system

Abstract

We provide the system. [Solution] A means for receiving user information input by the user and storing it in a user authentication database, A means of authenticating a user using authentication information, A means for generating a session ID to initiate a conversational session for an authenticated user and providing the user with the session ID, A means for transmitting user input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence, A means for displaying the response from the artificial intelligence to the user, A means to save the logs of the dialogue session and analyze them as needed, Means of ending a dialogue session, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Currently, conventional counseling services are often conducted face-to-face or by phone, making it difficult for users to objectively re-examine their worries and thoughts. Also, due to dependence on the counselor's schedule, it is difficult to provide counseling promptly and at a free timing. Furthermore, the price of counseling is high, and not all users can easily afford it. There is a need for a system that solves these problems and makes it easier for users to objectively re-examine their worries and thoughts.

Means for Solving the Problems

[0005] This invention is a system that includes means for receiving user information input from a user and storing it in a user authentication database; means for authenticating the user using the authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence; means for displaying the response from the artificial intelligence to the user; means for saving a log of the dialogue session and performing analysis as necessary; and means for ending the dialogue session. This system allows users to objectively re-examine their worries and thoughts regardless of time or place. Furthermore, saving the dialogue session log can be useful for future analysis and learning the artificial intelligence.

[0006] This invention solves the problems of conventional counseling services and provides users with an environment where they can more easily organize their thoughts and feelings.

[0007] "User information" refers to information necessary to identify a user, such as the user's name, ID, and password.

[0008] A "user authentication database" is a database used to store and manage user authentication information.

[0009] "Authentication information" refers to information used to authenticate a user, such as a user's ID and password.

[0010] A "session ID" is a unique identifier generated to identify an interactive session.

[0011] A "dialogue session" refers to a series of conversational exchanges that take place between a user and artificial intelligence.

[0012] "Natural language processing" refers to technology that understands user input in human language and generates appropriate responses.

[0013] "Artificial intelligence" refers to a machine learning model that has the function of generating appropriate responses to user input.

[0014] "Response" refers to the text or message that the artificial intelligence sends back to the user.

[0015] A "dialogue log" is a record of all interactions that took place during a dialogue session.

[0016] An "authentication token" is a temporary identification piece issued to an authenticated user and is used to perform authentication in subsequent requests.

[0017] A "database" is a system used to systematically store information and to access and manage it later.

[0018] "Analysis" is the process of evaluating collected data and finding patterns and meanings. [Brief explanation of the drawing]

[0019] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0020] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0021] First, the language used in the following description will be explained.

[0022] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0032] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0039] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0040] This invention is a system that uses artificial intelligence with natural language processing capabilities to help users objectively re-examine their own thoughts and feelings. The system consists of several main components, each with a specific function.

[0041] 1. User registration and login

[0042] The server receives a new user registration request. When the user enters their name, ID, and password and submits them, the server receives this information, hashes the password, and stores it in the user authentication database. The user then logs in using the registered information. During login, the server authenticates the entered ID and password and, if correct, issues an authentication token. This authentication token is used by the user for subsequent operations.

[0043] For example, when a user creates a new account, the server receives and stores the user information. Then, the user enters their ID and password on the login screen and sends a login request. The server receives this and, if authentication is successful, issues a token.

[0044] 2. Start of the dialogue session

[0045] When a user initiates an interactive session, the server generates a session ID and provides it to the user. The session ID is unique and used to identify the relevant interactive session. Upon receiving a session initiation request, the server stores the session ID and user information in the session management database.

[0046] 3. Conversation progression using natural language processing

[0047] Users input questions or comments about their worries and emotions and send them to the server. The server sends the user's input to an artificial intelligence with natural language processing capabilities, which then generates appropriate responses to the questions. These responses are then provided to the user via the server. This allows users to organize their emotions and thoughts through dialogue.

[0048] For example, if a user submits a question such as "I've been feeling stressed at work lately," the server sends the question to the artificial intelligence and waits for a response. The AI ​​generates a response such as "What specifically is causing you stress?", and the server forwards this response to the user.

[0049] 4. Providing and recording feedback

[0050] The server records all interactions during a dialogue session as a dialogue log. This dialogue log is used for later analysis and AI training. Users can also review their own dialogue logs later, providing them with valuable data for self-analysis.

[0051] 5. End of session

[0052] At the end of a conversational session, the user sends an termination request to the server. The server receives the request, saves the session log to the database, and terminates the session. The saved log can be accessed later and used by the user for review.

[0053] In this way, the present invention provides an effective means for users to objectively re-examine their worries and emotions, and realizes a system that solves the problems of conventional counseling services.

[0054] The following describes the processing flow.

[0055] Step 1:

[0056] The user enters their name, ID, and password and sends a user registration request to the server.

[0057] Step 2:

[0058] The server stores the received user information in a database. Passwords are stored in hashed form.

[0059] Step 3:

[0060] The user enters their ID and password on the login screen and sends a login request to the server.

[0061] Step 4:

[0062] The server retrieves the corresponding hashed password from the database and checks if it matches the entered password. If they match, it issues an authentication token to the user.

[0063] Step 5:

[0064] The user uses an authentication token to send a request to the server to start an interactive session.

[0065] Step 6:

[0066] The server generates a new session ID and provides it to the user. The session ID, user information, and session log are stored for management purposes.

[0067] Step 7:

[0068] Users enter questions and comments within the dialogue session and send them to the server.

[0069] Step 8:

[0070] The server sends the questions received from the user to the artificial intelligence and receives a response from the AI.

[0071] Step 9:

[0072] The server displays the response from the artificial intelligence to the user.

[0073] Step 10:

[0074] Users can continue to enter further questions and comments based on the AI's responses.

[0075] Step 11:

[0076] The server saves logs of all dialogue sessions for future analysis and use in training artificial intelligence.

[0077] Step 12:

[0078] If a user wishes to end a conversation session, they send a session termination request to the server.

[0079] Step 13:

[0080] The server saves the session log to the database and terminates the interactive session. The user can then review the interactive log later.

[0081] (Example 1)

[0082] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0083] Traditional counseling services have many shortcomings. In particular, they lack appropriate means for users to objectively re-examine their own emotions and problems. Furthermore, the counseling process and feedback are inconsistent, and sufficient data for later review is not stored. As a result, there is a lack of useful information for users to conduct self-analysis, making effective counseling difficult.

[0084] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0085] In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for authenticating the user using the authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to a generative AI model having natural language processing capabilities and receiving a response from the generative AI model; means for displaying the response from the generative AI model to the user; means for recording a log of the dialogue session and performing analysis as necessary; means for allowing the user to review the recorded dialogue session log later; and means for ending the dialogue session. This makes it possible to provide a means for the user to objectively re-examine their emotions and worries and to conduct effective self-analysis. Furthermore, it enables a consistent counseling process and allows for sufficient storage of data for feedback and reflection.

[0086] "User information" refers to identification information such as the user's name, ID, and password that they register in the system.

[0087] A "user authentication database" is a database used to store user information and authenticate users.

[0088] "Authentication information" refers to information such as a user ID and password that a user uses to access the system.

[0089] A "session ID" is a unique identifier generated by the server and provided to the user to identify an interactive session.

[0090] "Natural language processing capabilities" refer to the technology used to understand and process human language, and generative AI models possess this capability.

[0091] A "generative AI model" is an artificial intelligence model that generates appropriate responses to user input.

[0092] A "response" is the answer that a generative AI model generates based on the user's input.

[0093] A "dialogue session" is a series of processes in which a user interacts with a generative AI model.

[0094] A "dialogue log" is data that records all interactions during a dialogue session.

[0095] "Analysis" is the process of analyzing data using dialogue logs and extracting useful information.

[0096] "Reflection" is the process of reviewing saved conversation logs later and conducting self-analysis and re-evaluation.

[0097] This invention is a system that helps users objectively re-examine their own thoughts and feelings using artificial intelligence (generative AI model) with natural language processing capabilities. This system consists of several main components, each with a specific function.

[0098] The system's hardware includes servers, user terminals (such as personal computers and smartphones), and a database. The software includes a user authentication system, a generative AI model with natural language processing capabilities, and a database management system.

[0099] User registration and login

[0100] The user first enters their name, ID, and password and sends them to the server. The server receives this information, hashes the password, and stores it in the user authentication database. The user then enters their ID and password on the login screen and receives an authentication token. This token is used to perform subsequent operations.

[0101] Start of the dialogue session

[0102] When a user initiates an interactive session, the server generates a new session ID and provides it to the user. The server stores the session ID and user information in the session management database and then initiates the interactive session.

[0103] Conversation progression using natural language processing

[0104] Users input questions or comments about their worries or feelings and send them to the server. The server sends the user's input to a generating AI model and generates an appropriate response. The generated response is then provided to the user via the server.

[0105] Specific example:

[0106] If a user enters "I've been under a lot of stress at work lately," the server sends this message to a generating AI model. The generating AI model then generates a response asking "What specifically is causing you stress?", which the server then sends to the user.

[0107] Providing and recording feedback

[0108] The server records all interactions during a dialogue session as a dialogue log and stores it in a database. This dialogue log can be reviewed by the user later and used to train generative AI models. Users can also review their own dialogue logs later.

[0109] End of session

[0110] At the end of an interactive session, the user sends a session termination request to the server. The server receives this request, saves the session log to the database, and terminates the session.

[0111] Example of a prompt:

[0112] User: "I've been under a lot of stress at work lately."

[0113] AI: "Specifically, what aspects are causing you stress?"

[0114] In this way, the present invention is a system that provides an effective means for users to objectively re-examine their worries and emotions and to conduct self-analysis. Furthermore, this system solves the problems of conventional counseling services and supports users in maintaining better mental health.

[0115] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0116] Step 1: User Registration

[0117] 1. Input: The user enters their name, ID, and password.

[0118] 2. Specific operation: The terminal sends the entered information to the server in JSON format.

[0119] 3. Data Processing and Calculation: The server processes the received information and hashes the password using the bcrypt algorithm.

[0120] 4. Output: The hashed password and user information are stored in the user authentication database. The server sends a registration completion message to the user.

[0121] Step 2: Log in

[0122] 1. Input: The user enters their ID and password on the login screen.

[0123] 2. Specific operation: The terminal sends the input information to the server in JSON format.

[0124] 3. Data Processing and Calculation: The server receives the user ID and password and compares them against the hashed password in the database.

[0125] 4. Output: If authentication is successful, the server generates an authentication token and sends it to the user.

[0126] Step 3: Start the dialogue session

[0127] 1. Input: The user sends a request to start a conversation session.

[0128] 2. Specific action: The terminal sends a request to the server.

[0129] 3. Data processing and calculation: The server generates a new session ID and saves that ID to the session management database.

[0130] 4. Output: The server provides the user with a session ID.

[0131] Step 4: Conversation progression using natural language processing

[0132] 1. Input: The user enters a message about their worries or feelings.

[0133] 2. Specific action: The terminal sends the entered message to the server.

[0134] 3. Data Processing and Calculation: The server sends the message to the AI ​​model that generates the appropriate response.

[0135] 4. Output: The server sends the generated response to the user. For example, if the user inputs "I've been under a lot of stress at work lately," the AI ​​model will generate the response "What specifically is causing you stress?" and the server will send this to the user.

[0136] Step 5: Provide and record feedback

[0137] 1. Input: The user sends a request to review the conversation log later.

[0138] 2. Specific operation: The server records all interactions during the conversation session as a conversation log.

[0139] 3. Data Processing and Calculation: Recorded dialogue logs are saved to a database and used to train the generated AI model as needed.

[0140] 4. Output: The server will provide the user with the recorded conversation log upon request.

