lavender tea)." It also suggests relevant food delivery options, such as "lavender tea" from a "healthy cafe."

[1255] Example 2: Skin care consultation

[1256] If a user types, "My skin has been rough lately. Please tell me what kind of skin care products I should use," the server analyzes this and generates advice such as, "I recommend using skin care products that emphasize moisturizing," and also provides links to related skin care products.

[1257] This allows users to receive specific advice that matches their emotional state and use related products and services.

[1258] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1259] Step 1: User Registration

[1260] (User input) The user accesses the system and enters their name, email address, and password.

[1261] (Executed by the terminal) The terminal sends this input information to the server.

[1262] (Executed by the server) The server stores the received registration information in a database, generates a user ID, and returns confirmation of registration completion to the terminal.

[1263] Step 2: Log in

[1264] (User Input) The user attempts to log in by entering their email address and password.

[1265] (Executed by the terminal) The terminal sends the login information to the server.

[1266] (Executed by the server) The server compares the user information with that in the database and sends the authentication result to the terminal.

[1267] Step 3: Enter your consultation details

[1268] (User input) After logging in to the system, the user inputs the details of their mental and physical condition and beauty consultations via text or voice.

[1269] (Executed by the terminal) The terminal sends the entered consultation details to the server.

[1270] (Input) User's consultation content (text or voice)

[1271] (Output) The consultation content is sent to the server

[1272] Step 4: Analysis of consultation content

[1273] (Executed by the server) The server uses a natural language processing engine and a sentiment analysis engine to analyze the consultation content.

[1274] (Data processing) Tokenizing text, tagging parts of speech, analyzing sentiment, and recognizing intent.

[1275] (Input) User's consultation details

[1276] (Output) Analysis results (emotional state and purpose of consultation)

[1277] Step 5: Generating Advice

[1278] (Executed by the server) Based on the analysis results, the server generates customized advice that takes into account the emotional state.

[1279] (Data calculation) Advice is generated by referring to the analysis results and knowledge base.

[1280] (Input) Analysis results (emotional state and purpose of consultation)

[1281] (Output) The generated advice

[1282] Step 6: Acquire related products and services

[1283] (Executed by the server) The server uses APIs of external commerce sites and health-related services to obtain related products and services.

[1284] (Data Calculation) Acquisition of related products and services

[1285] (Input) Advice content

[1286] (Output) Links to related products and services

[1287] Step 7: Providing advice and links

[1288] The server (runs on the server) sends the generated advice and links to the acquired products and services to the terminal.

[1289] (Input) Generated advice and links to related products and services

[1290] (Output) Advice and links sent to your terminal

[1291] Step 8: Display to the User

[1292] (Executed by the terminal) The terminal displays the received advice and links to related products and services to the user.

[1293] (User confirms) The user confirms the advice and links provided.

[1294] (Input) Advice and links sent by the server

[1295] (Output) Visual display to the user

[1296] Step 9: Save and train data

[1297] (Executed by the server) The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and training of the AI ​​system.

[1298] (Input) User's consultation details and advice provided

[1299] (Output) Data saved to the database

[1300] Step 10: Improve the analysis algorithm

[1301] (Executed by the server) The server periodically uses the stored data to update the analysis algorithm and improve the accuracy of the system's advice.

[1302] (Data calculation) Past data analysis and model updates

[1303] (Input) Saved data from the database

[1304] (Output) Updated parsing algorithm

[1305] 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.

[1306] 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.

[1307] 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.

[1308] [Third embodiment]

[1309] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1310] 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.

[1311] 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).

[1312] 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.

[1313] 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.

[1314] 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).

[1315] 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.

[1316] 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.

[1317] 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.

[1318] 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.

[1319] 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.

[1320] 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."

[1321] This invention relates to an AI consultation desk system that allows users to easily consult about mental, physical, and beauty-related matters. We will explain in detail how various servers, terminals, and users interact to implement the system.

[1322] 1. User Registration and Login

[1323] User

[1324] First, the user accesses the system's registration page and enters information such as name, email address, and password. After entering the information, the user clicks the Register button to send the information from the terminal to the server. Once registration is complete, the user enters their email address and password and clicks the Login button to log in to the system.

[1325] Terminal

[1326] The terminal receives the registration information entered by the user and sends it to the server, as well as the login information.

[1327] server

[1328] The server stores the received registration information in a database and generates a user ID. When logging in, it compares the information with the database and sends the authentication result to the terminal.

[1329] 2. Receiving and analyzing consultation details

[1330] User

[1331] After logging in to the system, the user enters the content of the consultation in the text box and clicks the send button.

[1332] Terminal

[1333] The terminal transmits the consultation content input by the user to the server and waits for a response from the server.

[1334] server

[1335] The server passes the received consultation content to the natural language processing engine and executes the following processes:

[1336] 1. Text tokenization

[1337] 2. Part-of-Speech Tagging

[1338] 3. Emotion analysis

[1339] 4. Intention Recognition

[1340] 3. Generating Advice

[1341] server

[1342] Based on the analysis results, the server uses an advice generation engine to generate appropriate advice. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice.

[1343] 4. Introducing related products and services

[1344] server

[1345] Based on the consultation content, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[1346] 5. Providing advice and product links

[1347] server

[1348] The server transmits the generated advice and links to related products and services to the terminal.

[1349] Terminal

[1350] The terminal displays the received advice and links to the user.

[1351] User

[1352] The user can check the advice displayed on the device and, if necessary, click on the provided link to check the product or service in detail and purchase it.

[1353] 6. Data storage and AI training

[1354] server

[1355] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to improve the analysis algorithm and increase the accuracy of the advice.

[1356] Specific examples

[1357] Example 1: Stress consultation

[1358] 1. User: Enter "I've been having trouble sleeping lately because of stress. Please tell me what to do."

[1359] 2. Terminal: Sends the consultation details to the server.

[1360] 3. Server:

[1361] Analysis: Identify stress and sleep problems.

[1362] Advice generation: "We recommend relaxing activities and supplements."

[1363] Get related services: Get links to stress relief related supplements.

[1364] 4. Server: Sends advice and product links to the device.

[1365] 5. Terminal: Display advice and links to the user.

[1366] 6. User: Reads the advice and clicks on the supplement link to purchase.

[1367] Example 2: Skin care consultation

[1368] 1. User: Type "I'm worried about my skin. Please tell me how to take care of it."

[1369] 2. Terminal: Sends the consultation details to the server.

[1370] 3. Server:

[1371] Analysis: Identify skin care problems.

[1372] Advice generated: "We recommend using skin care products that emphasize moisturizing."

[1373] Get related services: Get links to moisturizing skin care products.

[1374] 4. Server: Sends advice and product links to the device.

[1375] 5. Terminal: Display advice and links to the user.

[1376] 6. User: Read the advice and click on the product link to purchase.

[1377] In this way, the system of the present invention allows users to easily consult with the doctor and quickly provides appropriate advice and related product suggestions, thereby greatly improving quality of life and health management.

[1378] The processing flow will be explained below.

[1379] Step 1:

[1380] A user accesses the system's registration page, enters their name, email address, and password, and then clicks the registration button to submit the information.

[1381] Step 2:

[1382] The terminal receives the registration information entered by the user and transmits it to the server.

[1383] Step 3:

[1384] The server stores the received registration information in a database, generates a user ID, and returns a message to the terminal indicating that the user registration was successful.

[1385] Step 4:

[1386] A user visits the login page, enters their email address and password, and clicks the login button.

[1387] Step 5:

[1388] The terminal sends the entered login information to the server.

[1389] Step 6:

[1390] The server checks the information in the database and sends the authentication result to the terminal. If the authentication is successful, the user can access the system.

[1391] Step 7:

[1392] After logging in, the user enters the content they want to consult about in the text box and clicks the send button.

[1393] Step 8:

[1394] The device sends the consultation content and user ID to the server.

[1395] Step 9:

[1396] The server passes the received consultation content to a natural language processing engine and analyzes the content.

[1397] Tokenize the text and split the sentence into words.

[1398] Part-of-speech tagging is performed to identify the part of speech of each word.

[1399] Sentiment analysis is performed to analyze the emotional tone of the entire text.

[1400] Perform intent recognition and identify the main idea of ​​the text.

[1401] Step 10:

[1402] The server generates appropriate advice based on the analysis results using an advice generation engine.

[1403] Based on the analysis results, the relevant knowledge base is referenced.

[1404] Select the appropriate advice template.

[1405] Generate specific advice sentences according to templates.

[1406] Step 11:

[1407] Based on the consultation content, the server retrieves related products and services via the APIs of external e-commerce sites and health-related services.

[1408] Query the EC site API based on the keywords in the inquiry.

[1409] The acquired product information is filtered to select the most suitable product for the user.

[1410] Step 12:

[1411] The server transmits the generated advice and links to related products to the terminal.

[1412] Step 13:

[1413] The device displays the received advice and link to the user.

[1414] Step 14:

[1415] The user checks the advice displayed on the device and, if necessary, clicks on the provided link to check the product or service in detail and purchase it.

[1416] Step 15:

[1417] The server stores the user's consultation details and the advice provided in a database.

[1418] The user ID, consultation content, analysis results, and advice provided are saved as records.

[1419] Step 16:

[1420] The server periodically uses the data to update the AI ​​learning model.

[1421] Extract new training data from the saved database.

[1422] Retrain the AI ​​model to improve accuracy.

[1423] Push the updated AI model into production.

[1424] In this way, the system of the present invention improves the user's quality of life and health management by quickly and efficiently analyzing the content of the user's consultation and providing appropriate advice and related products.

[1425] Example 1

[1426] 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."

[1427] With conventional consultation systems, even if users input their consultation details, it was difficult to provide appropriate advice in a short time. Furthermore, suggestions for related products and services were limited, and the overall system lacked integration, making it difficult to improve the user experience. Furthermore, there was no mechanism for improving the accuracy of the system by utilizing past consultation details and advice. This made it difficult to create a system that users could use with confidence.

[1428] 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.

[1429] In this invention, the server includes means for receiving and validating user registration information and storing it in a database, means for receiving login information and verifying it against the database for authentication, means for tokenizing text, tagging parts of speech, analyzing sentiment, and recognizing intent using a natural language processing engine, means for generating appropriate advice by referencing a knowledge base using an advice generation engine, and means for transmitting and displaying the advice and links to related products and services to the terminal. This makes it possible to provide prompt and appropriate advice in response to the consultation content entered by the user and suggest related products and services. In addition, the analysis algorithm can be improved based on the stored data, improving the accuracy of the entire system and the user experience.

[1430] "Means of receiving user registration information, validating it, and saving it in a database" is a function that obtains personal information and login information entered by the user, checks its accuracy and correctness of format, and then stores it in a database.

[1431] The "means for receiving login information and verifying it against a database" is a function that obtains login information provided by a user and compares it with existing data in a database to identify and authenticate the user.

[1432] A "natural language processing engine" is a software component that analyzes text data and performs information extraction and semantic analysis using techniques for understanding human language.

[1433] "Tokenization" is a natural language processing technique that analyzes sentences and text and breaks them down into smaller units (words and phrases).