[0141] Step 6: Ending the session

[0142] 1. Input: The user sends a request to end the conversation session.

[0143] 2. Specific action: The terminal sends a termination request to the server.

[0144] 3. Data processing and calculation: The server saves the session log to the database.

[0145] 4. Output: The server sends a session termination notification to the user.

[0146] Through the steps outlined above, a system will be created that allows users to objectively re-examine their worries and emotions and conduct effective self-analysis.

[0147] (Application Example 1)

[0148] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0149] In modern virtual stores, customers are expected to receive appropriate advice and personalized product recommendations when making purchases. However, conventional systems do not adequately provide immediate natural language processing responses to customer inquiries or individualized recommendations based on past logs. As a result, customers lack sufficient means to have a satisfying purchasing experience, which diminishes their desire to buy.

[0150] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0151] In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for authenticating the user using the authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence; means for displaying the response from the artificial intelligence to the user; means for storing a log of the dialogue session and performing analysis as necessary; means for ending the dialogue session; means for receiving consultation from the customer regarding product selection within the virtual store and making product suggestions based on natural language processing; and means for learning the user's preferences using past dialogue logs and generating personalized suggestions. As a result, customers can receive consultation regarding appropriate product selection within the virtual store, receive personalized services, and obtain a more satisfying purchasing experience.

[0152] "User information" refers to information that a user enters for their own identification and authentication, and includes names, IDs, passwords, etc.

[0153] A "user authentication database" is a database that securely stores user authentication information and supports the authentication process.

[0154] A "session ID" is a unique identifier that is generated and provided to the user to identify a specific interaction session.

[0155] "Artificial intelligence with natural language processing capabilities" refers to an artificial intelligence system that understands human language and has the ability to generate appropriate responses based on that input.

[0156] A "dialogue session log" is data that records all interactions that took place during a dialogue session with a user, and is used for later analysis and training of artificial intelligence.

[0157] A "virtual store" is a virtual store that exists on the internet, a platform where customers can select and purchase products online.

[0158] "Personalized suggestions" is an approach that individually proposes the most suitable products and services to each user based on past conversation logs and user preferences.

[0159] The system for implementing this invention performs user information input and authentication, dialogue session management, response generation using natural language processing, and dialogue log recording and analysis. To realize these functions, the following steps are taken between the server, terminal, and user.

[0160] User information input and authentication

[0161] The user enters user information such as their name, ID, and password using a device (smartphone or head-mounted display). The server receives this information, hashes the password, and stores it in the user authentication database. During login, the server verifies the entered ID and password, and if correct, issues an authentication token (session ID). This authentication token is required for the user to perform subsequent operations.

[0162] Managing dialogue sessions

[0163] When an authenticated user initiates an interactive session, the server generates a unique session ID and provides it to the user. This session ID is used to identify a specific interactive session and is stored in the session management database. Upon receiving a session initiation request, the server manages the session ID and user information internally.

[0164] Response generation using natural language processing

[0165] Users input questions and inquiries about product selection through their terminals. The server sends this input to an artificial intelligence (AI) with natural language processing capabilities, which generates appropriate responses. The generated responses are provided to the user via the server, allowing the user to receive product information and suggestions through the dialogue. The AI ​​uses natural language processing, such as NLTK.

[0166] Specific example

[0167] For example, if a user enters "I'm looking for a summer shirt," the server sends that input to the artificial intelligence, which then generates a response such as "What kind of design do you prefer?" This response is then provided to the user via the server.

[0168] Recording and analysis of dialogue logs

[0169] All interactions during a conversation session are recorded as conversation logs. The server saves these logs for later analysis and use in training the artificial intelligence. The logs are stored in a database and can be reviewed by the user later. Furthermore, past conversation logs are used to learn the user's preferences and generate more personalized suggestions.

[0170] End of session

[0171] At the end of a conversational session, the user sends an termination request to the server. The server receives this request, saves the session log to the database, and terminates the session. The saved log can be used for later analysis and review.

[0172] Example of a prompt

[0173] For example, if the user input is "I'm looking for a summer shirt," the following prompt message is sent to the AI ​​model.

[0174] User input: I'm looking for a summer shirt.

[0175] The above describes a specific form for carrying out the invention. This system allows users to enjoy a comfortable shopping experience within a virtual store.

[0176] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0177] Step 1:

[0178] The user enters user information such as name, ID, and password using a terminal. The entered data includes name and ID in text format, while the password is hashed for security.

[0179] Step 2:

[0180] The server receives information sent by the user and hashes the password. The hashed password and other user information are stored in the user authentication database. The specific data processing used here is password hashing.

[0181] Step 3:

[0182] The user enters their ID and password on the login screen via their device. The entered ID and password are sent to the server as a login request.

[0183] Step 4:

[0184] The server verifies the entered ID and password against the user authentication database. During this process, the password is hashed again and compared to the hash value in the database. If the correct authentication information is confirmed, an authentication token (session ID) is issued. The authentication token is unique and stored in the session management database.

[0185] Step 5:

[0186] An authenticated user sends a request to start a conversation session through their terminal. The server generates a unique session ID and provides it to the user. The session ID is an identifier used to identify a specific conversation session.

[0187] Step 6:

[0188] Users input inquiries and questions regarding product selection through their terminals. The entered inquiries are sent to the server. The specific data format used here is text data.

[0189] Step 7:

[0190] The server sends user input to an artificial intelligence (AI) with natural language processing capabilities. The AI ​​analyzes the user input and generates an appropriate response. NLTK or generative AI models are used in this process. A prompt such as "User input: I'm looking for a summer shirt" is sent to the AI.

[0191] Step 8:

[0192] The response generated by the artificial intelligence is sent back to the server. The server receives this response and displays it to the user. The output data format is text data.

[0193] Step 9:

[0194] All interactions during a dialogue session are recorded as a dialogue log. The server stores the dialogue log in a database and analyzes it as needed. The dialogue log is used later for AI training and user feedback.

[0195] Step 10:

[0196] The user sends a request to end the conversation session through their terminal. The server receives the termination request, saves the session log to the database, and terminates the session. The session log is accessible later and can be used for review.

[0197] The above outlines the specific processing steps of the system. This system allows users to smoothly consult about product selection within the virtual store and receive personalized suggestions.

[0198] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0199] This invention is a system that helps users objectively re-examine their own thoughts and feelings using artificial intelligence with natural language processing capabilities and an emotion engine. The system consists of several main components, each with a specific function.

[0200] 1. User registration and login

[0201] The server receives a new user registration request. When the user enters their name, ID, and password and submits them, the server receives this information, hashes the password, and stores it in the user authentication database. The user then logs in using the registered information. During login, the server authenticates the entered ID and password and, if correct, issues an authentication token. This authentication token is used by the user for subsequent operations.

[0202] For example, when a user creates a new account, the server receives and stores the user information. Then, the user enters their ID and password on the login screen and sends a login request. The server receives this and, if authentication is successful, issues a token.

[0203] 2. Start of the dialogue session

[0204] When a user initiates an interactive session, the server generates a session ID and provides it to the user. The session ID is unique and used to identify the relevant interactive session. Upon receiving a session initiation request, the server stores the session ID and user information in the session management database.

[0205] 3. Conversation progression and emotion recognition using natural language processing.

[0206] Users input questions or comments about their worries and emotions and send them to the server. The server sends the user's input to an artificial intelligence with natural language processing capabilities and simultaneously uses an emotion engine to recognize the user's emotions. The emotion engine extracts the emotional state from the user's input and provides this emotional information to the AI ​​to optimize the response. The AI ​​generates a response that takes the emotional information into account, and this response is provided to the user via the server.

[0207] For example, if a user submits a question like, "I've been feeling a lot of stress at work lately," the server sends that question to the artificial intelligence, and the emotion engine recognizes the user's emotion as "stress." Based on that information, the AI ​​generates a response asking, "What specifically is causing you stress?" and the server forwards that response to the user.

[0208] 4. Providing feedback and recording emotional data

[0209] The server records all interactions and emotional data during a dialogue session as a dialogue log. This dialogue log is used for later analysis and AI training. Users can also review their own dialogue logs later, providing them with valuable data for self-analysis.

[0210] 5. End of session

[0211] At the end of a conversational session, the user sends an termination request to the server. The server receives the request, saves the session log and sentiment data to the database, and terminates the session. The saved log can be accessed later and used by the user for reflection.

[0212] In this way, the present invention provides an effective means for users to objectively re-examine their worries and emotions, and realizes a system that solves the problems of conventional counseling services. By introducing an emotion engine, it becomes possible to accurately grasp the user's emotional state and provide appropriate feedback based on that.

[0213] The following describes the processing flow.

[0214] Step 1:

[0215] The user enters their name, ID, and password and sends a user registration request to the server.

[0216] Step 2:

[0217] The server stores the received user information in a database. Passwords are stored in hashed form.

[0218] Step 3:

[0219] The user enters their ID and password on the login screen and sends a login request to the server.

[0220] Step 4:

[0221] The server retrieves the corresponding hashed password from the database and checks if it matches the entered password. If they match, it issues an authentication token to the user.

[0222] Step 5:

[0223] The user uses an authentication token to send a request to the server to start an interactive session.

[0224] Step 6:

[0225] The server generates a new session ID and provides it to the user. The session ID, user information, and session log are stored for management purposes.

[0226] Step 7:

[0227] Users enter questions and comments within the dialogue session and send them to the server.

[0228] Step 8:

[0229] The server sends the questions received from the user to the emotion engine, which then analyzes the user's emotional state.

[0230] Step 9:

[0231] The emotion engine recognizes the user's emotions from the text of the question and generates emotion data.

[0232] Step 10:

[0233] The server sends the question along with sentiment data to the artificial intelligence, which then generates an appropriate response.

[0234] Step 11:

[0235] The artificial intelligence considers emotional data to generate the optimal response for the user and sends it to the server.

[0236] Step 12:

[0237] The server displays the response from the artificial intelligence to the user.

[0238] Step 13:

[0239] Users can continue to enter further questions and comments based on the AI's responses.

[0240] Step 14:

[0241] The server stores logs and sentiment data from all dialogue sessions, which will be used for future analysis and training of artificial intelligence.

[0242] Step 15:

[0243] If a user wishes to end a conversation session, they send a session termination request to the server.

[0244] Step 16:

[0245] The server saves the session log and sentiment data to the database and ends the conversation session. The user can then review the conversation log and sentiment data later.

[0246] (Example 2)

[0247] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0248] Traditional systems struggled to accurately understand users' emotions and psychological states and provide appropriate feedback based on that understanding. Furthermore, they lacked sufficient management and analysis of dialogue logs, failing to offer effective means for user self-analysis and improvement. As a result, conventional counseling services and support systems were subject to numerous limitations.

[0249] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0250] In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for performing authentication using the user authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence; means for recognizing the user's emotions using an emotion engine and providing the emotion information to the artificial intelligence to optimize the response; means for displaying the response from the artificial intelligence to the user; means for saving a log of the dialogue session and performing analysis as necessary; and means for ending the dialogue session. As a result, the user can simply input their emotions and concerns in natural language, and the artificial intelligence and emotion engine will work together to provide optimal feedback, and the dialogue log can be reviewed and analyzed later.

[0251] "User information" refers to information such as the name, ID, and password that a user needs to register with the system.

[0252] A "user authentication database" is a database used to store and verify user authentication information.

[0253] "User authentication information" refers to information such as the ID and password that a user uses to access the system.

[0254] A "dialogue session" refers to a series of interactions in which a user and artificial intelligence engage in dialogue.

[0255] A "session ID" is an identifier used to uniquely identify a specific dialogue session.