[1434] "Part-of-speech tagging" is a technique for identifying the grammatical role (noun, verb, adjective, etc.) of each word in a sentence and assigning it a tag.

[1435] "Sentiment analysis" is a technology that reads the speaker's emotions and intentions from text data and classifies them into emotional categories such as positive, negative, and neutral.

[1436] "Intention recognition" is an analytical technique for understanding the intention and purpose of what a user says and determining the appropriate response or action.

[1437] The "advice generation engine" is a software component that generates optimal advice for the user based on the analyzed data.

[1438] A "knowledge base" is a database that systematically collects and stores specialized information and facts about a particular field, and is an information source that can be referenced by an advice generation engine.

[1439] A "terminal" is an input and output device such as a computer or smartphone that can be directly operated by a user.

[1440] The "means for sending and displaying links to related products and services" is a function for searching for related products and services based on the content of the user's consultation, and sending the link information to the user's terminal for display.

[1441] "Means for improving analysis algorithms based on stored data" refers to a function that stores past user consultation details and advice provided in a database, and uses that data to improve the algorithms in order to enhance the system's analytical capabilities.

[1442] This invention is an AI consultation system that allows users to easily consult about physical, mental, and beauty-related issues. This section explains in detail how various servers, terminals, and users interact to implement the system. The system performs a series of processes: user registration, reception and analysis of consultation details, generation of advice, introduction of related products and services, provision of advice and product links, data storage, and AI learning.

[1443] User Registration and Login

[1444] User

[1445] A user accesses the system's registration page, enters information such as name, email address, and password, and clicks the Register button. The user also enters an email address and password on the login page and clicks the Login button.

[1446] Terminal

[1447] The device receives the registration information and login information entered by the user, sends it to the server, receives the response from the server, and displays the result to the user.

[1448] server

[1449] The server deserializes the received registration information, validates it, and saves it in the database. In the case of login information, it compares it with the information in the database and sends the authentication result to the terminal.

[1450] Receiving and analyzing consultation details

[1451] User

[1452] After logging in, the user enters the content of the consultation in the text box and clicks the send button.

[1453] Terminal

[1454] The terminal receives the consultation content input by the user and transmits it to the server.

[1455] server

[1456] The server passes the received consultation content to a natural language processing engine (such as SpaCy or NLTK), which performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition.

[1457] Generating Advice

[1458] server

[1459] Based on the analysis results of the natural language processing engine, the server uses an advice generation engine to refer to a knowledge base and generate appropriate advice, such as "We recommend taking deep breaths to relax."

[1460] Introducing related products and services

[1461] server

[1462] Based on the consultation details, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, it uses the Amazon API to search for "relaxation supplements" and retrieves links to relevant products.

[1463] Providing advice and product links

[1464] server

[1465] The server transmits the generated advice and links to related products to the terminal.

[1466] Terminal

[1467] The terminal displays the received advice and links to the user.

[1468] User

[1469] The user checks the advice and links displayed on the device and, if necessary, clicks on the provided link to view more details.

[1470] Data storage and AI training

[1471] server

[1472] The server stores the user's consultation details and the advice provided in a database. The stored data is used to improve the analysis algorithm and for AI training. This data is periodically used to update the analysis algorithm and improve the accuracy of the entire system.

[1473] Specific examples

[1474] Example 1: Stress consultation

[1475] 1. User: Enter "I've been having trouble sleeping lately because of stress. Please tell me what to do."

[1476] 2. Terminal: Sends the consultation details to the server.

[1477] 3. Server:

[1478] Analysis: Identify stress and sleep problems.

[1479] Advice generation: "We recommend relaxing activities and supplements."

[1480] Get related services: Get links to stress relief related supplements.

[1481] 4. Server: Sends advice and product links to the device.

[1482] 5. Terminal: Display advice and links to the user.

[1483] 6. User: Reads the advice and clicks on the supplement link to purchase.

[1484] Example 2: Skin care consultation

[1485] 1. User: Type "I'm worried about my skin. Please tell me how to take care of it."

[1486] 2. Terminal: Sends the consultation details to the server.

[1487] 3. Server:

[1488] Analysis: Identify skin care problems.

[1489] Advice generated: "We recommend using skin care products that emphasize moisturizing."

[1490] Get related services: Get links to moisturizing skin care products.

[1491] 4. Server: Sends advice and product links to the device.

[1492] 5. Terminal: Display advice and links to the user.

[1493] 6. User: Read the advice and click on the product link to purchase.

[1494] Prompt Sentence Examples

[1495] 1. Registration prompt

[1496] Please visit the registration page and enter the required information to complete your registration. Enter your name, email address, and password and click the submit button.

[1497] 2. Consultation prompts

[1498] After logging in, enter the content you would like to discuss in the text box and click the send button. For example, you could enter, "I've been having trouble sleeping lately because of stress. Please tell me what to do."

[1499] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1500] Step 1: User Registration

[1501] (input)

[1502] The user accesses the system's registration page, enters their name, email address, and password, and clicks the registration button.

[1503] (process)

[1504] The device receives the entered registration information and sends it to the server in JSON format.

[1505] The server deserializes the received registration information and performs validation. Validation checks the format and duplication of the input data. The data is then saved to the database. After saving, a user ID is generated and a response indicating successful registration is sent to the terminal.

[1506] (output)

[1507] The user will see a message on their terminal confirming successful registration.

[1508] Step 2: User Login

[1509] (input)

[1510] The user enters their email address and password on the login page and clicks the login button.

[1511] (process)

[1512] The terminal receives the entered login information and sends it to the server.

[1513] The server deserializes the received login information and checks it against the registered information in the database. If authentication is successful, it generates an authentication token and sends it to the device. If authentication fails, it sends an error message.

[1514] (output)

[1515] The user will see a message on the terminal indicating whether the login was successful or unsuccessful.

[1516] Step 3: Enter your consultation details

[1517] (input)

[1518] After logging in, the user enters the content of their problem (e.g., "I can't sleep lately because of stress") into the text box and clicks the send button.

[1519] (process)

[1520] The terminal receives the entered text and sends it to the server.

[1521] The server passes the received text to a natural language processing engine (e.g., SpaCy, NLTK) that performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition, thereby extracting important information and sentiment from the text.

[1522] (output)

[1523] The server stores the analysis results internally and proceeds to the next step.

[1524] Step 4: Generating Advice

[1525] (input)

[1526] The server passes data to the advice generation engine based on the analysis results.

[1527] (process)

[1528] The advice generation engine refers to the knowledge base and generates appropriate advice, such as "We recommend taking deep breaths to relax."

[1529] (output)

[1530] The server keeps the generated advice internally and proceeds to the next step.

[1531] Step 5: Acquire related products and services

[1532] (input)

[1533] The server sends an API request to an external e-commerce site or health-related service to obtain related products or services based on the consultation content (e.g., stress management).

[1534] (process)

[1535] In response to a request, the external service returns information about related products or services (e.g., links to supplements).

[1536] The server receives this and stores it as internal data.

[1537] (output)

[1538] The server internally stores the links to the acquired products and services and proceeds to the next step.

[1539] Step 6: Provide advice and product links

[1540] (input)

[1541] The server integrates the generated advice with the retrieved product links.

[1542] (process)

[1543] The server transmits the consolidated information package to the terminal.

[1544] The terminal receives this and displays it to the user.

[1545] (output)

[1546] Users can view the advice and product links on their device and, if necessary, click on the provided link to view more details.

[1547] Step 7: Store the data and train the AI

[1548] (input)

[1549] The server stores all users' consultation details, advice provided, and user responses in a database.

[1550] (process)

[1551] Periodically, the server analyzes the stored data and performs a learning process to improve the accuracy of the analysis algorithms and advice generation engine.

[1552] (output)

[1553] Improved analysis algorithms and advice generation engines will result in more accurate responses to future users.

[1554] (Application example 1)

[1555] 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."

[1556] Improving the physical and mental health of workers and the working environment has become an important issue in modern factories. However, many workers have few easy access points to discuss their health and concerns, leading to continued stress and health problems. Furthermore, managers have few means of understanding workers' health status in real time, making it difficult to provide appropriate support. A system to resolve these issues and improve the working environment is needed.

[1557] 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.

[1558] In this invention, the server includes: means for analyzing the consultation content received from the user; means for generating appropriate advice based on the analysis results; means for acquiring related products and services based on the consultation content; means for providing the generated advice and the acquired products and services to the user; means for storing the consultation content from the user and the provided advice in a database; means for improving the analysis algorithm based on the stored data; means for receiving the consultation content from the user, the means including a robot that interacts with workers in the factory; and means for displaying the generated advice and the acquired products and services to workers in the factory. This allows workers to easily seek advice about their physical and mental health and work, and further enables the provision of appropriate advice and related products. Furthermore, managers can grasp the health status and consultation content of workers in real time, enabling them to provide prompt and appropriate support.

[1559] "Users" refer to factory workers who seek consultation through this system.

[1560] "Consultation content" is the text of worries and questions about mental and physical health and work that users input into the system.

[1561] The "analysis means" is a process of analyzing the input consultation content using a natural language processing engine, tokenizing the text, tagging parts of speech, analyzing emotions, and recognizing intent.

[1562] The "means for generating advice" is a process for extracting and generating appropriate advice from the knowledge base based on the analysis results.

[1563] "Means of obtaining related products and services" refers to the process of obtaining links to related products and services through the APIs of external e-commerce sites and health-related services based on the content of the consultation.

[1564] "Storage means" refers to the process of recording the user's consultation details and the advice provided in a database.

[1565] "Means for improving the analysis algorithm" refers to the process of periodically improving the analysis algorithm based on the stored data to improve the accuracy of advice.

[1566] A "robot that can interact with factory workers" is a robot that is placed in a factory, accepts inquiries from workers, and has the ability to return analysis results and advice in real time.

[1567] "Means for displaying to workers" refers to the process of visually providing generated advice and links to acquired goods and services to workers through a robot or other device.

[1568] This invention is an AI consultation system that allows factory workers to easily seek advice on mental, physical, and work-related issues. This paper explains in detail how various servers, terminals, and robots interact to implement the system.

[1569] 1. User Registration and Login

[1570] User:

[1571] A factory worker, who is a user, first accesses the system's registration page and enters information such as name, email address, and password. After entering the information, the user clicks the registration button to send the information from the terminal to the server. Once registration is complete, the user enters their email address and password and clicks the login button to log in to the system.

[1572] Device:

[1573] The terminal receives the registration information entered by the user and sends it to the server, as well as the login information.

[1574] server:

[1575] The server stores the received registration information in a database and generates a user ID. When logging in, it compares the information with the database and sends the authentication result to the terminal.

[1576] 2. Receiving and analyzing consultation details

[1577] User:

[1578] After logging in to the system, the user enters the content of the consultation in the text box and clicks the send button.

[1579] Device:

[1580] The terminal transmits the consultation content input by the user to the server and waits for a response from the server.

[1581] server:

[1582] The server passes the received consultation content to the natural language processing engine and executes the following processes:

[1583] 1. Text tokenization

[1584] 2. Part-of-Speech Tagging

[1585] 3. Emotion analysis

[1586] 4. Intention Recognition

[1587] 3. Generating Advice

[1588] server:

[1589] Based on the analysis results, the server uses an advice generation engine to generate appropriate advice. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice.