[0256] "Natural language processing functionality" refers to the technology used to analyze and understand natural language input from users.

[0257] "Artificial intelligence" is a computer system that possesses the knowledge to generate appropriate responses to user input.

[0258] An "emotion engine" is a technology that extracts and recognizes emotional states from user input.

[0259] A "dialogue log" is a record of the interactions and emotional data during a dialogue session.

[0260] "Analysis" refers to the act of analyzing the user's emotional state and the content of conversations based on saved dialogue logs.

[0261] "Feedback" refers to information that a system provides based on the user's input and emotional state.

[0262] This invention is a system that helps users objectively re-examine their own thoughts and feelings using artificial intelligence with natural language processing capabilities and an emotion engine. The system consists of several main components, each with a specific function.

[0263] User registration and login

[0264] The server receives a new user registration request. The user enters their name, ID, and password into the terminal and submits them. The server hashes the received password using a hashing algorithm (e.g., SHA-256) and stores the user information in the user authentication database. The user then logs in using the registered information. During login, the server compares the entered ID and password with the information in the database, and if authentication is successful, generates an authentication token and sends it to the user. This authentication token is used by the user when performing subsequent operations.

[0265] For example, a user might type "I want to create a new account" and click the submit button. The server receives this request, saves the user's information, and returns a message to the user saying "Your account has been created."

[0266] Start of the dialogue session

[0267] When a user initiates an interactive session, the server generates a unique session ID and provides it to the user. Upon receiving the session initiation request, the server stores the session ID and user information in the session management database.

[0268] For example, if a user types and submits "I want to start a conversation session," the server generates a new session ID and displays "Session started" on the user's screen.

[0269] Conversation progression and emotion recognition using natural language processing

[0270] The user enters comments about their worries and emotions and sends them to the server. The server sends these comments to a generative AI model with natural language processing capabilities (e.g., OpenAI®'s GPT-3®). Simultaneously, the server uses an emotion engine (e.g., Affectiva) to recognize the user's emotions and extract emotion information. The server provides this emotion information to the generative AI model and requests it to generate the most appropriate response. The generative AI model generates a response that takes the emotion information into account, and the server sends that response to the user.

[0271] For example, a user might type and submit "I've been feeling a lot of stress at work lately." The server sends this input to a generative AI model and an emotion engine. The generative AI model, based on the emotion information "stress," generates a response asking "What specifically makes you feel stressed?" and sends it to the user.

[0272] Providing feedback and recording emotional data

[0273] The server records all interactions and emotional data during a dialogue session as a dialogue log. This dialogue log is used for later analysis and training of generative AI models. Users can review their own dialogue logs later and use them as data for self-analysis.

[0274] End of session

[0275] When a conversation session ends, the user sends an end request to the server. The server receives the request, saves the session log and sentiment data to the database, and terminates the session. The saved log is accessible to the user later and can be used for reflection.

[0276] For example, if a user types and submits "Please end the conversation session," the server saves the session log and returns the message "Session ended."

[0277] In summary, the present invention provides an effective means for users to objectively re-examine their own worries and emotions, and realizes a system that solves the problems of conventional counseling services.

[0278] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0279] Step 1: Receiving a user registration request

[0280] The server receives a new registration request from a user. The user enters their name, ID, and password on their terminal and sends them to the server. Upon receiving the input, the server hashes the password using a hashing algorithm (e.g., SHA-256). The hashed password and user information are then generated as output.

[0281] Specific example of operation: The user types "I want to create a new account" on their device and clicks the submit button.

[0282] Step 2: Saving User Information

[0283] The server stores the hashed password and other user information in the user authentication database. The input is the hashed password generated in Step 1 and the user information, and the output is the status of success.

[0284] Specific operation example: The user information is stored in the database, and a message "Account created" is returned to the user.

[0285] Step 3: Receive login request

[0286] After registration, the user enters the ID and password on the login screen and sends a login request to the server. The input is the ID and password, which are received by the server. The output is the generation of an authentication token.

[0287] Specific operation example: The user enters the ID and password on the terminal and clicks the "Login" button.

[0288] Step 4: User authentication

[0289] The server compares the entered ID and password with the information in the user authentication database. The input is the ID and password provided by the user, and as processing, the hashed password is compared with the information in the database. The output is the generation of an authentication token.

[0290] Specific operation example: When authentication is successful, "Login successful" is displayed.

[0291] Step 5: Receive request for interactive session

[0292] The user sends a request to start an interactive session. The input is a click operation or a prompt sentence such as "Want to start an interactive session". The output is the generation of a unique session ID.

[0293] Specific example of operation: The user types and submits "I want to start a conversation session."

[0294] Step 6: Generate a session ID

[0295] The server generates a unique session ID and provides it to the user. The input is a request for an interactive session, and the output is the session ID.

[0296] Specific example of operation: The server generates a new session ID, and the user's screen displays "Session started".

[0297] Step 7: User submits comment

[0298] Users enter comments about their worries and feelings and send them to the server. The input is the user's comment, and the output is the comment sent to the server.

[0299] Specific example of operation: The user types "I've been feeling a lot of stress at work lately" into the device and sends it.

[0300] Step 8: NLP Processing

[0301] The server sends user input to a generative AI model with natural language processing capabilities. The input is the user's comment, which the generative AI model analyzes. The output consists of the analysis result and the generated response.

[0302] Specific example of operation: The server sends the comment "I've been under a lot of stress at work lately" to the AI ​​model that generates the comment.

[0303] Step 9: Emotion Recognition

[0304] The server uses an emotion engine to recognize the user's emotions and extract emotion information. The input is the user's comment, and the emotion engine analyzes the emotion information. The output is the extracted emotion information.

[0305] Specific operation example: The emotion engine recognizes the emotion of "stress" from the user's comment.

[0306] Step 10: Response generation by AI

[0307] The server provides the emotion information to the generation AI model and requests it to generate an optimal response. The input is the emotion information and the original comment, and the output is the optimized response.

[0308] Specific operation example: The generation AI model considers the emotion information of "stress" and generates a response of "Specifically, what aspects are stressful?"

[0309] Step 11: Sending a response to the user

[0310] The server sends the response from the AI model to the user. The input is the response from the generation AI model, and the output is the display to the user.

[0311] Specific operation example: The response of "Specifically, what aspects are stressful?" is displayed on the user's terminal.

[0312] Step 12: Recording the conversation log

[0313] The server records all the interactions and emotion data during the conversation session as a conversation log. The input is the interactions and emotion data of the conversation session, and the output is the recording of the conversation log.

[0314] Specific operation example: The server saves the conversation interactions as a log in real time.

[0315] Step 13: Analysis and utilization of the conversation log

[0316] The server performs analysis based on the conversation log and uses it for the learning of the generation AI model. The input is the conversation log, and the output is the analysis result and the update of the generation AI model.

[0317] Specific example of operation: The server analyzes the saved dialogue logs and uses them to improve the performance of the generated AI model.

[0318] Step 14: Received a request to end the dialogue session.

[0319] The user requests the end of the interaction session. The input is the termination request, and the output is the start of the session termination process.

[0320] Specific example of operation: The user types and submits "Please end the conversation session."

[0321] Step 15: Saving session data

[0322] The server saves the session log and sentiment data to the database and then terminates the session. The inputs are the session log and sentiment data, and the outputs are "Saving complete" and "Session terminated."

[0323] Specific example of operation: The server returns the message "Session ended" to the user.

[0324] (Application Example 2)

[0325] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0326] Traditional in-store customer service often struggled to immediately understand and appropriately respond to customer emotions and opinions, potentially leading to decreased customer satisfaction. Furthermore, store staff lacked effective means to quickly grasp specific customer concerns and dissatisfactions. Therefore, there was a need for a system that could accurately understand customer emotions and provide appropriate responses in real time.

[0327] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for authenticating the user using the authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence; means for displaying the response from the artificial intelligence to the user; means for identifying the user's emotions using an emotion recognition engine, recording the identification result, and optimizing the corresponding response; means for saving a log of the dialogue session and performing analysis as necessary; and means for ending the dialogue session. This makes it possible to accurately grasp the emotions of customers in physical stores and respond quickly and appropriately.

[0328] "User information" refers to information necessary to identify and authenticate a user, such as the user's name, ID, and password.

[0329] A "user authentication database" is a database used to securely store user information and utilize it in the authentication process.

[0330] A "session ID" is an identifier generated to uniquely identify an interactive session.

[0331] "Natural language processing" refers to the technology used to analyze and understand user input in natural language.

[0332] "Artificial intelligence" is a system that possesses natural language processing capabilities and generates appropriate responses based on user input.

[0333] A "response" is the answer or message that artificial intelligence generates in response to user input.

[0334] An "emotion recognition engine" is a system that identifies emotions from user input and provides information about those emotions.

[0335] A "dialogue session log" is a record of the interactions that took place between the user and the system.

[0336] "Analysis" is the process of using dialogue session logs and identified sentiment data to find specific patterns and insights.

[0337] "System" refers to the entire computer system, including the means and functions described above.

[0338] This invention is a system that enables accurate understanding of customer emotions in physical stores and facilitates quick and appropriate responses. A specific embodiment of this system is described below.

[0339] User registration and login

[0340] The server receives a new registration request from a user and retrieves the name, ID, and password entered by the user.

[0341] The server hashes the password and stores the result in the user authentication database.

[0342] Once registration is complete, users enter their ID and password on the login screen and submit a login request.

[0343] The server authenticates the entered ID and password, and if successful, generates an authentication token and provides it to the user.

[0344] Start of the dialogue session

[0345] When a user initiates a conversational session, the server generates a unique session ID and provides it to the user.

[0346] Session IDs and user information are stored in the session management database.

[0347] Conversation progression and emotion recognition using natural language processing

[0348] Users input questions and comments about their worries and feelings via their smartphones or smart glasses and send them to the server.

[0349] The server sends this input to an artificial intelligence with natural language processing capabilities (such as Google's NLP API).

[0350] Simultaneously, the server uses an emotion recognition engine (such as Microsoft® Azure® Emotion API) to identify the user's emotions.

[0351] The emotion recognition engine extracts the emotional state, provides that information to the artificial intelligence, and generates the optimal response.

[0352] The generated response is provided to the user via the server.

[0353] Providing feedback and recording emotional data

[0354] The server records all interactions and emotional data during a dialogue session as a dialogue log.

[0355] The dialogue logs will be used for later analysis and for training artificial intelligence.

[0356] Users can review their conversation logs later.

[0357] End of session

[0358] At the end of a conversational session, the user sends an termination request to the server.

[0359] The server saves the session log and sentiment data to the database and then terminates the session.

[0360] Specific example

[0361] The following are examples of prompt statements for a generative AI model:

[0362] User: "I've been dissatisfied with the service at this store recently."

[0363] System: "Could you please tell us specifically what caused your dissatisfaction?"

[0364] This system is expected to significantly improve customer service in physical stores and increase customer satisfaction. The server receives user input via smartphones and smart glasses, and uses artificial intelligence and an emotion recognition engine to provide effective responses. In this way, store staff can respond to customers quickly and appropriately.

[0365] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0366] Step 1:

[0367] This system accepts new user registration requests. Users enter their name, ID, and password via their device and send this data to the server. The server receives this user information, hashes the password, and stores it in the user authentication database. Input: User's name, ID, and password. Output: Hashed password, and a message confirming registration to the authentication database.

[0368] Step 2:

[0369] The user enters their ID and password on the login screen using their device and sends a login request to the server. The server verifies the user's ID and hashed password against the authentication database, and if authentication is successful, generates an authentication token and provides it to the user. Input: User ID, password. Output: Authentication token.