[1590] 4. Introducing related products and services

[1591] server:

[1592] Based on the consultation content, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[1593] 5. Providing advice and product links

[1594] server:

[1595] The server transmits the generated advice and links to related products and services to the terminal.

[1596] Device:

[1597] The terminal displays the received advice and links to the user.

[1598] 6. Data storage and AI training

[1599] server:

[1600] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to improve the analysis algorithm and increase the accuracy of the advice.

[1601] Specific examples

[1602] Usage example

[1603] Example 1: Stress consultation

[1604] User: Type "I've been having trouble sleeping lately because of stress. What can I do?"

[1605] Terminal: Sends the consultation details to the server.

[1606] server:

[1607] - Analysis: Identify stress and sleep problems.

[1608] - Advice generation: "We recommend relaxing activities and supplements."

[1609] - Get related services: Get links to stress relief related supplements.

[1610] Server: Sends advice and product links to the device.

[1611] Terminal: Display advice and links to users.

[1612] User: Reads the advice, clicks on the supplement link and purchases.

[1613] Example 2: Skin care consultation

[1614] User: Type "I'm worried about my skin. Please tell me how to take care of it."

[1615] Terminal: Sends the consultation details to the server.

[1616] server:

[1617] - Analysis: Identify skin care problems.

[1618] - Advice generation: "We recommend using skin care products that emphasize moisturizing."

[1619] - Get related services: Get links to moisturizing skin care products.

[1620] Server: Sends advice and product links to the device.

[1621] Terminal: Display advice and links to users.

[1622] User: Reads the advice and clicks on the product link to purchase.

[1623] In this way, the system of the present invention allows workers to easily seek advice on mental, physical, and work-related issues, and provides appropriate advice in real time and prompt recommendations for related products. The specific hardware used is a conversational robot installed in the factory, and the software used is a server using a natural language processing engine (spaCy or Transformers) and Flask. This is expected to significantly improve the working environment and improve worker health management.

[1624] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1625] Step 1:

[1626] User: Accesses the system's registration page and enters registration information such as name, email address, password, etc. After entering the information, clicks the registration button to send the entered data from the terminal to the server.

[1627] Step 2:

[1628] Terminal: Sends the registration information received from the user to the server and waits for a response from the server.

[1629] Step 3:

[1630] Server: Receives the registration information and stores it in the database. After storing it, it generates a user ID and returns a message to the device indicating that registration is complete.

[1631] Step 4:

[1632] User: After completing registration, log in to the system using your email address and password.

[1633] Step 5:

[1634] Device: Sends login information to the server and begins the authentication process.

[1635] Step 6:

[1636] Server: Compares the login information with the information in the database and sends the authentication result to the terminal.

[1637] Step 7:

[1638] User: After logging in to the system, enter the content of the consultation in the text box and click the send button.

[1639] Step 8:

[1640] Terminal: Sends the consultation content to the server and waits for a response from the server.

[1641] Step 9:

[1642] Server: Passes the received consultation content to the natural language processing engine and performs the following data processing and calculations:

[1643] Text tokenization: Splitting the conversation into words and phrases.

[1644] Part-of-speech tagging: Assigning a part-of-speech label to each word.

[1645] Sentiment analysis: Analyzes the sentiment of the consultation content and assigns sentiment labels such as positive, negative, or neutral.

[1646] Intent recognition: Identify the specific intent of the consultation (e.g., stress, skin care, etc.).

[1647] Step 10:

[1648] Server: Based on the analysis results, the advice generation engine extracts and generates appropriate advice from the knowledge base.

[1649] Step 11:

[1650] Server: To obtain related products and services based on the consultation details, the server calls the APIs of external e-commerce sites and health-related services to obtain related product links.

[1651] Step 12:

[1652] Server: Sends the generated advice and the obtained product link together to the device.

[1653] Step 13:

[1654] Terminal: Display received advice and links to the user.

[1655] Step 14:

[1656] User: Reads the advice and clicks on the provided links to learn more about the product or service and make a purchase if necessary.

[1657] Step 15:

[1658] Server: The content of the consultation and the advice provided are stored in a database and used as material for improving the analysis algorithm. The stored data is periodically analyzed to improve the accuracy of the algorithm.

[1659] 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.

[1660] This invention relates to an AI consultation system that allows users to easily consult about mental, physical, and beauty-related matters. In particular, it features an emotion engine that provides appropriate advice based on the user's emotional state.

[1661] 1. User Registration and Login

[1662] User

[1663] First, the user accesses the system's registration page and enters information such as name, email address, and password. After entering the information, the user clicks the Register button to send the information from the terminal to the server. Once registration is complete, the user enters their email address and password and clicks the Login button to log in to the system.

[1664] Terminal

[1665] The terminal receives the registration information entered by the user and sends it to the server, as well as the login information.

[1666] server

[1667] The server stores the received registration information in a database and generates a user ID. When logging in, it compares the information with the database and sends the authentication result to the terminal.

[1668] 2. Receiving and analyzing consultation details

[1669] User

[1670] After logging in to the system, users enter the content of their consultation in the text box and click the send button. If voice input is available, users can also record and send the content of their consultation by voice.

[1671] Terminal

[1672] The terminal transmits the consultation content input by the user to the server and waits for a response from the server.

[1673] server

[1674] The server passes the received consultation content to a natural language processing engine and an emotion engine, and analyzes the content.

[1675] Tokenize the text and split the sentence into words.

[1676] Part-of-speech tagging is performed to identify the part of speech of each word.

[1677] Sentiment analysis is performed to analyze the emotional tone of the entire text.

[1678] Perform intent recognition and identify the main idea of ​​the text.

[1679] When there is voice input, it performs voice analysis, converts the content into text, and recognizes emotions.

[1680] 3. Generating Advice

[1681] server

[1682] The server generates appropriate advice using an advice generation engine based on the analysis results. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice. The advice is customized based on the user's emotions recognized by the emotion engine.

[1683] 4. Introducing related products and services

[1684] server

[1685] Based on the consultation content, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[1686] 5. Providing advice and product links

[1687] server

[1688] The server transmits the generated advice and links to related products and services to the terminal.

[1689] Terminal

[1690] The terminal displays the received advice and links to the user.

[1691] User

[1692] The user can check the advice displayed on the device and, if necessary, click on the provided link to check the product or service in detail and purchase it.

[1693] 6. Data storage and AI training

[1694] server

[1695] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to update the analysis algorithm and improve the accuracy of the advice.

[1696] Specific examples

[1697] Example 1: Stress consultation

[1698] 1. User: Enter text such as "I've been having trouble sleeping lately because of stress. Please tell me what to do." Or, enter voice such as "I'm so stressed, I don't know what to do."

[1699] 2. Terminal: Sends the consultation details to the server.

[1700] 3. Server:

[1701] Analysis: Identify stress and sleep problems.

[1702] Sentiment analysis: Identifying emotions such as stress and anxiety from user input.

[1703] Advice generation: "We recommend you engage in relaxing activities or take supplements." Furthermore, the advice is customized to match the user's emotional state, such as "First, try taking a deep breath and relaxing."

[1704] Get related services: Get links to stress relief related supplements.

[1705] 4. Server: Sends advice and product links to the device.

[1706] 5. Terminal: Display advice and links to the user.

[1707] 6. User: Reads the advice and clicks on the supplement link to purchase.

[1708] Example 2: Skin care consultation

[1709] 1. User: Enter text such as "I'm worried about my skin condition. Please tell me how to take care of it." Or enter voice such as "My skin condition has been getting worse recently and I don't know what to do."

[1710] 2. Terminal: Sends the consultation details to the server.

[1711] 3. Server:

[1712] Analysis: Identify skin care problems.

[1713] Sentiment analysis: Identifying feelings of confusion or anxiety from user input.

[1714] Advice generation: "We recommend using skin care products that emphasize moisturizing." This can be further customized to ease confusion and anxiety by saying, "Don't worry, we'll recommend effective products."

[1715] Get related services: Get links to moisturizing skin care products.

[1716] 4. Server: Sends advice and product links to the device.

[1717] 5. Terminal: Display advice and links to the user.

[1718] 6. User: Read the advice and click on the product link to purchase.

[1719] In this way, the system of the present invention improves the user's quality of life and health management by quickly and efficiently analyzing the user's consultation content and providing appropriate advice and related products and services that take into account the user's emotional state.

[1720] The processing flow will be explained below.

[1721] Step 1:

[1722] A user accesses the system's registration page, enters their name, email address, and password, and then clicks the registration button to submit the information.

[1723] Step 2:

[1724] The terminal receives the registration information entered by the user and transmits it to the server.

[1725] Step 3:

[1726] The server stores the received registration information in a database, generates a user ID, and returns a message to the terminal indicating that the user registration was successful.

[1727] Step 4:

[1728] A user visits the login page, enters their email address and password, and clicks the login button.

[1729] Step 5:

[1730] The terminal sends the entered login information to the server.

[1731] Step 6:

[1732] The server checks the information in the database and sends the authentication result to the terminal. If the authentication is successful, the user can access the system.

[1733] Step 7:

[1734] After logging in, the user enters the content of the consultation in the text box and clicks the send button. If voice input is available, the user can also record the content of the consultation by voice and send it.

[1735] Step 8:

[1736] The device sends the consultation content and user ID to the server.

[1737] Step 9:

[1738] The server passes the received consultation content to a natural language processing engine and an emotion engine, and analyzes the content.

[1739] Tokenize the text and split the sentence into words.

[1740] Part-of-speech tagging is performed to identify the part of speech of each word.

[1741] Sentiment analysis is performed to identify the user's emotions from the input.

[1742] Perform intent recognition and identify the main idea of ​​the text.

[1743] When there is voice input, it performs voice analysis, converts the content into text, and recognizes emotions.

[1744] Step 10:

[1745] The server generates appropriate advice based on the analysis results using an advice generation engine.

[1746] Based on the analysis results, the relevant knowledge base is referenced.

[1747] Select the appropriate advice template.

[1748] Generate specific advice sentences according to templates.

[1749] Customize the tone and content of your advice based on emotion.

[1750] Step 11:

[1751] Based on the consultation content, the server retrieves related products and services via the APIs of external e-commerce sites and health-related services.

[1752] Query the EC site API based on the keywords in the inquiry.

[1753] The acquired product information is filtered to select the most suitable product for the user.

[1754] Step 12:

[1755] The server transmits the generated advice and links to related products to the terminal.

[1756] Step 13:

[1757] The device displays the received advice and link to the user.

[1758] Step 14:

[1759] The user checks the advice displayed on the device and, if necessary, clicks on the provided link to check the product or service in detail and purchase it.

[1760] Step 15:

[1761] The server stores the user's consultation details and the advice provided in a database.

[1762] The user ID, consultation content, analysis results, and advice provided are saved as records.

[1763] Step 16:

[1764] The server periodically uses the data to update the AI ​​learning model.

[1765] Extract new training data from the saved database.

[1766] Retrain the AI ​​model to improve accuracy.

[1767] Push the updated AI model into production.

[1768] Example 2

[1769] 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."