[0370] Step 3:

[0371] An authenticated user sends a request to the server to start an interactive session from their terminal. The server generates a unique session ID, provides it to the user, and stores the session ID and user information in the session management database. Input: Interactive session start request. Output: Session ID, registration in the session management database.

[0372] Step 4:

[0373] Users input questions and comments about their worries and emotions via their device and send them to the server. The server receives the input from the user and sends it to an artificial intelligence with natural language processing capabilities (such as the Google NLP API). Input: User's questions and comments. Output: Analysis results from natural language processing.

[0374] Step 5:

[0375] The server simultaneously uses an emotion recognition engine (such as the Microsoft Azure Emotion API) to identify the user's emotional state from their input. The emotion recognition engine extracts the user's emotions, provides this information to the artificial intelligence, and generates an appropriate response. Input: User's questions and comments. Output: Emotion recognition results, optimized response.

[0376] Step 6:

[0377] The server receives the generated response and provides it to the user through the terminal. This allows the user to receive feedback about their concerns and emotions. The server also records all interactions and emotional data during the conversation session as a dialogue log, preparing for analysis as needed. Input: Optimized response. Output: Providing the response to the user, recording the dialogue log.

[0378] Step 7:

[0379] When a user requests to end a conversation session, the server saves the session log and sentiment data to the database and terminates the session. The user can review the conversation log later. Input: Session termination request. Output: Saving of session log and sentiment data, session termination message.

[0380] By following the above steps, the system will be able to accurately understand the user's emotions and respond quickly and appropriately.

[0381] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0382] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0383] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0384] [Second Embodiment]

[0385] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0386] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0387] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0388] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0389] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0390] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0391] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0392] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0393] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0394] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0395] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0396] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0397] This invention is a system that uses artificial intelligence with natural language processing capabilities to help users objectively re-examine their own thoughts and feelings. The system consists of several main components, each with a specific function.

[0398] 1. User registration and login

[0399] The server receives a new user registration request. When the user enters their name, ID, and password and submits them, the server receives this information, hashes the password, and stores it in the user authentication database. The user then logs in using the registered information. During login, the server authenticates the entered ID and password and, if correct, issues an authentication token. This authentication token is used by the user for subsequent operations.

[0400] For example, when a user creates a new account, the server receives and stores the user information. Then, the user enters their ID and password on the login screen and sends a login request. The server receives this and, if authentication is successful, issues a token.

[0401] 2. Start of the dialogue session

[0402] When a user initiates an interactive session, the server generates a session ID and provides it to the user. The session ID is unique and used to identify the relevant interactive session. Upon receiving a session initiation request, the server stores the session ID and user information in the session management database.

[0403] 3. Conversation progression using natural language processing

[0404] Users input questions or comments about their worries and emotions and send them to the server. The server sends the user's input to an artificial intelligence with natural language processing capabilities, which then generates appropriate responses to the questions. These responses are then provided to the user via the server. This allows users to organize their emotions and thoughts through dialogue.

[0405] For example, if a user submits a question such as "I've been feeling stressed at work lately," the server sends the question to the artificial intelligence and waits for a response. The AI ​​generates a response such as "What specifically is causing you stress?", and the server forwards this response to the user.

[0406] 4. Providing and recording feedback

[0407] The server records all interactions during a dialogue session as a dialogue log. This dialogue log is used for later analysis and AI training. Users can also review their own dialogue logs later, providing them with valuable data for self-analysis.

[0408] 5. End of session

[0409] At the end of a conversational session, the user sends an termination request to the server. The server receives the request, saves the session log to the database, and terminates the session. The saved log can be accessed later and used by the user for review.

[0410] In this way, the present invention provides an effective means for users to objectively re-examine their worries and emotions, and realizes a system that solves the problems of conventional counseling services.

[0411] The following describes the processing flow.

[0412] Step 1:

[0413] The user enters their name, ID, and password and sends a user registration request to the server.

[0414] Step 2:

[0415] The server stores the received user information in a database. Passwords are stored in hashed form.

[0416] Step 3:

[0417] The user enters their ID and password on the login screen and sends a login request to the server.

[0418] Step 4:

[0419] The server retrieves the corresponding hashed password from the database and checks if it matches the entered password. If they match, it issues an authentication token to the user.

[0420] Step 5:

[0421] The user uses an authentication token to send a request to the server to start an interactive session.

[0422] Step 6:

[0423] The server generates a new session ID and provides it to the user. The session ID, user information, and session log are stored for management purposes.

[0424] Step 7:

[0425] Users enter questions and comments within the dialogue session and send them to the server.

[0426] Step 8:

[0427] The server sends the questions received from the user to the artificial intelligence and receives a response from the AI.

[0428] Step 9:

[0429] The server displays the response from the artificial intelligence to the user.

[0430] Step 10:

[0431] Users can continue to enter further questions and comments based on the AI's responses.

[0432] Step 11:

[0433] The server saves logs of all dialogue sessions for future analysis and use in training artificial intelligence.

[0434] Step 12:

[0435] If a user wishes to end a conversation session, they send a session termination request to the server.

[0436] Step 13:

[0437] The server saves the session log to the database and terminates the interactive session. The user can then review the interactive log later.

[0438] (Example 1)

[0439] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0440] Traditional counseling services have many shortcomings. In particular, they lack appropriate means for users to objectively re-examine their own emotions and problems. Furthermore, the counseling process and feedback are inconsistent, and sufficient data for later review is not stored. As a result, there is a lack of useful information for users to conduct self-analysis, making effective counseling difficult.

[0441] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0442] In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for authenticating the user using the authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to a generative AI model having natural language processing capabilities and receiving a response from the generative AI model; means for displaying the response from the generative AI model to the user; means for recording a log of the dialogue session and performing analysis as necessary; means for allowing the user to review the recorded dialogue session log later; and means for ending the dialogue session. This makes it possible to provide a means for the user to objectively re-examine their emotions and worries and to conduct effective self-analysis. Furthermore, it enables a consistent counseling process and allows for sufficient storage of data for feedback and reflection.

[0443] "User information" refers to identification information such as the user's name, ID, and password that they register in the system.

[0444] A "user authentication database" is a database used to store user information and authenticate users.

[0445] "Authentication information" refers to information such as a user ID and password that a user uses to access the system.

[0446] A "session ID" is a unique identifier generated by the server and provided to the user to identify an interactive session.

[0447] "Natural language processing capabilities" refer to the technology used to understand and process human language, and generative AI models possess this capability.

[0448] A "generative AI model" is an artificial intelligence model that generates appropriate responses to user input.

[0449] A "response" is the answer that a generative AI model generates based on the user's input.

[0450] A "dialogue session" is a series of processes in which a user interacts with a generative AI model.

[0451] A "dialogue log" is data that records all interactions during a dialogue session.

[0452] "Analysis" is the process of analyzing data using dialogue logs and extracting useful information.

[0453] "Reflection" is the process of reviewing saved conversation logs later and conducting self-analysis and re-evaluation.

[0454] This invention is a system that helps users objectively re-examine their own thoughts and feelings using artificial intelligence (generative AI model) with natural language processing capabilities. This system consists of several main components, each with a specific function.

[0455] The system's hardware includes servers, user terminals (such as personal computers and smartphones), and a database. The software includes a user authentication system, a generative AI model with natural language processing capabilities, and a database management system.

[0456] User registration and login

[0457] The user first enters their name, ID, and password and sends them to the server. The server receives this information, hashes the password, and stores it in the user authentication database. The user then enters their ID and password on the login screen and receives an authentication token. This token is used to perform subsequent operations.

[0458] Start of the dialogue session

[0459] When a user initiates an interactive session, the server generates a new session ID and provides it to the user. The server stores the session ID and user information in the session management database and then initiates the interactive session.

[0460] Conversation progression using natural language processing

[0461] Users input questions or comments about their worries or feelings and send them to the server. The server sends the user's input to a generating AI model and generates an appropriate response. The generated response is then provided to the user via the server.

[0462] Specific example:

[0463] If a user enters "I've been under a lot of stress at work lately," the server sends this message to a generating AI model. The generating AI model then generates a response asking "What specifically is causing you stress?", which the server then sends to the user.

[0464] Providing and recording feedback

[0465] The server records all interactions during a dialogue session as a dialogue log and stores it in a database. This dialogue log can be reviewed by the user later and used to train generative AI models. Users can also review their own dialogue logs later.

[0466] End of session

[0467] At the end of an interactive session, the user sends a session termination request to the server. The server receives this request, saves the session log to the database, and terminates the session.

[0468] Example of a prompt:

[0469] User: "I've been under a lot of stress at work lately."

[0470] AI: "Specifically, what aspects are causing you stress?"

[0471] In this way, the present invention is a system that provides an effective means for users to objectively re-examine their worries and emotions and to conduct self-analysis. Furthermore, this system solves the problems of conventional counseling services and supports users in maintaining better mental health.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1: User Registration

[0474] 1. Input: The user enters their name, ID, and password.

[0475] 2. Specific operation: The terminal sends the entered information to the server in JSON format.

[0476] 3. Data Processing and Calculation: The server processes the received information and hashes the password using the bcrypt algorithm.

[0477] 4. Output: The hashed password and user information are stored in the user authentication database. The server sends a registration completion message to the user.

[0478] Step 2: Log in

[0479] 1. Input: The user enters their ID and password on the login screen.

[0480] 2. Specific operation: The terminal sends the input information to the server in JSON format.

[0481] 3. Data Processing and Calculation: The server receives the user ID and password and compares them against the hashed password in the database.

[0482] 4. Output: If authentication is successful, the server generates an authentication token and sends it to the user.

[0483] Step 3: Start the dialogue session

[0484] 1. Input: The user sends a request to start a conversation session.

[0485] 2. Specific action: The terminal sends a request to the server.

[0486] 3. Data processing and calculation: The server generates a new session ID and saves that ID to the session management database.

[0487] 4. Output: The server provides the user with a session ID.

[0488] Step 4: Conversation progression using natural language processing

[0489] 1. Input: The user enters a message about their worries or feelings.

[0490] 2. Specific action: The terminal sends the entered message to the server.

[0491] 3. Data Processing and Calculation: The server sends the message to the AI ​​model that generates the appropriate response.

[0492] 4. Output: The server sends the generated response to the user. For example, if the user inputs "I've been under a lot of stress at work lately," the AI ​​model will generate the response "What specifically is causing you stress?" and the server will send this to the user.

[0493] Step 5: Provide and record feedback

[0494] 1. Input: The user sends a request to review the conversation log later.

[0495] 2. Specific operation: The server records all interactions during the conversation session as a conversation log.

[0496] 3. Data Processing and Calculation: Recorded dialogue logs are saved to a database and used to train the generated AI model as needed.

[0497] 4. Output: The server will provide the user with the recorded conversation log upon request.

[0498] Step 6: Ending the session

[0499] 1. Input: The user sends a request to end the conversation session.

[0500] 2. Specific action: The terminal sends a termination request to the server.

[0501] 3. Data processing and calculation: The server saves the session log to the database.

[0502] 4. Output: The server sends a session termination notification to the user.

[0503] Through the steps outlined above, a system will be created that allows users to objectively re-examine their worries and emotions and conduct effective self-analysis.

[0504] (Application Example 1)

[0505] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0506] In modern virtual stores, customers are expected to receive appropriate advice and personalized product recommendations when making purchases. However, conventional systems do not adequately provide immediate natural language processing responses to customer inquiries or individualized recommendations based on past logs. As a result, customers lack sufficient means to have a satisfying purchasing experience, which diminishes their desire to buy.

[0507] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0508] In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for authenticating the user using the authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence; means for displaying the response from the artificial intelligence to the user; means for storing a log of the dialogue session and performing analysis as necessary; means for ending the dialogue session; means for receiving consultation from the customer regarding product selection within the virtual store and making product suggestions based on natural language processing; and means for learning the user's preferences using past dialogue logs and generating personalized suggestions. As a result, customers can receive consultation regarding appropriate product selection within the virtual store, receive personalized services, and obtain a more satisfying purchasing experience.

[0509] "User information" refers to information that a user enters for their own identification and authentication, and includes names, IDs, passwords, etc.

[0510] A "user authentication database" is a database that securely stores user authentication information and supports the authentication process.

[0511] A "session ID" is a unique identifier that is generated and provided to the user to identify a specific interaction session.

[0512] "Artificial intelligence with natural language processing capabilities" refers to an artificial intelligence system that understands human language and has the ability to generate appropriate responses based on that input.

[0513] A "dialogue session log" is data that records all interactions that took place during a dialogue session with a user, and is used for later analysis and training of artificial intelligence.

[0514] A "virtual store" is a virtual store that exists on the internet, a platform where customers can select and purchase products online.

[0515] "Personalized suggestions" is an approach that individually proposes the most suitable products and services to each user based on past conversation logs and user preferences.

[0516] The system for implementing this invention performs user information input and authentication, dialogue session management, response generation using natural language processing, and dialogue log recording and analysis. To realize these functions, the following steps are taken between the server, terminal, and user.

[0517] User information input and authentication

[0518] The user enters user information such as their name, ID, and password using a device (smartphone or head-mounted display). The server receives this information, hashes the password, and stores it in the user authentication database. During login, the server verifies the entered ID and password, and if correct, issues an authentication token (session ID). This authentication token is required for the user to perform subsequent operations.

[0519] Managing dialogue sessions

[0520] When an authenticated user initiates an interactive session, the server generates a unique session ID and provides it to the user. This session ID is used to identify a specific interactive session and is stored in the session management database. Upon receiving a session initiation request, the server manages the session ID and user information internally.

[0521] Response generation using natural language processing

[0522] Users input questions and inquiries about product selection through their terminals. The server sends this input to an artificial intelligence (AI) with natural language processing capabilities, which generates appropriate responses. The generated responses are provided to the user via the server, allowing the user to receive product information and suggestions through the dialogue. The AI ​​uses natural language processing, such as NLTK.

[0523] Specific example

[0524] For example, if a user enters "I'm looking for a summer shirt," the server sends that input to the artificial intelligence, which then generates a response such as "What kind of design do you prefer?" This response is then provided to the user via the server.

[0525] Recording and analysis of dialogue logs

[0526] All interactions during a conversation session are recorded as conversation logs. The server saves these logs for later analysis and use in training the artificial intelligence. The logs are stored in a database and can be reviewed by the user later. Furthermore, past conversation logs are used to learn the user's preferences and generate more personalized suggestions.

[0527] End of session

[0528] At the end of a conversational session, the user sends an termination request to the server. The server receives this request, saves the session log to the database, and terminates the session. The saved log can be used for later analysis and review.

[0529] Example of a prompt

[0530] For example, if the user input is "I'm looking for a summer shirt," the following prompt message is sent to the AI ​​model.

[0531] User input: I'm looking for a summer shirt.

[0532] The above describes a specific form for carrying out the invention. This system allows users to enjoy a comfortable shopping experience within a virtual store.

[0533] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0534] Step 1:

[0535] The user enters user information such as name, ID, and password using a terminal. The entered data includes name and ID in text format, while the password is hashed for security.

[0536] Step 2:

[0537] The server receives information sent by the user and hashes the password. The hashed password and other user information are stored in the user authentication database. The specific data processing used here is password hashing.

[0538] Step 3:

[0539] The user enters their ID and password on the login screen via their device. The entered ID and password are sent to the server as a login request.

[0540] Step 4:

[0541] The server verifies the entered ID and password against the user authentication database. During this process, the password is hashed again and compared to the hash value in the database. If the correct authentication information is confirmed, an authentication token (session ID) is issued. The authentication token is unique and stored in the session management database.

[0542] Step 5:

[0543] An authenticated user sends a request to start a conversation session through their terminal. The server generates a unique session ID and provides it to the user. The session ID is an identifier used to identify a specific conversation session.

[0544] Step 6:

[0545] Users input inquiries and questions regarding product selection through their terminals. The entered inquiries are sent to the server. The specific data format used here is text data.

[0546] Step 7:

[0547] The server sends user input to an artificial intelligence (AI) with natural language processing capabilities. The AI ​​analyzes the user input and generates an appropriate response. NLTK or generative AI models are used in this process. A prompt such as "User input: I'm looking for a summer shirt" is sent to the AI.

[0548] Step 8:

[0549] The response generated by the artificial intelligence is sent back to the server. The server receives this response and displays it to the user. The output data format is text data.

[0550] Step 9:

[0551] All interactions during a dialogue session are recorded as a dialogue log. The server stores the dialogue log in a database and analyzes it as needed. The dialogue log is used later for AI training and user feedback.

[0552] Step 10:

[0553] The user sends a request to end the conversation session through their terminal. The server receives the termination request, saves the session log to the database, and terminates the session. The session log is accessible later and can be used for review.

[0554] The above outlines the specific processing steps of the system. This system allows users to smoothly consult about product selection within the virtual store and receive personalized suggestions.

[0555] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0556] This invention is a system that helps users objectively re-examine their own thoughts and feelings using artificial intelligence with natural language processing capabilities and an emotion engine. The system consists of several main components, each with a specific function.

[0557] 1. User registration and login

[0558] The server receives a new user registration request. When the user enters their name, ID, and password and submits them, the server receives this information, hashes the password, and stores it in the user authentication database. The user then logs in using the registered information. During login, the server authenticates the entered ID and password and, if correct, issues an authentication token. This authentication token is used by the user for subsequent operations.

[0559] For example, when a user creates a new account, the server receives and stores the user information. Then, the user enters their ID and password on the login screen and sends a login request. The server receives this and, if authentication is successful, issues a token.

[0560] 2. Start of the dialogue session

[0561] When a user initiates an interactive session, the server generates a session ID and provides it to the user. The session ID is unique and used to identify the relevant interactive session. Upon receiving a session initiation request, the server stores the session ID and user information in the session management database.

[0562] 3. Conversation progression and emotion recognition using natural language processing.

[0563] Users input questions or comments about their worries and emotions and send them to the server. The server sends the user's input to an artificial intelligence with natural language processing capabilities and simultaneously uses an emotion engine to recognize the user's emotions. The emotion engine extracts the emotional state from the user's input and provides this emotional information to the AI ​​to optimize the response. The AI ​​generates a response that takes the emotional information into account, and this response is provided to the user via the server.

[0564] For example, if a user submits a question like, "I've been feeling a lot of stress at work lately," the server sends that question to the artificial intelligence, and the emotion engine recognizes the user's emotion as "stress." Based on that information, the AI ​​generates a response asking, "What specifically is causing you stress?" and the server forwards that response to the user.

[0565] 4. Providing feedback and recording emotional data

[0566] The server records all interactions and emotional data during a dialogue session as a dialogue log. This dialogue log is used for later analysis and AI training. Users can also review their own dialogue logs later, providing them with valuable data for self-analysis.

[0567] 5. End of session

[0568] At the end of a conversational session, the user sends an termination request to the server. The server receives the request, saves the session log and sentiment data to the database, and terminates the session. The saved log can be accessed later and used by the user for reflection.

[0569] In this way, the present invention provides an effective means for users to objectively re-examine their worries and emotions, and realizes a system that solves the problems of conventional counseling services. By introducing an emotion engine, it becomes possible to accurately grasp the user's emotional state and provide appropriate feedback based on that.

[0570] The following describes the processing flow.

[0571] Step 1:

[0572] The user enters their name, ID, and password and sends a user registration request to the server.

[0573] Step 2:

[0574] The server stores the received user information in a database. Passwords are stored in hashed form.

[0575] Step 3:

[0576] The user enters their ID and password on the login screen and sends a login request to the server.

[0577] Step 4:

[0578] The server retrieves the corresponding hashed password from the database and checks if it matches the entered password. If they match, it issues an authentication token to the user.

[0579] Step 5:

[0580] The user uses an authentication token to send a request to the server to start an interactive session.

[0581] Step 6:

[0582] The server generates a new session ID and provides it to the user. The session ID, user information, and session log are stored for management purposes.

[0583] Step 7:

[0584] Users enter questions and comments within the dialogue session and send them to the server.

[0585] Step 8:

[0586] The server sends the questions received from the user to the emotion engine, which then analyzes the user's emotional state.

[0587] Step 9:

[0588] The emotion engine recognizes the user's emotions from the text of the question and generates emotion data.

[0589] Step 10:

[0590] The server sends the question along with sentiment data to the artificial intelligence, which then generates an appropriate response.

[0591] Step 11:

[0592] The artificial intelligence considers emotional data to generate the optimal response for the user and sends it to the server.

[0593] Step 12:

[0594] The server displays the response from the artificial intelligence to the user.

[0595] Step 13:

[0596] Users can continue to enter further questions and comments based on the AI's responses.

[0597] Step 14:

[0598] The server stores logs and sentiment data from all dialogue sessions, which will be used for future analysis and training of artificial intelligence.

[0599] Step 15:

[0600] If a user wishes to end a conversation session, they send a session termination request to the server.

[0601] Step 16:

[0602] The server saves the session log and sentiment data to the database and ends the conversation session. The user can then review the conversation log and sentiment data later.

[0603] (Example 2)

[0604] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0605] Traditional systems struggled to accurately understand users' emotions and psychological states and provide appropriate feedback based on that understanding. Furthermore, they lacked sufficient management and analysis of dialogue logs, failing to offer effective means for user self-analysis and improvement. As a result, conventional counseling services and support systems were subject to numerous limitations.

[0606] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0607] In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for performing authentication using the user authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence; means for recognizing the user's emotions using an emotion engine and providing the emotion information to the artificial intelligence to optimize the response; means for displaying the response from the artificial intelligence to the user; means for saving a log of the dialogue session and performing analysis as necessary; and means for ending the dialogue session. As a result, the user can simply input their emotions and concerns in natural language, and the artificial intelligence and emotion engine will work together to provide optimal feedback, and the dialogue log can be reviewed and analyzed later.

[0608] "User information" refers to information such as the name, ID, and password that a user needs to register with the system.

[0609] A "user authentication database" is a database used to store and verify user authentication information.

[0610] "User authentication information" refers to information such as the ID and password that a user uses to access the system.

[0611] A "dialogue session" refers to a series of interactions in which a user and artificial intelligence engage in dialogue.

[0612] A "session ID" is an identifier used to uniquely identify a specific dialogue session.

[0613] "Natural language processing functionality" refers to the technology used to analyze and understand natural language input from users.

[0614] "Artificial intelligence" is a computer system that possesses the knowledge to generate appropriate responses to user input.

[0615] An "emotion engine" is a technology that extracts and recognizes emotional states from user input.

[0616] A "dialogue log" is a record of the interactions and emotional data during a dialogue session.

[0617] "Analysis" refers to the act of analyzing the user's emotional state and the content of conversations based on saved dialogue logs.

[0618] "Feedback" refers to information that a system provides based on the user's input and emotional state.

[0619] This invention is a system that helps users objectively re-examine their own thoughts and feelings using artificial intelligence with natural language processing capabilities and an emotion engine. The system consists of several main components, each with a specific function.