[1770] Conventional consultation systems have difficulty providing advice that takes into account the user's emotional state, and lack efficient means for acquiring related products and services. As a result, users are unable to obtain appropriate support and information, which has led to problems that cannot contribute to improving their quality of life and health management.

[1771] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing the consultation content received from the user, means for generating appropriate advice based on the analysis result, means for acquiring related products and services based on the consultation content and the user's emotional state, means for providing the generated advice and the acquired products and services to the user, means for storing the consultation content from the user and the provided advice in a database, and means for improving the analysis algorithm based on the stored data and training the AI. This makes it possible to provide appropriate advice that takes the user's emotional state into consideration and efficiently acquire related products and services.

[1772] "User" refers to any individual or entity that uses the System.

[1773] "Consultation content" refers to the content of questions or concerns that users input or send to the system.

[1774] "Analysis" is a general term for information processing carried out to understand the content of a user's inquiry and generate an appropriate response.

[1775] "Advice" refers to suggestions or advice to the user that the system generates based on the analysis results.

[1776] The "emotional state" refers to the user's emotions that are estimated from the consultation content entered by the user.

[1777] "Goods and services" refer to goods and services related to the user's consultation content or emotional state.

[1778] "Database" refers to a system for systematically storing users' consultations, advice, and other related information.

[1779] A "natural language processing engine" refers to software technology for understanding and processing human language.

[1780] "Emotion engine" refers to software technology for analyzing emotions from user input.

[1781] "Tokenization" refers to the process of breaking text into words or other units.

[1782] "Part-of-speech tagging" refers to the process of assigning parts of speech (verbs, nouns, adjectives, etc.) to segmented words.

[1783] "Intention recognition" refers to the process of understanding the gist and purpose of the text entered by the user.

[1784] "API" refers to an interface for connecting with external systems.

[1785] "AI learning" refers to the process by which a system improves itself based on past data.

[1786] MODE FOR CARRYING OUT THE INVENTION

[1787] This invention relates to an AI consultation system that allows users to easily consult about mental, physical, and beauty-related issues. In particular, it features an emotion engine that provides appropriate advice based on the user's emotional state. Specific implementation methods for this system are described below.

[1788] Overall system configuration

[1789] This system is mainly composed of a server, a terminal, and a user interface.

[1790] User Registration and Login

[1791] A user accesses the system and registers by entering their name, email address, and password. The terminal collects this registration information and sends it to the server. The server saves the registration information in a database and generates a unique user ID. The user then logs in by entering their email address and password. The terminal sends the login information to the server, which verifies the information. If authentication is successful, the server sends the authentication result to the terminal, and the user can log in to the system.

[1792] Receiving and analyzing consultation details

[1793] After logging in, the user enters the content of the consultation into the text box and clicks the send button. Alternatively, the consultation content can be recorded and sent by voice. The device sends the entered text or voice data to the server. The server passes the received consultation content to a natural language processing engine (NLP engine) and emotion engine for analysis. At this time, the following process takes place:

[1794] Text tokenization: Splitting a sentence into words.

[1795] Part-of-speech tagging: Identifying the part of speech of each word.

[1796] Sentiment analysis: Analyze the emotional tone of the entire text.

[1797] Intention recognition: Identifying the main idea of ​​a sentence.

[1798] When there is voice input, it performs voice analysis, converts the content into text, and recognizes emotions.

[1799] Generating Advice

[1800] The server generates appropriate advice using an advice generation engine based on the analysis results. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice. Furthermore, the advice is customized based on the user's emotions recognized by the emotion engine.

[1801] Introducing related products and services

[1802] The server retrieves related products and services via APIs of external commercial transaction sites and health-related services based on the consultation content. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[1803] Providing advice and product links

[1804] The server sends the generated advice and links to related products and services to the terminal. The terminal displays the received advice and links to the user. The user can check the displayed advice and click the provided links to check the details of the products and services and purchase them.

[1805] Data storage and AI training

[1806] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to update the analysis algorithm and improve the accuracy of the advice.

[1807] Specific examples

[1808] Examples of stress consultations

[1809] 1. User: Enter text such as "I've been having trouble sleeping lately because of stress. Please tell me what to do." Or, enter voice such as "I'm so stressed, I don't know what to do."

[1810] 2. Terminal: Sends the consultation details to the server.

[1811] 3. Server:

[1812] Analysis: Identify stress and sleep problems.

[1813] Sentiment analysis: Identifying emotions such as stress and anxiety from user input.

[1814] Advice generation: Generate "We recommend you engage in relaxing activities or take supplements," and customize the advice to "First, try taking a deep breath and relaxing" based on the user's emotional state.

[1815] Get related services: Get links to stress relief related supplements.

[1816] 4. Server: Sends advice and product links to the device.

[1817] 5. Terminal: Display advice and links to the user.

[1818] 6. User: Reads the advice and clicks on the supplement link to purchase.

[1819] Skin care consultation example

[1820] 1. User: Enter text such as "I'm worried about my skin condition. Please tell me how to take care of it." Or enter voice such as "My skin condition has been getting worse recently and I don't know what to do."

[1821] 2. Terminal: Sends the consultation details to the server.

[1822] 3. Server:

[1823] Analysis: Identify skin care problems.

[1824] Sentiment analysis: Identifying feelings of confusion or anxiety from user input.

[1825] Advice generation: Generate "We recommend using skin care products that emphasize hydration," and customize it to "Don't worry, we recommend effective products" to ease confusion and anxiety.

[1826] Get related services: Get links to moisturizing skin care products.

[1827] 4. Server: Sends advice and product links to the device.

[1828] 5. Terminal: Display advice and links to the user.

[1829] 6. User: Reads the advice and clicks on the product link to purchase.

[1830] In this way, the system of the present invention improves the user's quality of life and health management by quickly and efficiently analyzing the user's consultation content and providing appropriate advice and related products and services that take into account the user's emotional state.

[1831] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1832] Step 1: User Registration

[1833] The user accesses the system's registration page and enters their name, email address, and password (input). When they click the Register button, the registration information is sent. The terminal sends the entered registration information to the server using an HTTP POST request (output).

[1834] Step 2: Save user information and generate a user ID

[1835] The server saves the received registration information in a database and generates a unique user ID (data processing). It performs the necessary database operations, and once the saving process is complete, it sends a registration completion notification including the user ID to the terminal (output). The terminal receives the notification and displays it to the user (operation).

[1836] Step 3: User Login

[1837] The user enters their email address and password on the login page and clicks the login button (input). The device sends the login information to the server using an HTTP POST request (output).

[1838] Step 4: Authentication process

[1839] The server compares the received login information with the information in the database and performs authentication processing (data calculation). If authentication is successful, it sends an authentication success message to the terminal and redirects the user to the homepage (output). Similarly, if authentication fails, it sends a failure message (operation).

[1840] Step 5: Enter your consultation details

[1841] After logging in, the user accesses the system's consultation page, enters the content of the consultation in the text box, and clicks the send button (input). Alternatively, the user can record the content of the consultation by voice and send it (action).

[1842] Step 6: Submit your consultation

[1843] The terminal sends the text or voice data entered by the user to the server using an HTTP POST request (output).

[1844] Step 7: Natural Language Processing Analysis

[1845] The server passes the received consultation content to a natural language processing engine for analysis (data calculation). Specifically, it performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition. Based on the analysis results, it proceeds to the next step (output).

[1846] Step 8: Sentiment Analysis

[1847] The server uses an emotion engine to analyze the emotional tone of the received consultation content (data calculation). For example, if the word "stress" appears frequently, it identifies emotions of anxiety and tension (output).

[1848] Step 9: Advice Generation

[1849] The server uses an advice generation engine to generate appropriate advice based on the results of natural language processing and sentiment analysis (data processing). It then references a knowledge base that matches the analyzed content and generates specific advice (output).

[1850] Step 10: Acquire related products and services

[1851] The server obtains related products and services based on the consultation details via APIs of external commercial transaction sites and health-related services (data calculation), and uses the APIs to obtain data and organize the necessary information (output).

[1852] Step 11: Provide advice and product links

[1853] The server sends the generated advice and links to related products and services to the terminal (output), and the terminal displays the received advice and links to the user (operation).

[1854] Step 12: User Verification and Purchase

[1855] The user can check the displayed advice and, if necessary, click on the provided link to check the product or service in detail and purchase it (action).

[1856] Step 13: Save your data

[1857] The server stores the user's consultation details and the advice provided in a database (data processing), which makes it possible to track the data history (output).

[1858] Step 14: Training the AI

[1859] The server periodically analyzes the stored data and uses it to train the AI ​​model (data calculation), and provides feedback based on the analysis results to generate advice for the next time and thereafter (operation).

[1860] The above are the specific processing steps of the program in the system of the present invention. This system makes it possible to quickly and efficiently analyze the content of the user's consultation and provide appropriate advice and related products and services that take into account the user's emotional state.

[1861] (Application example 2)

[1862] 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."

[1863] In modern society, many people are concerned about their physical and mental health and beauty. However, accessing experts who provide appropriate advice and services is not easy, and time and cost are also issues. In particular, there are only a limited number of systems that can provide personalized advice based on the user's emotional state. This has led to a demand for a system that allows users to receive dietary advice tailored to their emotional state and use food delivery options easily and quickly.

[1864] 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 analyzing the consultation content received from the user, means for generating appropriate advice based on the analysis results, means for acquiring related products and services based on the consultation content, means for providing the generated advice and the acquired products and services to the user, means for saving the consultation content from the user and the provided advice in a database, means for improving the analysis algorithm based on the saved data, and means for generating dietary advice based on the user's emotional state and suggesting related food delivery options. This allows the user to receive dietary advice that matches their emotional state and immediately use an appropriate food delivery service.

[1865] "User" refers to an individual or corporation that accesses the system and provides consultations regarding mind, body, and beauty.

[1866] "Consultation content" refers to questions and concerns about mind, body, and beauty that users input into the system.

[1867] "Analysis" refers to the process of analyzing the content of inquiries received from users using a natural language processing engine and a sentiment analysis engine.

[1868] "Advice" refers to specific suggestions or instructions that are generated based on the analysis results and provided to the user.

[1869] "Products and services" refers to related items and services that the system acquires and suggests based on the user's inquiry.

[1870] The "database" is a repository of information for storing the user's inquiries and the advice provided.

[1871] "Analysis algorithm" refers to the computational method the system uses to generate advice based on stored data.

[1872] "Emotional state" refers to the psychological state of the user analyzed by the emotion analysis engine.

[1873] "Dietary advice" refers to dietary suggestions provided based on the user's emotional state.

[1874] "Food Delivery Option" means a delivery service that provides suggested meals based on dietary advice.

[1875] MODE FOR CARRYING OUT THE INVENTION

[1876] A system for realizing the present invention comprises the following components:

[1877] User Registration and Login

[1878] A user first accesses the system and enters basic information such as name, email address, and password on the registration page, which generates a user ID and allows the user to log in to the system using their email address and password.

[1879] The terminal is responsible for transmitting the information entered by the user to the server, and upon successful login, access to the required system functions is granted.

[1880] The server stores user registration information in a database and performs authentication when users log in. This ensures that only authorized users can use the system.

[1881] Receiving and analyzing consultation details

[1882] After logging in, the user can input their concerns about their physical and mental condition or beauty into a text box or by voice input.

[1883] The terminal sends the user's input directly to the server, enabling real-time data processing.

[1884] The server analyzes the consultation using a natural language processing engine and a sentiment analysis engine. Specifically, it performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition. In the case of voice input, the voice data is converted to text and then the same analysis procedures are applied.

[1885] Generating Advice

[1886] The server generates dietary advice based on the analysis results, providing customized advice based on emotional state and suggesting suitable food delivery options.

[1887] Obtaining related products and services

[1888] The server obtains relevant product and service information from external commerce sites and health-related service application program interfaces.

[1889] Providing advice and services

[1890] The server transmits the generated advice and links to related products and services to the terminal.

[1891] The device displays advice and links to the user for immediate reference.

[1892] Storing data and improving AI algorithms

[1893] The server stores the user's consultation details and the advice provided in a database, and periodically uses this data to improve the analysis algorithm.

[1894] Specific examples

[1895] Example 1: Stress consultation

[1896] If a user types, "I've been stressed lately and can't sleep, so please tell me what foods will help me relax," the server receives this and, after performing sentiment analysis, generates advice such as, "I recommend some foods that will help you relax (e.g., lavender tea)." It also suggests relevant food delivery options, such as "lavender tea" from a "healthy cafe."

[1897] Example 2: Skin care consultation

[1898] If a user types, "My skin has been rough lately. Please tell me what kind of skin care products I should use," the server analyzes this and generates advice such as, "I recommend using skin care products that emphasize moisturizing," and also provides links to related skin care products.

[1899] This allows users to receive specific advice that matches their emotional state and use related products and services.

[1900] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1901] Step 1: User Registration

[1902] (User input) The user accesses the system and enters their name, email address, and password.

[1903] (Executed by the terminal) The terminal sends this input information to the server.

[1904] (Executed by the server) The server stores the received registration information in a database, generates a user ID, and returns confirmation of registration completion to the terminal.

[1905] Step 2: Log in

[1906] (User Input) The user attempts to log in by entering their email address and password.

[1907] (Executed by the terminal) The terminal sends the login information to the server.

[1908] (Executed by the server) The server compares the user information with that in the database and sends the authentication result to the terminal.

[1909] Step 3: Enter your consultation details

[1910] (User input) After logging in to the system, the user inputs the details of their mental and physical condition and beauty consultations via text or voice.

[1911] (Executed by the terminal) The terminal sends the entered consultation details to the server.

[1912] (Input) User's consultation content (text or voice)

[1913] (Output) The consultation content is sent to the server

[1914] Step 4: Analysis of consultation content

[1915] (Executed by the server) The server uses a natural language processing engine and a sentiment analysis engine to analyze the consultation content.

[1916] (Data processing) Tokenizing text, tagging parts of speech, analyzing sentiment, and recognizing intent.

[1917] (Input) User's consultation details

[1918] (Output) Analysis results (emotional state and purpose of consultation)

[1919] Step 5: Generating Advice

[1920] (Executed by the server) Based on the analysis results, the server generates customized advice that takes into account the emotional state.

[1921] (Data calculation) Advice is generated by referring to the analysis results and knowledge base.

[1922] (Input) Analysis results (emotional state and purpose of consultation)

[1923] (Output) The generated advice

[1924] Step 6: Acquire related products and services

[1925] (Executed by the server) The server uses APIs of external commerce sites and health-related services to obtain related products and services.

[1926] (Data Calculation) Acquisition of related products and services

[1927] (Input) Advice content

[1928] (Output) Links to related products and services

[1929] Step 7: Providing advice and links

[1930] The server (runs on the server) sends the generated advice and links to the acquired products and services to the terminal.

[1931] (Input) Generated advice and links to related products and services

[1932] (Output) Advice and links sent to your terminal

[1933] Step 8: Display to the User

[1934] (Executed by the terminal) The terminal displays the received advice and links to related products and services to the user.

[1935] (User confirms) The user confirms the advice and links provided.

[1936] (Input) Advice and links sent by the server

[1937] (Output) Visual display to the user

[1938] Step 9: Save and train data

[1939] (Executed by the server) The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and training of the AI ​​system.

[1940] (Input) User's consultation details and advice provided

[1941] (Output) Data saved to the database

[1942] Step 10: Improve the analysis algorithm

[1943] (Executed by the server) The server periodically uses the stored data to update the analysis algorithm and improve the accuracy of the system's advice.

[1944] (Data calculation) Past data analysis and model updates

[1945] (Input) Saved data from the database

[1946] (Output) Updated parsing algorithm

[1947] 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.

[1948] 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.

[1949] 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.

[1950] [Fourth embodiment]

[1951] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1952] 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.

[1953] 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).

[1954] 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.

[1955] 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.

[1956] 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).

[1957] 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.

[1958] 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.

[1959] 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.

[1960] 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.

[1961] 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.

[1962] 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.

[1963] 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."

[1964] This invention relates to an AI consultation desk system that allows users to easily consult about mental, physical, and beauty-related matters. We will explain in detail how various servers, terminals, and users interact to implement the system.

[1965] 1. User Registration and Login

[1966] User

[1967] First, the user accesses the system's registration page and enters information such as name, email address, and password. After entering the information, the user clicks the Register button to send the information from the terminal to the server. Once registration is complete, the user enters their email address and password and clicks the Login button to log in to the system.

[1968] Terminal

[1969] The terminal receives the registration information entered by the user and sends it to the server, as well as the login information.

[1970] server

[1971] The server stores the received registration information in a database and generates a user ID. When logging in, it compares the information with the database and sends the authentication result to the terminal.

[1972] 2. Receiving and analyzing consultation details

[1973] User

[1974] After logging in to the system, the user enters the content of the consultation in the text box and clicks the send button.

[1975] Terminal

[1976] The terminal transmits the consultation content input by the user to the server and waits for a response from the server.

[1977] server

[1978] The server passes the received consultation content to the natural language processing engine and executes the following processes:

[1979] 1. Text tokenization

[1980] 2. Part-of-Speech Tagging

[1981] 3. Emotion analysis

[1982] 4. Intention Recognition

[1983] 3. Generating Advice

[1984] server

[1985] Based on the analysis results, the server uses an advice generation engine to generate appropriate advice. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice.

[1986] 4. Introducing related products and services

[1987] server

[1988] Based on the consultation content, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[1989] 5. Providing advice and product links

[1990] server

[1991] The server transmits the generated advice and links to related products and services to the terminal.

[1992] Terminal

[1993] The terminal displays the received advice and links to the user.

[1994] User

[1995] The user can check the advice displayed on the device and, if necessary, click on the provided link to check the product or service in detail and purchase it.

[1996] 6. Data storage and AI training

[1997] server

[1998] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to improve the analysis algorithm and increase the accuracy of the advice.

[1999] Specific examples

[2000] Example 1: Stress consultation

[2001] 1. User: Enter "I've been having trouble sleeping lately because of stress. Please tell me what to do."

[2002] 2. Terminal: Sends the consultation details to the server.

[2003] 3. Server:

[2004] Analysis: Identify stress and sleep problems.

[2005] Advice generation: "We recommend relaxing activities and supplements."

[2006] Get related services: Get links to stress relief related supplements.

[2007] 4. Server: Sends advice and product links to the device.

[2008] 5. Terminal: Display advice and links to the user.

[2009] 6. User: Reads the advice and clicks on the supplement link to purchase.

[2010] Example 2: Skin care consultation

[2011] 1. User: Type "I'm worried about my skin. Please tell me how to take care of it."

[2012] 2. Terminal: Sends the consultation details to the server.

[2013] 3. Server:

[2014] Analysis: Identify skin care problems.

[2015] Advice generated: "We recommend using skin care products that emphasize moisturizing."

[2016] Get related services: Get links to moisturizing skin care products.

[2017] 4. Server: Sends advice and product links to the device.

[2018] 5. Terminal: Display advice and links to the user.

[2019] 6. User: Read the advice and click on the product link to purchase.

[2020] In this way, the system of the present invention allows users to easily consult with the doctor and quickly provides appropriate advice and related product suggestions, thereby greatly improving quality of life and health management.

[2021] The processing flow will be explained below.

[2022] Step 1:

[2023] A user accesses the system's registration page, enters their name, email address, and password, and then clicks the registration button to submit the information.

[2024] Step 2:

[2025] The terminal receives the registration information entered by the user and transmits it to the server.

[2026] Step 3:

[2027] The server stores the received registration information in a database, generates a user ID, and returns a message to the terminal indicating that the user registration was successful.

[2028] Step 4:

[2029] A user visits the login page, enters their email address and password, and clicks the login button.

[2030] Step 5:

[2031] The terminal sends the entered login information to the server.

[2032] Step 6:

[2033] The server checks the information in the database and sends the authentication result to the terminal. If the authentication is successful, the user can access the system.

[2034] Step 7:

[2035] After logging in, the user enters the content they want to consult about in the text box and clicks the send button.

[2036] Step 8:

[2037] The device sends the consultation content and user ID to the server.

[2038] Step 9:

[2039] The server passes the received consultation content to a natural language processing engine and analyzes the content.

[2040] Tokenize the text and split the sentence into words.

[2041] Part-of-speech tagging is performed to identify the part of speech of each word.

[2042] Sentiment analysis is performed to analyze the emotional tone of the entire text.

[2043] Perform intent recognition and identify the main idea of ​​the text.

[2044] Step 10:

[2045] The server generates appropriate advice based on the analysis results using an advice generation engine.

[2046] Based on the analysis results, the relevant knowledge base is referenced.

[2047] Select the appropriate advice template.

[2048] Generate specific advice sentences according to templates.

[2049] Step 11:

[2050] Based on the consultation content, the server retrieves related products and services via the APIs of external e-commerce sites and health-related services.

[2051] Query the EC site API based on the keywords in the inquiry.

[2052] The acquired product information is filtered to select the most suitable product for the user.

[2053] Step 12:

[2054] The server transmits the generated advice and links to related products to the terminal.

[2055] Step 13:

[2056] The device displays the received advice and link to the user.

[2057] Step 14:

[2058] The user checks the advice displayed on the device and, if necessary, clicks on the provided link to check the product or service in detail and purchase it.

[2059] Step 15:

[2060] The server stores the user's consultation details and the advice provided in a database.

[2061] The user ID, consultation content, analysis results, and advice provided are saved as records.

[2062] Step 16:

[2063] The server periodically uses the data to update the AI ​​learning model.

[2064] Extract new training data from the saved database.

[2065] Retrain the AI ​​model to improve accuracy.

[2066] Push the updated AI model into production.

[2067] In this way, the system of the present invention improves the user's quality of life and health management by quickly and efficiently analyzing the content of the user's consultation and providing appropriate advice and related products.

[2068] Example 1

[2069] 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."

[2070] With conventional consultation systems, even if users input their consultation details, it was difficult to provide appropriate advice in a short time. Furthermore, suggestions for related products and services were limited, and the overall system lacked integration, making it difficult to improve the user experience. Furthermore, there was no mechanism for improving the accuracy of the system by utilizing past consultation details and advice. This made it difficult to create a system that users could use with confidence.