[0620] User registration and login

[0621] The server receives a new user registration request. The user enters their name, ID, and password into the terminal and submits them. The server hashes the received password using a hashing algorithm (e.g., SHA-256) and stores the user information in the user authentication database. The user then logs in using the registered information. During login, the server compares the entered ID and password with the information in the database, and if authentication is successful, generates an authentication token and sends it to the user. This authentication token is used by the user when performing subsequent operations.

[0622] For example, a user might type "I want to create a new account" and click the submit button. The server receives this request, saves the user's information, and returns a message to the user saying "Your account has been created."

[0623] Start of the dialogue session

[0624] When a user initiates an interactive session, the server generates a unique session ID and provides it to the user. Upon receiving the session initiation request, the server stores the session ID and user information in the session management database.

[0625] For example, if a user types and submits "I want to start a conversation session," the server generates a new session ID and displays "Session started" on the user's screen.

[0626] Conversation progression and emotion recognition using natural language processing

[0627] The user enters comments about their worries and emotions and sends them to the server. The server sends these comments to a generative AI model with natural language processing capabilities (e.g., OpenAI's GPT-3). Simultaneously, the server uses an emotion engine (e.g., Affectiva) to recognize the user's emotions and extract emotion information. The server provides this emotion information to the generative AI model and requests it to generate the most appropriate response. The generative AI model generates a response that takes the emotion information into account, and the server sends that response to the user.

[0628] For example, a user might type and submit "I've been feeling a lot of stress at work lately." The server sends this input to a generative AI model and an emotion engine. The generative AI model, based on the emotion information "stress," generates a response asking "What specifically makes you feel stressed?" and sends it to the user.

[0629] Providing feedback and recording emotional data

[0630] The server records all interactions and emotional data during a dialogue session as a dialogue log. This dialogue log is used for later analysis and training of generative AI models. Users can review their own dialogue logs later and use them as data for self-analysis.

[0631] End of session

[0632] When a conversation session ends, the user sends an end request to the server. The server receives the request, saves the session log and sentiment data to the database, and terminates the session. The saved log is accessible to the user later and can be used for reflection.

[0633] For example, if a user types and submits "Please end the conversation session," the server saves the session log and returns the message "Session ended."

[0634] In summary, the present invention provides an effective means for users to objectively re-examine their own worries and emotions, and realizes a system that solves the problems of conventional counseling services.

[0635] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0636] Step 1: Receiving a user registration request

[0637] The server receives a new registration request from a user. The user enters their name, ID, and password on their terminal and sends them to the server. Upon receiving the input, the server hashes the password using a hashing algorithm (e.g., SHA-256). The hashed password and user information are then generated as output.

[0638] Specific example of operation: The user types "I want to create a new account" on their device and clicks the submit button.

[0639] Step 2: Saving User Information

[0640] The server stores the hashed password and other user information in the user authentication database. The input is the hashed password and user information generated in step 1, and the output is the success status.

[0641] Specific example of operation: User information is saved to the database, and the user receives a message saying "Account created."

[0642] Step 3: Receiving the login request

[0643] After registration, the user enters their ID and password on the login screen and sends a login request to the server. The input is the ID and password, which the server receives. The output is the generation of an authentication token.

[0644] Specific example of operation: The user enters their ID and password on their device and clicks the "Login" button.

[0645] Step 4: User Authentication

[0646] The server verifies the entered ID and password against the information in the user authentication database. The input is the ID and password provided by the user, and the process involves comparing the hashed password with the information in the database. The output is the generation of an authentication token.

[0647] Specific example of operation: If authentication is successful, "Login successful" will be displayed.

[0648] Step 5: Receiving a request for a dialogue session

[0649] The user sends a request to start a conversational session. Input is a click action or a prompt message such as "I want to start a conversational session." Output is the generation of a unique session ID.

[0650] Specific example of operation: The user types and submits "I want to start a conversation session."

[0651] Step 6: Generate a session ID

[0652] The server generates a unique session ID and provides it to the user. The input is a request for an interactive session, and the output is the session ID.

[0653] Specific example of operation: The server generates a new session ID, and the user's screen displays "Session started".

[0654] Step 7: User submits comment

[0655] Users enter comments about their worries and feelings and send them to the server. The input is the user's comment, and the output is the comment sent to the server.

[0656] Specific example of operation: The user types "I've been feeling a lot of stress at work lately" into the device and sends it.

[0657] Step 8: NLP Processing

[0658] The server sends user input to a generative AI model with natural language processing capabilities. The input is the user's comment, which the generative AI model analyzes. The output consists of the analysis result and the generated response.

[0659] Specific example of operation: The server sends the comment "I've been under a lot of stress at work lately" to the AI ​​model that generates the comment.

[0660] Step 9: Emotion Recognition

[0661] The server uses an emotion engine to recognize the user's emotions and extract emotion information. The input is the user's comment, and the emotion engine analyzes the emotion information. The output is the extracted emotion information.

[0662] Specific example of operation: The emotion engine recognizes the emotion "stress" from the user's comment.

[0663] Step 10: AI-powered response generation

[0664] The server provides sentiment information to an AI model and requests it to generate the optimal response. The input is sentiment information and the original comment, and the output is the optimized response.

[0665] Specific example of operation: The generating AI model considers emotional information such as "stress" and generates a response asking, "What specifically makes you feel stressed?"

[0666] Step 11: Sending a response to the user

[0667] The server sends a response from the AI ​​model to the user. The input is the response from the generating AI model, and the output is what is displayed to the user.

[0668] Specific example of operation: The response "What specifically is causing you stress?" will be displayed on the user's device.

[0669] Step 12: Recording the conversation log

[0670] The server records all interactions and sentiment data during a dialogue session as a dialogue log. The input is the interactions and sentiment data of the dialogue session, and the output is the dialogue log.

[0671] Specific example of operation: The server saves the interaction as a log in real time.

[0672] Step 13: Analysis and use of dialogue logs

[0673] The server analyzes the dialogue logs and uses the results to train the generative AI model. The input is the dialogue logs, and the output is the analysis results and updates to the generative AI model.

[0674] Specific example of operation: The server analyzes the saved dialogue logs and uses them to improve the performance of the generated AI model.

[0675] Step 14: Received a request to end the dialogue session.

[0676] The user requests the end of the interaction session. The input is the termination request, and the output is the start of the session termination process.

[0677] Specific example of operation: The user types and submits "Please end the conversation session."

[0678] Step 15: Saving session data

[0679] The server saves the session log and sentiment data to the database and then terminates the session. The inputs are the session log and sentiment data, and the outputs are "Saving complete" and "Session terminated."

[0680] Specific example of operation: The server returns the message "Session ended" to the user.

[0681] (Application Example 2)

[0682] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0683] Traditional in-store customer service often struggled to immediately understand and appropriately respond to customer emotions and opinions, potentially leading to decreased customer satisfaction. Furthermore, store staff lacked effective means to quickly grasp specific customer concerns and dissatisfactions. Therefore, there was a need for a system that could accurately understand customer emotions and provide appropriate responses in real time.

[0684] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for authenticating the user using the authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence; means for displaying the response from the artificial intelligence to the user; means for identifying the user's emotions using an emotion recognition engine, recording the identification result, and optimizing the corresponding response; means for saving a log of the dialogue session and performing analysis as necessary; and means for ending the dialogue session. This makes it possible to accurately grasp the emotions of customers in physical stores and respond quickly and appropriately.

[0685] "User information" refers to information necessary to identify and authenticate a user, such as the user's name, ID, and password.

[0686] A "user authentication database" is a database used to securely store user information and utilize it in the authentication process.

[0687] A "session ID" is an identifier generated to uniquely identify an interactive session.

[0688] "Natural language processing" refers to the technology used to analyze and understand user input in natural language.

[0689] "Artificial intelligence" is a system that possesses natural language processing capabilities and generates appropriate responses based on user input.

[0690] A "response" is the answer or message that artificial intelligence generates in response to user input.

[0691] An "emotion recognition engine" is a system that identifies emotions from user input and provides information about those emotions.

[0692] A "dialogue session log" is a record of the interactions that took place between the user and the system.

[0693] "Analysis" is the process of using dialogue session logs and identified sentiment data to find specific patterns and insights.

[0694] "System" refers to the entire computer system, including the means and functions described above.

[0695] This invention is a system that enables accurate understanding of customer emotions in physical stores and facilitates quick and appropriate responses. A specific embodiment of this system is described below.

[0696] User registration and login

[0697] The server receives a new registration request from a user and retrieves the name, ID, and password entered by the user.

[0698] The server hashes the password and stores the result in the user authentication database.

[0699] Once registration is complete, users enter their ID and password on the login screen and submit a login request.

[0700] The server authenticates the entered ID and password, and if successful, generates an authentication token and provides it to the user.

[0701] Start of the dialogue session

[0702] When a user initiates a conversational session, the server generates a unique session ID and provides it to the user.

[0703] Session IDs and user information are stored in the session management database.

[0704] Conversation progression and emotion recognition using natural language processing

[0705] Users input questions and comments about their worries and feelings via their smartphones or smart glasses and send them to the server.

[0706] The server sends this input to an artificial intelligence with natural language processing capabilities (such as the Google NLP API).

[0707] Simultaneously, the server uses an emotion recognition engine (such as the Microsoft Azure Emotion API) to identify the user's emotions.

[0708] The emotion recognition engine extracts the emotional state, provides that information to the artificial intelligence, and generates the optimal response.

[0709] The generated response is provided to the user via the server.

[0710] Providing feedback and recording emotional data

[0711] The server records all interactions and emotional data during a dialogue session as a dialogue log.

[0712] The dialogue logs will be used for later analysis and for training artificial intelligence.

[0713] Users can review their conversation logs later.

[0714] End of session

[0715] At the end of a conversational session, the user sends an termination request to the server.

[0716] The server saves the session log and sentiment data to the database and then terminates the session.

[0717] Specific example

[0718] The following are examples of prompt statements for a generative AI model:

[0719] User: "I've been dissatisfied with the service at this store recently."

[0720] System: "Could you please tell us specifically what caused your dissatisfaction?"

[0721] This system is expected to significantly improve customer service in physical stores and increase customer satisfaction. The server receives user input via smartphones and smart glasses, and uses artificial intelligence and an emotion recognition engine to provide effective responses. In this way, store staff can respond to customers quickly and appropriately.

[0722] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0723] Step 1:

[0724] This system accepts new user registration requests. Users enter their name, ID, and password via their device and send this data to the server. The server receives this user information, hashes the password, and stores it in the user authentication database. Input: User's name, ID, and password. Output: Hashed password, and a message confirming registration to the authentication database.

[0725] Step 2:

[0726] The user enters their ID and password on the login screen using their device and sends a login request to the server. The server verifies the user's ID and hashed password against the authentication database, and if authentication is successful, generates an authentication token and provides it to the user. Input: User ID, password. Output: Authentication token.

[0727] Step 3:

[0728] An authenticated user sends a request to the server to start an interactive session from their terminal. The server generates a unique session ID, provides it to the user, and stores the session ID and user information in the session management database. Input: Interactive session start request. Output: Session ID, registration in the session management database.

[0729] Step 4:

[0730] Users input questions and comments about their worries and emotions via their device and send them to the server. The server receives the input from the user and sends it to an artificial intelligence with natural language processing capabilities (such as the Google NLP API). Input: User's questions and comments. Output: Analysis results from natural language processing.

[0731] Step 5:

[0732] The server simultaneously uses an emotion recognition engine (such as the Microsoft Azure Emotion API) to identify the user's emotional state from their input. The emotion recognition engine extracts the user's emotions, provides this information to the artificial intelligence, and generates an appropriate response. Input: User's questions and comments. Output: Emotion recognition results, optimized response.