[2071] 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.

[2072] In this invention, the server includes means for receiving and validating user registration information and storing it in a database, means for receiving login information and verifying it against the database for authentication, means for tokenizing text, tagging parts of speech, analyzing sentiment, and recognizing intent using a natural language processing engine, means for generating appropriate advice by referencing a knowledge base using an advice generation engine, and means for transmitting and displaying the advice and links to related products and services to the terminal. This makes it possible to provide prompt and appropriate advice in response to the consultation content entered by the user and suggest related products and services. In addition, the analysis algorithm can be improved based on the stored data, improving the accuracy of the entire system and the user experience.

[2073] "Means of receiving user registration information, validating it, and saving it in a database" is a function that obtains personal information and login information entered by the user, checks its accuracy and correctness of format, and then stores it in a database.

[2074] The "means for receiving login information and verifying it against a database" is a function that obtains login information provided by a user and compares it with existing data in a database to identify and authenticate the user.

[2075] A "natural language processing engine" is a software component that analyzes text data and performs information extraction and semantic analysis using techniques for understanding human language.

[2076] "Tokenization" is a natural language processing technique that analyzes sentences and text and breaks them down into smaller units (words and phrases).

[2077] "Part-of-speech tagging" is a technique for identifying the grammatical role (noun, verb, adjective, etc.) of each word in a sentence and assigning it a tag.

[2078] "Sentiment analysis" is a technology that reads the speaker's emotions and intentions from text data and classifies them into emotional categories such as positive, negative, and neutral.

[2079] "Intention recognition" is an analytical technique for understanding the intention and purpose of what a user says and determining the appropriate response or action.

[2080] The "advice generation engine" is a software component that generates optimal advice for the user based on the analyzed data.

[2081] A "knowledge base" is a database that systematically collects and stores specialized information and facts about a particular field, and is an information source that can be referenced by an advice generation engine.

[2082] A "terminal" is an input and output device such as a computer or smartphone that can be directly operated by a user.

[2083] The "means for sending and displaying links to related products and services" is a function for searching for related products and services based on the content of the user's consultation, and sending the link information to the user's terminal for display.

[2084] "Means for improving analysis algorithms based on stored data" refers to a function that stores past user consultation details and advice provided in a database, and uses that data to improve the algorithms in order to enhance the system's analytical capabilities.

[2085] This invention is an AI consultation system that allows users to easily consult about physical, mental, and beauty-related issues. This section explains in detail how various servers, terminals, and users interact to implement the system. The system performs a series of processes: user registration, reception and analysis of consultation details, generation of advice, introduction of related products and services, provision of advice and product links, data storage, and AI learning.

[2086] User Registration and Login

[2087] User

[2088] A user accesses the system's registration page, enters information such as name, email address, and password, and clicks the Register button. The user also enters an email address and password on the login page and clicks the Login button.

[2089] Terminal

[2090] The device receives the registration information and login information entered by the user, sends it to the server, receives the response from the server, and displays the result to the user.

[2091] server

[2092] The server deserializes the received registration information, validates it, and saves it in the database. In the case of login information, it compares it with the information in the database and sends the authentication result to the terminal.

[2093] Receiving and analyzing consultation details

[2094] User

[2095] After logging in, the user enters the content of the consultation in the text box and clicks the send button.

[2096] Terminal

[2097] The terminal receives the consultation content input by the user and transmits it to the server.

[2098] server

[2099] The server passes the received consultation content to a natural language processing engine (such as SpaCy or NLTK), which performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition.

[2100] Generating Advice

[2101] server

[2102] Based on the analysis results of the natural language processing engine, the server uses an advice generation engine to refer to a knowledge base and generate appropriate advice, such as "We recommend taking deep breaths to relax."

[2103] Introducing related products and services

[2104] server

[2105] Based on the consultation details, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, it uses the Amazon API to search for "relaxation supplements" and retrieves links to relevant products.

[2106] Providing advice and product links

[2107] server

[2108] The server transmits the generated advice and links to related products to the terminal.

[2109] Terminal

[2110] The terminal displays the received advice and links to the user.

[2111] User

[2112] The user checks the advice and links displayed on the device and, if necessary, clicks on the provided link to view more details.

[2113] Data storage and AI training

[2114] server

[2115] The server stores the user's consultation details and the advice provided in a database. The stored data is used to improve the analysis algorithm and for AI training. This data is periodically used to update the analysis algorithm and improve the accuracy of the entire system.

[2116] Specific examples

[2117] Example 1: Stress consultation

[2118] 1. User: Enter "I've been having trouble sleeping lately because of stress. Please tell me what to do."

[2119] 2. Terminal: Sends the consultation details to the server.

[2120] 3. Server:

[2121] Analysis: Identify stress and sleep problems.

[2122] Advice generation: "We recommend relaxing activities and supplements."

[2123] Get related services: Get links to stress relief related supplements.

[2124] 4. Server: Sends advice and product links to the device.

[2125] 5. Terminal: Display advice and links to the user.

[2126] 6. User: Reads the advice and clicks on the supplement link to purchase.

[2127] Example 2: Skin care consultation

[2128] 1. User: Type "I'm worried about my skin. Please tell me how to take care of it."

[2129] 2. Terminal: Sends the consultation details to the server.

[2130] 3. Server:

[2131] Analysis: Identify skin care problems.

[2132] Advice generated: "We recommend using skin care products that emphasize moisturizing."

[2133] Get related services: Get links to moisturizing skin care products.

[2134] 4. Server: Sends advice and product links to the device.

[2135] 5. Terminal: Display advice and links to the user.

[2136] 6. User: Read the advice and click on the product link to purchase.

[2137] Prompt Sentence Examples

[2138] 1. Registration prompt

[2139] Please visit the registration page and enter the required information to complete your registration. Enter your name, email address, and password and click the submit button.

[2140] 2. Consultation prompts

[2141] After logging in, enter the content you would like to discuss in the text box and click the send button. For example, you could enter, "I've been having trouble sleeping lately because of stress. Please tell me what to do."

[2142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2143] Step 1: User Registration

[2144] (input)

[2145] The user accesses the system's registration page, enters their name, email address, and password, and clicks the registration button.

[2146] (process)

[2147] The device receives the entered registration information and sends it to the server in JSON format.

[2148] The server deserializes the received registration information and performs validation. Validation checks the format and duplication of the input data. The data is then saved to the database. After saving, a user ID is generated and a response indicating successful registration is sent to the terminal.

[2149] (output)

[2150] The user will see a message on their terminal confirming successful registration.

[2151] Step 2: User Login

[2152] (input)

[2153] The user enters their email address and password on the login page and clicks the login button.

[2154] (process)

[2155] The terminal receives the entered login information and sends it to the server.

[2156] The server deserializes the received login information and checks it against the registered information in the database. If authentication is successful, it generates an authentication token and sends it to the device. If authentication fails, it sends an error message.

[2157] (output)

[2158] The user will see a message on the terminal indicating whether the login was successful or unsuccessful.

[2159] Step 3: Enter your consultation details

[2160] (input)

[2161] After logging in, the user enters the content of their problem (e.g., "I can't sleep lately because of stress") into the text box and clicks the send button.

[2162] (process)

[2163] The terminal receives the entered text and sends it to the server.

[2164] The server passes the received text to a natural language processing engine (e.g., SpaCy, NLTK) that performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition, thereby extracting important information and sentiment from the text.

[2165] (output)

[2166] The server stores the analysis results internally and proceeds to the next step.

[2167] Step 4: Generating Advice

[2168] (input)

[2169] The server passes data to the advice generation engine based on the analysis results.

[2170] (process)

[2171] The advice generation engine refers to the knowledge base and generates appropriate advice, such as "We recommend taking deep breaths to relax."

[2172] (output)

[2173] The server keeps the generated advice internally and proceeds to the next step.

[2174] Step 5: Acquire related products and services

[2175] (input)

[2176] The server sends an API request to an external e-commerce site or health-related service to obtain related products or services based on the consultation content (e.g., stress management).

[2177] (process)

[2178] In response to a request, the external service returns information about related products or services (e.g., links to supplements).

[2179] The server receives this and stores it as internal data.

[2180] (output)

[2181] The server internally stores the links to the acquired products and services and proceeds to the next step.

[2182] Step 6: Provide advice and product links

[2183] (input)

[2184] The server integrates the generated advice with the retrieved product links.

[2185] (process)

[2186] The server transmits the consolidated information package to the terminal.

[2187] The terminal receives this and displays it to the user.

[2188] (output)

[2189] Users can view the advice and product links on their device and, if necessary, click on the provided link to view more details.

[2190] Step 7: Store the data and train the AI

[2191] (input)

[2192] The server stores all users' consultation details, advice provided, and user responses in a database.

[2193] (process)

[2194] Periodically, the server analyzes the stored data and performs a learning process to improve the accuracy of the analysis algorithms and advice generation engine.

[2195] (output)

[2196] Improved analysis algorithms and advice generation engines will result in more accurate responses to future users.

[2197] (Application example 1)

[2198] 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."

[2199] Improving the physical and mental health of workers and the working environment has become an important issue in modern factories. However, many workers have few easy access points to discuss their health and concerns, leading to continued stress and health problems. Furthermore, managers have few means of understanding workers' health status in real time, making it difficult to provide appropriate support. A system to resolve these issues and improve the working environment is needed.

[2200] 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.

[2201] In this invention, the server includes: means for analyzing the consultation content received from the user; means for generating appropriate advice based on the analysis results; means for acquiring related products and services based on the consultation content; means for providing the generated advice and the acquired products and services to the user; means for storing the consultation content from the user and the provided advice in a database; means for improving the analysis algorithm based on the stored data; means for receiving the consultation content from the user, the means including a robot that interacts with workers in the factory; and means for displaying the generated advice and the acquired products and services to workers in the factory. This allows workers to easily seek advice about their physical and mental health and work, and further enables the provision of appropriate advice and related products. Furthermore, managers can grasp the health status and consultation content of workers in real time, enabling them to provide prompt and appropriate support.

[2202] "Users" refer to factory workers who seek consultation through this system.

[2203] "Consultation content" is the text of worries and questions about mental and physical health and work that users input into the system.

[2204] The "analysis means" is a process of analyzing the input consultation content using a natural language processing engine, tokenizing the text, tagging parts of speech, analyzing emotions, and recognizing intent.

[2205] The "means for generating advice" is a process for extracting and generating appropriate advice from the knowledge base based on the analysis results.

[2206] "Means of obtaining related products and services" refers to the process of obtaining links to related products and services through the APIs of external e-commerce sites and health-related services based on the content of the consultation.

[2207] "Storage means" refers to the process of recording the user's consultation details and the advice provided in a database.

[2208] "Means for improving the analysis algorithm" refers to the process of periodically improving the analysis algorithm based on the stored data to improve the accuracy of advice.

[2209] A "robot that can interact with factory workers" is a robot that is placed in a factory, accepts inquiries from workers, and has the ability to return analysis results and advice in real time.

[2210] "Means for displaying to workers" refers to the process of visually providing generated advice and links to acquired goods and services to workers through a robot or other device.

[2211] This invention is an AI consultation system that allows factory workers to easily seek advice on mental, physical, and work-related issues. This paper explains in detail how various servers, terminals, and robots interact to implement the system.

[2212] 1. User Registration and Login

[2213] User:

[2214] A factory worker, who is a user, first accesses the system's registration page and enters information such as name, email address, and password. After entering the information, the user clicks the registration button to send the information from the terminal to the server. Once registration is complete, the user enters their email address and password and clicks the login button to log in to the system.

[2215] Device:

[2216] The terminal receives the registration information entered by the user and sends it to the server, as well as the login information.

[2217] server:

[2218] The server stores the received registration information in a database and generates a user ID. When logging in, it compares the information with the database and sends the authentication result to the terminal.

[2219] 2. Receiving and analyzing consultation details

[2220] User:

[2221] After logging in to the system, the user enters the content of the consultation in the text box and clicks the send button.

[2222] Device:

[2223] The terminal transmits the consultation content input by the user to the server and waits for a response from the server.

[2224] server:

[2225] The server passes the received consultation content to the natural language processing engine and executes the following processes:

[2226] 1. Text tokenization

[2227] 2. Part-of-Speech Tagging

[2228] 3. Emotion analysis

[2229] 4. Intention Recognition

[2230] 3. Generating Advice

[2231] server:

[2232] Based on the analysis results, the server uses an advice generation engine to generate appropriate advice. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice.

[2233] 4. Introducing related products and services

[2234] server:

[2235] Based on the consultation content, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[2236] 5. Providing advice and product links

[2237] server:

[2238] The server transmits the generated advice and links to related products and services to the terminal.

[2239] Device:

[2240] The terminal displays the received advice and links to the user.

[2241] 6. Data storage and AI training

[2242] server:

[2243] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to improve the analysis algorithm and increase the accuracy of the advice.

[2244] Specific examples

[2245] Usage example

[2246] Example 1: Stress consultation

[2247] User: Type "I've been having trouble sleeping lately because of stress. What can I do?"

[2248] Terminal: Sends the consultation details to the server.

[2249] server:

[2250] - Analysis: Identify stress and sleep problems.

[2251] - Advice generation: "We recommend relaxing activities and supplements."

[2252] - Get related services: Get links to stress relief related supplements.

[2253] Server: Sends advice and product links to the device.

[2254] Terminal: Display advice and links to users.

[2255] User: Reads the advice, clicks on the supplement link and purchases.

[2256] Example 2: Skin care consultation

[2257] User: Type "I'm worried about my skin. Please tell me how to take care of it."

[2258] Terminal: Sends the consultation details to the server.

[2259] server:

[2260] - Analysis: Identify skin care problems.

[2261] - Advice generation: "We recommend using skin care products that emphasize moisturizing."

[2262] - Get related services: Get links to moisturizing skin care products.

[2263] Server: Sends advice and product links to the device.

[2264] Terminal: Display advice and links to users.

[2265] User: Reads the advice and clicks on the product link to purchase.

[2266] In this way, the system of the present invention allows workers to easily seek advice on mental, physical, and work-related issues, and provides appropriate advice in real time and prompt recommendations for related products. The specific hardware used is a conversational robot installed in the factory, and the software used is a server using a natural language processing engine (spaCy or Transformers) and Flask. This is expected to significantly improve the working environment and improve worker health management.

[2267] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2268] Step 1:

[2269] User: Accesses the system's registration page and enters registration information such as name, email address, password, etc. After entering the information, clicks the registration button to send the entered data from the terminal to the server.

[2270] Step 2:

[2271] Terminal: Sends the registration information received from the user to the server and waits for a response from the server.

[2272] Step 3:

[2273] Server: Receives the registration information and stores it in the database. After storing it, it generates a user ID and returns a message to the device indicating that registration is complete.

[2274] Step 4:

[2275] User: After completing registration, log in to the system using your email address and password.

[2276] Step 5:

[2277] Device: Sends login information to the server and begins the authentication process.

[2278] Step 6:

[2279] Server: Compares the login information with the information in the database and sends the authentication result to the terminal.

[2280] Step 7:

[2281] User: After logging in to the system, enter the content of the consultation in the text box and click the send button.

[2282] Step 8:

[2283] Terminal: Sends the consultation content to the server and waits for a response from the server.

[2284] Step 9:

[2285] Server: Passes the received consultation content to the natural language processing engine and performs the following data processing and calculations:

[2286] Text tokenization: Splitting the conversation into words and phrases.

[2287] Part-of-speech tagging: Assigning a part-of-speech label to each word.

[2288] Sentiment analysis: Analyzes the sentiment of the consultation content and assigns sentiment labels such as positive, negative, or neutral.

[2289] Intent recognition: Identify the specific intent of the consultation (e.g., stress, skin care, etc.).

[2290] Step 10:

[2291] Server: Based on the analysis results, the advice generation engine extracts and generates appropriate advice from the knowledge base.

[2292] Step 11:

[2293] Server: To obtain related products and services based on the consultation details, the server calls the APIs of external e-commerce sites and health-related services to obtain related product links.

[2294] Step 12:

[2295] Server: Sends the generated advice and the obtained product link together to the device.

[2296] Step 13:

[2297] Terminal: Display received advice and links to the user.

[2298] Step 14:

[2299] User: Reads the advice and clicks on the provided links to learn more about the product or service and make a purchase if necessary.

[2300] Step 15:

[2301] Server: The content of the consultation and the advice provided are stored in a database and used as material for improving the analysis algorithm. The stored data is periodically analyzed to improve the accuracy of the algorithm.

[2302] 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.

[2303] This invention relates to an AI consultation system that allows users to easily consult about mental, physical, and beauty-related matters. In particular, it features an emotion engine that provides appropriate advice based on the user's emotional state.

[2304] 1. User Registration and Login

[2305] User

[2306] First, the user accesses the system's registration page and enters information such as name, email address, and password. After entering the information, the user clicks the Register button to send the information from the terminal to the server. Once registration is complete, the user enters their email address and password and clicks the Login button to log in to the system.

[2307] Terminal

[2308] The terminal receives the registration information entered by the user and sends it to the server, as well as the login information.

[2309] server

[2310] The server stores the received registration information in a database and generates a user ID. When logging in, it compares the information with the database and sends the authentication result to the terminal.

[2311] 2. Receiving and analyzing consultation details

[2312] User

[2313] After logging in to the system, users enter the content of their consultation in the text box and click the send button. If voice input is available, users can also record and send the content of their consultation by voice.

[2314] Terminal

[2315] The terminal transmits the consultation content input by the user to the server and waits for a response from the server.

[2316] server

[2317] The server passes the received consultation content to a natural language processing engine and an emotion engine, and analyzes the content.

[2318] Tokenize the text and split the sentence into words.

[2319] Part-of-speech tagging is performed to identify the part of speech of each word.

[2320] Sentiment analysis is performed to analyze the emotional tone of the entire text.

[2321] Perform intent recognition and identify the main idea of ​​the text.

[2322] When there is voice input, it performs voice analysis, converts the content into text, and recognizes emotions.

[2323] 3. Generating Advice

[2324] server

[2325] The server generates appropriate advice using an advice generation engine based on the analysis results. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice. The advice is customized based on the user's emotions recognized by the emotion engine.

[2326] 4. Introducing related products and services

[2327] server

[2328] Based on the consultation content, the server retrieves related products and services via APIs of external e-commerce sites and health-related services. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[2329] 5. Providing advice and product links

[2330] server

[2331] The server transmits the generated advice and links to related products and services to the terminal.

[2332] Terminal

[2333] The terminal displays the received advice and links to the user.

[2334] User

[2335] The user can check the advice displayed on the device and, if necessary, click on the provided link to check the product or service in detail and purchase it.

[2336] 6. Data storage and AI training

[2337] server

[2338] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to update the analysis algorithm and improve the accuracy of the advice.

[2339] Specific examples

[2340] Example 1: Stress consultation

[2341] 1. User: Enter text such as "I've been having trouble sleeping lately because of stress. Please tell me what to do." Or, enter voice such as "I'm so stressed, I don't know what to do."

[2342] 2. Terminal: Sends the consultation details to the server.

[2343] 3. Server:

[2344] Analysis: Identify stress and sleep problems.

[2345] Sentiment analysis: Identifying emotions such as stress and anxiety from user input.

[2346] Advice generation: "We recommend you engage in relaxing activities or take supplements." Furthermore, the advice is customized to match the user's emotional state, such as "First, try taking a deep breath and relaxing."

[2347] Get related services: Get links to stress relief related supplements.

[2348] 4. Server: Sends advice and product links to the device.

[2349] 5. Terminal: Display advice and links to the user.

[2350] 6. User: Reads the advice and clicks on the supplement link to purchase.

[2351] Example 2: Skin care consultation

[2352] 1. User: Enter text such as "I'm worried about my skin condition. Please tell me how to take care of it." Or enter voice such as "My skin condition has been getting worse recently and I don't know what to do."

[2353] 2. Terminal: Sends the consultation details to the server.

[2354] 3. Server:

[2355] Analysis: Identify skin care problems.

[2356] Sentiment analysis: Identifying feelings of confusion or anxiety from user input.

[2357] Advice generation: "We recommend using skin care products that emphasize moisturizing." This can be further customized to ease confusion and anxiety by saying, "Don't worry, we'll recommend effective products."

[2358] Get related services: Get links to moisturizing skin care products.

[2359] 4. Server: Sends advice and product links to the device.

[2360] 5. Terminal: Display advice and links to the user.

[2361] 6. User: Read the advice and click on the product link to purchase.

[2362] In this way, the system of the present invention improves the user's quality of life and health management by quickly and efficiently analyzing the user's consultation content and providing appropriate advice and related products and services that take into account the user's emotional state.

[2363] The processing flow will be explained below.

[2364] Step 1:

[2365] A user accesses the system's registration page, enters their name, email address, and password, and then clicks the registration button to submit the information.

[2366] Step 2:

[2367] The terminal receives the registration information entered by the user and transmits it to the server.

[2368] Step 3:

[2369] The server stores the received registration information in a database, generates a user ID, and returns a message to the terminal indicating that the user registration was successful.

[2370] Step 4:

[2371] A user visits the login page, enters their email address and password, and clicks the login button.

[2372] Step 5:

[2373] The terminal sends the entered login information to the server.

[2374] Step 6:

[2375] The server checks the information in the database and sends the authentication result to the terminal. If the authentication is successful, the user can access the system.

[2376] Step 7:

[2377] After logging in, the user enters the content of the consultation in the text box and clicks the send button. If voice input is available, the user can also record the content of the consultation by voice and send it.

[2378] Step 8:

[2379] The device sends the consultation content and user ID to the server.

[2380] Step 9:

[2381] The server passes the received consultation content to a natural language processing engine and an emotion engine, and analyzes the content.

[2382] Tokenize the text and split the sentence into words.

[2383] Part-of-speech tagging is performed to identify the part of speech of each word.

[2384] Sentiment analysis is performed to identify the user's emotions from the input.

[2385] Perform intent recognition and identify the main idea of ​​the text.