[0733] Step 6:

[0734] The server receives the generated response and provides it to the user through the terminal. This allows the user to receive feedback about their concerns and emotions. The server also records all interactions and emotional data during the conversation session as a dialogue log, preparing for analysis as needed. Input: Optimized response. Output: Providing the response to the user, recording the dialogue log.

[0735] Step 7:

[0736] When a user requests to end a conversation session, the server saves the session log and sentiment data to the database and terminates the session. The user can review the conversation log later. Input: Session termination request. Output: Saving of session log and sentiment data, session termination message.

[0737] By following the above steps, the system will be able to accurately understand the user's emotions and respond quickly and appropriately.

[0738] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0739] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0740] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0741] [Third Embodiment]

[0742] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0743] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0744] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0745] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0746] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0747] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0748] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0749] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0750] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0751] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0752] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0753] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0754] This invention is a system that uses artificial intelligence with natural language processing capabilities to help users objectively re-examine their own thoughts and feelings. The system consists of several main components, each with a specific function.

[0755] 1. User registration and login

[0756] The server receives a new user registration request. When the user enters their name, ID, and password and submits them, the server receives this information, hashes the password, and stores it in the user authentication database. The user then logs in using the registered information. During login, the server authenticates the entered ID and password and, if correct, issues an authentication token. This authentication token is used by the user for subsequent operations.

[0757] For example, when a user creates a new account, the server receives and stores the user information. Then, the user enters their ID and password on the login screen and sends a login request. The server receives this and, if authentication is successful, issues a token.

[0758] 2. Start of the dialogue session

[0759] When a user initiates an interactive session, the server generates a session ID and provides it to the user. The session ID is unique and used to identify the relevant interactive session. Upon receiving a session initiation request, the server stores the session ID and user information in the session management database.

[0760] 3. Conversation progression using natural language processing

[0761] Users input questions or comments about their worries and emotions and send them to the server. The server sends the user's input to an artificial intelligence with natural language processing capabilities, which then generates appropriate responses to the questions. These responses are then provided to the user via the server. This allows users to organize their emotions and thoughts through dialogue.

[0762] For example, if a user submits a question such as "I've been feeling stressed at work lately," the server sends the question to the artificial intelligence and waits for a response. The AI ​​generates a response such as "What specifically is causing you stress?", and the server forwards this response to the user.

[0763] 4. Providing and recording feedback

[0764] The server records all interactions during a dialogue session as a dialogue log. This dialogue log is used for later analysis and AI training. Users can also review their own dialogue logs later, providing them with valuable data for self-analysis.

[0765] 5. End of session

[0766] At the end of a conversational session, the user sends an termination request to the server. The server receives the request, saves the session log to the database, and terminates the session. The saved log can be accessed later and used by the user for review.

[0767] In this way, the present invention provides an effective means for users to objectively re-examine their worries and emotions, and realizes a system that solves the problems of conventional counseling services.

[0768] The following describes the processing flow.

[0769] Step 1:

[0770] The user enters their name, ID, and password and sends a user registration request to the server.

[0771] Step 2:

[0772] The server stores the received user information in a database. Passwords are stored in hashed form.

[0773] Step 3:

[0774] The user enters their ID and password on the login screen and sends a login request to the server.

[0775] Step 4:

[0776] The server retrieves the corresponding hashed password from the database and checks if it matches the entered password. If they match, it issues an authentication token to the user.

[0777] Step 5:

[0778] The user uses an authentication token to send a request to the server to start an interactive session.

[0779] Step 6:

[0780] The server generates a new session ID and provides it to the user. The session ID, user information, and session log are stored for management purposes.

[0781] Step 7:

[0782] Users enter questions and comments within the dialogue session and send them to the server.

[0783] Step 8:

[0784] The server sends the questions received from the user to the artificial intelligence and receives a response from the AI.

[0785] Step 9:

[0786] The server displays the response from the artificial intelligence to the user.

[0787] Step 10:

[0788] Users can continue to enter further questions and comments based on the AI's responses.

[0789] Step 11:

[0790] The server saves logs of all dialogue sessions for future analysis and use in training artificial intelligence.

[0791] Step 12:

[0792] If a user wishes to end a conversation session, they send a session termination request to the server.

[0793] Step 13:

[0794] The server saves the session log to the database and terminates the interactive session. The user can then review the interactive log later.

[0795] (Example 1)

[0796] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0797] Traditional counseling services have many shortcomings. In particular, they lack appropriate means for users to objectively re-examine their own emotions and problems. Furthermore, the counseling process and feedback are inconsistent, and sufficient data for later review is not stored. As a result, there is a lack of useful information for users to conduct self-analysis, making effective counseling difficult.

[0798] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0799] In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for authenticating the user using the authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to a generative AI model having natural language processing capabilities and receiving a response from the generative AI model; means for displaying the response from the generative AI model to the user; means for recording a log of the dialogue session and performing analysis as necessary; means for allowing the user to review the recorded dialogue session log later; and means for ending the dialogue session. This makes it possible to provide a means for the user to objectively re-examine their emotions and worries and to conduct effective self-analysis. Furthermore, it enables a consistent counseling process and allows for sufficient storage of data for feedback and reflection.

[0800] "User information" refers to identification information such as the user's name, ID, and password that they register in the system.

[0801] A "user authentication database" is a database used to store user information and authenticate users.

[0802] "Authentication information" refers to information such as a user ID and password that a user uses to access the system.

[0803] A "session ID" is a unique identifier generated by the server and provided to the user to identify an interactive session.

[0804] "Natural language processing capabilities" refer to the technology used to understand and process human language, and generative AI models possess this capability.

[0805] A "generative AI model" is an artificial intelligence model that generates appropriate responses to user input.

[0806] A "response" is the answer that a generative AI model generates based on the user's input.

[0807] A "dialogue session" is a series of processes in which a user interacts with a generative AI model.

[0808] A "dialogue log" is data that records all interactions during a dialogue session.

[0809] "Analysis" is the process of analyzing data using dialogue logs and extracting useful information.

[0810] "Reflection" is the process of reviewing saved conversation logs later and conducting self-analysis and re-evaluation.

[0811] This invention is a system that helps users objectively re-examine their own thoughts and feelings using artificial intelligence (generative AI model) with natural language processing capabilities. This system consists of several main components, each with a specific function.

[0812] The system's hardware includes servers, user terminals (such as personal computers and smartphones), and a database. The software includes a user authentication system, a generative AI model with natural language processing capabilities, and a database management system.

[0813] User registration and login

[0814] The user first enters their name, ID, and password and sends them to the server. The server receives this information, hashes the password, and stores it in the user authentication database. The user then enters their ID and password on the login screen and receives an authentication token. This token is used to perform subsequent operations.

[0815] Start of the dialogue session

[0816] When a user initiates an interactive session, the server generates a new session ID and provides it to the user. The server stores the session ID and user information in the session management database and then initiates the interactive session.

[0817] Conversation progression using natural language processing

[0818] Users input questions or comments about their worries or feelings and send them to the server. The server sends the user's input to a generating AI model and generates an appropriate response. The generated response is then provided to the user via the server.

[0819] Specific example:

[0820] If a user enters "I've been under a lot of stress at work lately," the server sends this message to a generating AI model. The generating AI model then generates a response asking "What specifically is causing you stress?", which the server then sends to the user.

[0821] Providing and recording feedback

[0822] The server records all interactions during a dialogue session as a dialogue log and stores it in a database. This dialogue log can be reviewed by the user later and used to train generative AI models. Users can also review their own dialogue logs later.

[0823] End of session

[0824] At the end of an interactive session, the user sends a session termination request to the server. The server receives this request, saves the session log to the database, and terminates the session.

[0825] Example of a prompt:

[0826] User: "I've been under a lot of stress at work lately."

[0827] AI: "Specifically, what aspects are causing you stress?"

[0828] In this way, the present invention is a system that provides an effective means for users to objectively re-examine their worries and emotions and to conduct self-analysis. Furthermore, this system solves the problems of conventional counseling services and supports users in maintaining better mental health.

[0829] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0830] Step 1: User Registration

[0831] 1. Input: The user enters their name, ID, and password.

[0832] 2. Specific operation: The terminal sends the entered information to the server in JSON format.

[0833] 3. Data Processing and Calculation: The server processes the received information and hashes the password using the bcrypt algorithm.

[0834] 4. Output: The hashed password and user information are stored in the user authentication database. The server sends a registration completion message to the user.

[0835] Step 2: Log in

[0836] 1. Input: The user enters their ID and password on the login screen.

[0837] 2. Specific operation: The terminal sends the input information to the server in JSON format.

[0838] 3. Data Processing and Calculation: The server receives the user ID and password and compares them against the hashed password in the database.

[0839] 4. Output: If authentication is successful, the server generates an authentication token and sends it to the user.

[0840] Step 3: Start the dialogue session

[0841] 1. Input: The user sends a request to start a conversation session.

[0842] 2. Specific action: The terminal sends a request to the server.

[0843] 3. Data processing and calculation: The server generates a new session ID and saves that ID to the session management database.

[0844] 4. Output: The server provides the user with a session ID.

[0845] Step 4: Conversation progression using natural language processing

[0846] 1. Input: The user enters a message about their worries or feelings.

[0847] 2. Specific action: The terminal sends the entered message to the server.

[0848] 3. Data Processing and Calculation: The server sends the message to the AI ​​model that generates the appropriate response.

[0849] 4. Output: The server sends the generated response to the user. For example, if the user inputs "I've been under a lot of stress at work lately," the AI ​​model will generate the response "What specifically is causing you stress?" and the server will send this to the user.

[0850] Step 5: Provide and record feedback

[0851] 1. Input: The user sends a request to review the conversation log later.

[0852] 2. Specific operation: The server records all interactions during the conversation session as a conversation log.

[0853] 3. Data Processing and Calculation: Recorded dialogue logs are saved to a database and used to train the generated AI model as needed.

[0854] 4. Output: The server will provide the user with the recorded conversation log upon request.

[0855] Step 6: Ending the session

[0856] 1. Input: The user sends a request to end the conversation session.

[0857] 2. Specific action: The terminal sends a termination request to the server.

[0858] 3. Data processing and calculation: The server saves the session log to the database.

[0859] 4. Output: The server sends a session termination notification to the user.

[0860] Through the steps outlined above, a system will be created that allows users to objectively re-examine their worries and emotions and conduct effective self-analysis.

[0861] (Application Example 1)

[0862] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0863] In modern virtual stores, customers are expected to receive appropriate advice and personalized product recommendations when making purchases. However, conventional systems do not adequately provide immediate natural language processing responses to customer inquiries or individualized recommendations based on past logs. As a result, customers lack sufficient means to have a satisfying purchasing experience, which diminishes their desire to buy.

[0864] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0865] In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for authenticating the user using the authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence; means for displaying the response from the artificial intelligence to the user; means for storing a log of the dialogue session and performing analysis as necessary; means for ending the dialogue session; means for receiving consultation from the customer regarding product selection within the virtual store and making product suggestions based on natural language processing; and means for learning the user's preferences using past dialogue logs and generating personalized suggestions. As a result, customers can receive consultation regarding appropriate product selection within the virtual store, receive personalized services, and obtain a more satisfying purchasing experience.

[0866] "User information" refers to information that a user enters for their own identification and authentication, and includes names, IDs, passwords, etc.

[0867] A "user authentication database" is a database that securely stores user authentication information and supports the authentication process.

[0868] A "session ID" is a unique identifier that is generated and provided to the user to identify a specific interaction session.

[0869] "Artificial intelligence with natural language processing capabilities" refers to an artificial intelligence system that understands human language and has the ability to generate appropriate responses based on that input.