[2386] When there is voice input, it performs voice analysis, converts the content into text, and recognizes emotions.

[2387] Step 10:

[2388] The server generates appropriate advice based on the analysis results using an advice generation engine.

[2389] Based on the analysis results, the relevant knowledge base is referenced.

[2390] Select the appropriate advice template.

[2391] Generate specific advice sentences according to templates.

[2392] Customize the tone and content of your advice based on emotion.

[2393] Step 11:

[2394] Based on the consultation content, the server retrieves related products and services via the APIs of external e-commerce sites and health-related services.

[2395] Query the EC site API based on the keywords in the inquiry.

[2396] The acquired product information is filtered to select the most suitable product for the user.

[2397] Step 12:

[2398] The server transmits the generated advice and links to related products to the terminal.

[2399] Step 13:

[2400] The device displays the received advice and link to the user.

[2401] Step 14:

[2402] The user checks the advice displayed on the device and, if necessary, clicks on the provided link to check the product or service in detail and purchase it.

[2403] Step 15:

[2404] The server stores the user's consultation details and the advice provided in a database.

[2405] The user ID, consultation content, analysis results, and advice provided are saved as records.

[2406] Step 16:

[2407] The server periodically uses the data to update the AI ​​learning model.

[2408] Extract new training data from the saved database.

[2409] Retrain the AI ​​model to improve accuracy.

[2410] Push the updated AI model into production.

[2411] Example 2

[2412] 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."

[2413] Conventional consultation systems have difficulty providing advice that takes into account the user's emotional state, and lack efficient means for acquiring related products and services. As a result, users are unable to obtain appropriate support and information, which has led to problems that cannot contribute to improving their quality of life and health management.

[2414] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for analyzing the consultation content received from the user, means for generating appropriate advice based on the analysis result, means for acquiring related products and services based on the consultation content and the user's emotional state, means for providing the generated advice and the acquired products and services to the user, means for storing the consultation content from the user and the provided advice in a database, and means for improving the analysis algorithm based on the stored data and training the AI. This makes it possible to provide appropriate advice that takes the user's emotional state into consideration and efficiently acquire related products and services.

[2415] "User" refers to any individual or entity that uses the System.

[2416] "Consultation content" refers to the content of questions or concerns that users input or send to the system.

[2417] "Analysis" is a general term for information processing carried out to understand the content of a user's inquiry and generate an appropriate response.

[2418] "Advice" refers to suggestions or advice to the user that the system generates based on the analysis results.

[2419] The "emotional state" refers to the user's emotions that are estimated from the consultation content entered by the user.

[2420] "Goods and services" refer to goods and services related to the user's consultation content or emotional state.

[2421] "Database" refers to a system for systematically storing users' consultations, advice, and other related information.

[2422] A "natural language processing engine" refers to software technology for understanding and processing human language.

[2423] "Emotion engine" refers to software technology for analyzing emotions from user input.

[2424] "Tokenization" refers to the process of breaking text into words or other units.

[2425] "Part-of-speech tagging" refers to the process of assigning parts of speech (verbs, nouns, adjectives, etc.) to segmented words.

[2426] "Intention recognition" refers to the process of understanding the gist and purpose of the text entered by the user.

[2427] "API" refers to an interface for connecting with external systems.

[2428] "AI learning" refers to the process by which a system improves itself based on past data.

[2429] MODE FOR CARRYING OUT THE INVENTION

[2430] This invention relates to an AI consultation system that allows users to easily consult about mental, physical, and beauty-related issues. In particular, it features an emotion engine that provides appropriate advice based on the user's emotional state. Specific implementation methods for this system are described below.

[2431] Overall system configuration

[2432] This system is mainly composed of a server, a terminal, and a user interface.

[2433] User Registration and Login

[2434] A user accesses the system and registers by entering their name, email address, and password. The terminal collects this registration information and sends it to the server. The server saves the registration information in a database and generates a unique user ID. The user then logs in by entering their email address and password. The terminal sends the login information to the server, which verifies the information. If authentication is successful, the server sends the authentication result to the terminal, and the user can log in to the system.

[2435] Receiving and analyzing consultation details

[2436] After logging in, the user enters the content of the consultation into the text box and clicks the send button. Alternatively, the consultation content can be recorded and sent by voice. The device sends the entered text or voice data to the server. The server passes the received consultation content to a natural language processing engine (NLP engine) and emotion engine for analysis. At this time, the following process takes place:

[2437] Text tokenization: Splitting a sentence into words.

[2438] Part-of-speech tagging: Identifying the part of speech of each word.

[2439] Sentiment analysis: Analyze the emotional tone of the entire text.

[2440] Intention recognition: Identifying the main idea of ​​a sentence.

[2441] When there is voice input, it performs voice analysis, converts the content into text, and recognizes emotions.

[2442] Generating Advice

[2443] The server generates appropriate advice using an advice generation engine based on the analysis results. The advice generation engine references a knowledge base that matches the analyzed content and generates specific advice. Furthermore, the advice is customized based on the user's emotions recognized by the emotion engine.

[2444] Introducing related products and services

[2445] The server retrieves related products and services via APIs of external commercial transaction sites and health-related services based on the consultation content. For example, if the consultation is about stress, it retrieves links to relaxation supplements and massage products.

[2446] Providing advice and product links

[2447] The server sends the generated advice and links to related products and services to the terminal. The terminal displays the received advice and links to the user. The user can check the displayed advice and click the provided links to check the details of the products and services and purchase them.

[2448] Data storage and AI training

[2449] The server stores the user's consultation details and the advice provided in a database. The stored data is used for future analysis and AI training. The server periodically uses the data to update the analysis algorithm and improve the accuracy of the advice.

[2450] Specific examples

[2451] Examples of stress consultations

[2452] 1. User: Enter text such as "I've been having trouble sleeping lately because of stress. Please tell me what to do." Or, enter voice such as "I'm so stressed, I don't know what to do."

[2453] 2. Terminal: Sends the consultation details to the server.

[2454] 3. Server:

[2455] Analysis: Identify stress and sleep problems.

[2456] Sentiment analysis: Identifying emotions such as stress and anxiety from user input.

[2457] Advice generation: Generate "We recommend you engage in relaxing activities or take supplements," and customize the advice to "First, try taking a deep breath and relaxing" based on the user's emotional state.

[2458] Get related services: Get links to stress relief related supplements.

[2459] 4. Server: Sends advice and product links to the device.

[2460] 5. Terminal: Display advice and links to the user.

[2461] 6. User: Reads the advice and clicks on the supplement link to purchase.

[2462] Skin care consultation example

[2463] 1. User: Enter text such as "I'm worried about my skin condition. Please tell me how to take care of it." Or enter voice such as "My skin condition has been getting worse recently and I don't know what to do."

[2464] 2. Terminal: Sends the consultation details to the server.

[2465] 3. Server:

[2466] Analysis: Identify skin care problems.

[2467] Sentiment analysis: Identifying feelings of confusion or anxiety from user input.

[2468] Advice generation: Generate "We recommend using skin care products that emphasize hydration," and customize it to "Don't worry, we recommend effective products" to ease confusion and anxiety.

[2469] Get related services: Get links to moisturizing skin care products.

[2470] 4. Server: Sends advice and product links to the device.

[2471] 5. Terminal: Display advice and links to the user.

[2472] 6. User: Reads the advice and clicks on the product link to purchase.

[2473] In this way, the system of the present invention improves the user's quality of life and health management by quickly and efficiently analyzing the user's consultation content and providing appropriate advice and related products and services that take into account the user's emotional state.

[2474] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2475] Step 1: User Registration

[2476] The user accesses the system's registration page and enters their name, email address, and password (input). When they click the Register button, the registration information is sent. The terminal sends the entered registration information to the server using an HTTP POST request (output).

[2477] Step 2: Save user information and generate a user ID

[2478] The server saves the received registration information in a database and generates a unique user ID (data processing). It performs the necessary database operations, and once the saving process is complete, it sends a registration completion notification including the user ID to the terminal (output). The terminal receives the notification and displays it to the user (operation).

[2479] Step 3: User Login

[2480] The user enters their email address and password on the login page and clicks the login button (input). The device sends the login information to the server using an HTTP POST request (output).

[2481] Step 4: Authentication process

[2482] The server compares the received login information with the information in the database and performs authentication processing (data calculation). If authentication is successful, it sends an authentication success message to the terminal and redirects the user to the homepage (output). Similarly, if authentication fails, it sends a failure message (operation).

[2483] Step 5: Enter your consultation details

[2484] After logging in, the user accesses the system's consultation page, enters the content of the consultation in the text box, and clicks the send button (input). Alternatively, the user can record the content of the consultation by voice and send it (action).

[2485] Step 6: Submit your consultation

[2486] The terminal sends the text or voice data entered by the user to the server using an HTTP POST request (output).

[2487] Step 7: Natural Language Processing Analysis

[2488] The server passes the received consultation content to a natural language processing engine for analysis (data calculation). Specifically, it performs text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition. Based on the analysis results, it proceeds to the next step (output).

[2489] Step 8: Sentiment Analysis

[2490] The server uses an emotion engine to analyze the emotional tone of the received consultation content (data calculation). For example, if the word "stress" appears frequently, it identifies emotions of anxiety and tension (output).

[2491] Step 9: Advice Generation

[2492] The server uses an advice generation engine to generate appropriate advice based on the results of natural language processing and sentiment analysis (data processing). It then references a knowledge base that matches the analyzed content and generates specific advice (output).

[2493] Step 10: Acquire related products and services

[2494] The server obtains related products and services based on the consultation details via APIs of external commercial transaction sites and health-related services (data calculation), and uses the APIs to obtain data and organize the necessary information (output).

[2495] Step 11: Provide advice and product links

[2496] The server sends the generated advice and links to related products and services to the terminal (output), and the terminal displays the received advice and links to the user (operation).

[2497] Step 12: User Verification and Purchase

[2498] The user can check the displayed advice and, if necessary, click on the provided link to check the product or service in detail and purchase it (action).

[2499] Step 13: Save your data

[2500] The server stores the user's consultation details and the advice provided in a database (data processing), which makes it possible to track the data history (output).

[2501] Step 14: Training the AI

[2502] The server periodically analyzes the stored data and uses it to train the AI ​​model (data calculation), and provides feedback based on the analysis results to generate advice for the next time and thereafter (operation).

[2503] The above are the specific processing steps of the program in the system of the present invention. This system makes it possible to quickly and efficiently analyze the content of the user's consultation and provide appropriate advice and rel...

Claims

1. A means for analyzing the content of the consultation received from the user; means for generating appropriate advice based on the analysis results; A means for acquiring related products and services based on the consultation content; means for providing the generated advice and the acquired goods and services to a user; a means for storing the consultation details from the user and the advice provided in a database; a means for improving the analysis algorithm based on the stored data; A system including:

2. 2. The system of claim 1, wherein the means for generating appropriate advice includes means for analyzing the consultation content using a natural language processing engine, the natural language processing engine performing text tokenization, part-of-speech tagging, sentiment analysis, and intent recognition.

3. The system according to claim 1, wherein the means for acquiring the related products and services is via an API with an external e-commerce site and a health-related service.

Images