[0870] A "dialogue session log" is data that records all interactions that took place during a dialogue session with a user, and is used for later analysis and training of artificial intelligence.

[0871] A "virtual store" is a virtual store that exists on the internet, a platform where customers can select and purchase products online.

[0872] "Personalized suggestions" is an approach that individually proposes the most suitable products and services to each user based on past conversation logs and user preferences.

[0873] The system for implementing this invention performs user information input and authentication, dialogue session management, response generation using natural language processing, and dialogue log recording and analysis. To realize these functions, the following steps are taken between the server, terminal, and user.

[0874] User information input and authentication

[0875] The user enters user information such as their name, ID, and password using a device (smartphone or head-mounted display). The server receives this information, hashes the password, and stores it in the user authentication database. During login, the server verifies the entered ID and password, and if correct, issues an authentication token (session ID). This authentication token is required for the user to perform subsequent operations.

[0876] Managing dialogue sessions

[0877] When an authenticated user initiates an interactive session, the server generates a unique session ID and provides it to the user. This session ID is used to identify a specific interactive session and is stored in the session management database. Upon receiving a session initiation request, the server manages the session ID and user information internally.

[0878] Response generation using natural language processing

[0879] Users input questions and inquiries about product selection through their terminals. The server sends this input to an artificial intelligence (AI) with natural language processing capabilities, which generates appropriate responses. The generated responses are provided to the user via the server, allowing the user to receive product information and suggestions through the dialogue. The AI ​​uses natural language processing, such as NLTK.

[0880] Specific example

[0881] For example, if a user enters "I'm looking for a summer shirt," the server sends that input to the artificial intelligence, which then generates a response such as "What kind of design do you prefer?" This response is then provided to the user via the server.

[0882] Recording and analysis of dialogue logs

[0883] All interactions during a conversation session are recorded as conversation logs. The server saves these logs for later analysis and use in training the artificial intelligence. The logs are stored in a database and can be reviewed by the user later. Furthermore, past conversation logs are used to learn the user's preferences and generate more personalized suggestions.

[0884] End of session

[0885] At the end of a conversational session, the user sends an termination request to the server. The server receives this request, saves the session log to the database, and terminates the session. The saved log can be used for later analysis and review.

[0886] Example of a prompt

[0887] For example, if the user input is "I'm looking for a summer shirt," the following prompt message is sent to the AI ​​model.

[0888] User input: I'm looking for a summer shirt.

[0889] The above describes a specific form for carrying out the invention. This system allows users to enjoy a comfortable shopping experience within a virtual store.

[0890] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0891] Step 1:

[0892] The user enters user information such as name, ID, and password using a terminal. The entered data includes name and ID in text format, while the password is hashed for security.

[0893] Step 2:

[0894] The server receives information sent by the user and hashes the password. The hashed password and other user information are stored in the user authentication database. The specific data processing used here is password hashing.

[0895] Step 3:

[0896] The user enters their ID and password on the login screen via their device. The entered ID and password are sent to the server as a login request.

[0897] Step 4:

[0898] The server verifies the entered ID and password against the user authentication database. During this process, the password is hashed again and compared to the hash value in the database. If the correct authentication information is confirmed, an authentication token (session ID) is issued. The authentication token is unique and stored in the session management database.

[0899] Step 5:

[0900] An authenticated user sends a request to start a conversation session through their terminal. The server generates a unique session ID and provides it to the user. The session ID is an identifier used to identify a specific conversation session.

[0901] Step 6:

[0902] Users input inquiries and questions regarding product selection through their terminals. The entered inquiries are sent to the server. The specific data format used here is text data.

[0903] Step 7:

[0904] The server sends user input to an artificial intelligence (AI) with natural language processing capabilities. The AI ​​analyzes the user input and generates an appropriate response. NLTK or generative AI models are used in this process. A prompt such as "User input: I'm looking for a summer shirt" is sent to the AI.

[0905] Step 8:

[0906] The response generated by the artificial intelligence is sent back to the server. The server receives this response and displays it to the user. The output data format is text data.

[0907] Step 9:

[0908] All interactions during a dialogue session are recorded as a dialogue log. The server stores the dialogue log in a database and analyzes it as needed. The dialogue log is used later for AI training and user feedback.

[0909] Step 10:

[0910] The user sends a request to end the conversation session through their terminal. The server receives the termination request, saves the session log to the database, and terminates the session. The session log is accessible later and can be used for review.

[0911] The above outlines the specific processing steps of the system. This system allows users to smoothly consult about product selection within the virtual store and receive personalized suggestions.

[0912] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0913] This invention is a system that helps users objectively re-examine their own thoughts and feelings using artificial intelligence with natural language processing capabilities and an emotion engine. The system consists of several main components, each with a specific function.

[0914] 1. User registration and login

[0915] The server receives a new user registration request. When the user enters their name, ID, and password and submits them, the server receives this information, hashes the password, and stores it in the user authentication database. The user then logs in using the registered information. During login, the server authenticates the entered ID and password and, if correct, issues an authentication token. This authentication token is used by the user for subsequent operations.

[0916] For example, when a user creates a new account, the server receives and stores the user information. Then, the user enters their ID and password on the login screen and sends a login request. The server receives this and, if authentication is successful, issues a token.

[0917] 2. Start of the dialogue session

[0918] When a user initiates an interactive session, the server generates a session ID and provides it to the user. The session ID is unique and used to identify the relevant interactive session. Upon receiving a session initiation request, the server stores the session ID and user information in the session management database.

[0919] 3. Conversation progression and emotion recognition using natural language processing.

[0920] Users input questions or comments about their worries and emotions and send them to the server. The server sends the user's input to an artificial intelligence with natural language processing capabilities and simultaneously uses an emotion engine to recognize the user's emotions. The emotion engine extracts the emotional state from the user's input and provides this emotional information to the AI ​​to optimize the response. The AI ​​generates a response that takes the emotional information into account, and this response is provided to the user via the server.

[0921] For example, if a user submits a question like, "I've been feeling a lot of stress at work lately," the server sends that question to the artificial intelligence, and the emotion engine recognizes the user's emotion as "stress." Based on that information, the AI ​​generates a response asking, "What specifically is causing you stress?" and the server forwards that response to the user.

[0922] 4. Providing feedback and recording emotional data

[0923] The server records all interactions and emotional data during a dialogue session as a dialogue log. This dialogue log is used for later analysis and AI training. Users can also review their own dialogue logs later, providing them with valuable data for self-analysis.

[0924] 5. End of session

[0925] At the end of a conversational session, the user sends an termination request to the server. The server receives the request, saves the session log and sentiment data to the database, and terminates the session. The saved log can be accessed later and used by the user for reflection.

[0926] In this way, the present invention provides an effective means for users to objectively re-examine their worries and emotions, and realizes a system that solves the problems of conventional counseling services. By introducing an emotion engine, it becomes possible to accurately grasp the user's emotional state and provide appropriate feedback based on that.

[0927] The following describes the processing flow.

[0928] Step 1:

[0929] The user enters their name, ID, and password and sends a user registration request to the server.

[0930] Step 2:

[0931] The server stores the received user information in a database. Passwords are stored in hashed form.

[0932] Step 3:

[0933] The user enters their ID and password on the login screen and sends a login request to the server.

[0934] Step 4:

[0935] The server retrieves the corresponding hashed password from the database and checks if it matches the entered password. If they match, it issues an authentication token to the user.

[0936] Step 5:

[0937] The user uses an authentication token to send a request to the server to start an interactive session.

[0938] Step 6:

[0939] The server generates a new session ID and provides it to the user. The session ID, user information, and session log are stored for management purposes.

[0940] Step 7:

[0941] Users enter questions and comments within the dialogue session and send them to the server.

[0942] Step 8:

[0943] The server sends the questions received from the user to the emotion engine, which then analyzes the user's emotional state.

[0944] Step 9:

[0945] The emotion engine recognizes the user's emotions from the text of the question and generates emotion data.

[0946] Step 10:

[0947] The server sends the question along with sentiment data to the artificial intelligence, which then generates an appropriate response.

[0948] Step 11:

[0949] The artificial intelligence considers emotional data to generate the optimal response for the user and sends it to the server.

[0950] Step 12:

[0951] The server displays the response from the artificial intelligence to the user.

[0952] Step 13:

[0953] Users can continue to enter further questions and comments based on the AI's responses.

[0954] Step 14:

[0955] The server stores logs and sentiment data from all dialogue sessions, which will be used for future analysis and training of artificial intelligence.

[0956] Step 15:

[0957] If a user wishes to end a conversation session, they send a session termination request to the server.

[0958] Step 16:

[0959] The server saves the session log and sentiment data to the database and ends the conversation session. The user can then review the conversation log and sentiment data later.

[0960] (Example 2)

[0961] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0962] Traditional systems struggled to accurately understand users' emotions and psychological states and provide appropriate feedback based on that understanding. Furthermore, they lacked sufficient management and analysis of dialogue logs, failing to offer effective means for user self-analysis and improvement. As a result, conventional counseling services and support systems were subject to numerous limitations.

[0963] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0964] In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for performing authentication using the user authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence; means for recognizing the user's emotions using an emotion engine and providing the emotion information to the artificial intelligence to optimize the response; means for displaying the response from the artificial intelligence to the user; means for saving a log of the dialogue session and performing analysis as necessary; and means for ending the dialogue session. As a result, the user can simply input their emotions and concerns in natural language, and the artificial intelligence and emotion engine will work together to provide optimal feedback, and the dialogue log can be reviewed and analyzed later.

[0965] "User information" refers to information such as the name, ID, and password that a user needs to register with the system.

[0966] A "user authentication database" is a database used to store and verify user authentication information.

[0967] "User authentication information" refers to information such as the ID and password that a user uses to access the system.

[0968] A "dialogue session" refers to a series of interactions in which a user and artificial intelligence engage in dialogue.

[0969] A "session ID" is an identifier used to uniquely identify a specific dialogue session.

[0970] "Natural language processing functionality" refers to the technology used to analyze and understand natural language input from users.

[0971] "Artificial intelligence" is a computer system that possesses the knowledge to generate appropriate responses to user input.

[0972] An "emotion engine" is a technology that extracts and recognizes emotional states from user input.

[0973] A "dialogue log" is a record of the interactions and emotional data during a dialogue session.

[0974] "Analysis" refers to the act of analyzing the user's emotional state and the content of conversations based on saved dialogue logs.

[0975] "Feedback" refers to information that a system provides based on the user's input and emotional state.

[0976] This invention is a system that helps users objectively re-examine their own thoughts and feelings using artificial intelligence with natural language processing capabilities and an emotion engine. The system consists of several main components, each with a specific function.

[0977] User registration and login

[0978] The server receives a new user registration request. The user enters their name, ID, and password into the terminal and submits them. The server hashes the received password using a hashing algorithm (e.g., SHA-256) and stores the user information in the user authentication database. The user then logs in using the registered information. During login, the server compares the entered ID and password with the information in the database, and if authentication is successful, generates an authentication token and sends it to the user. This authentication token is used by the user when performing subsequent operations.

[0979] For example, a user might type "I want to create a new account" and click the submit button. The server receives this request, saves the user's information, and returns a message to the user saying "Your account has been created."

[0980] Start of the dialogue session

[0981] When a user initiates an interactive session, the server generates a unique session ID and provides it to the user. Upon receiving the session initiation request, the server stores the session ID and user information in the session management database.

[0982] For example, if a user types and submits "I want to start a conversation session," the server generates a new session ID and displays "Session started" on the user's screen.

[0983] Conversation progression and emotion recognition using natural language processing

[0984] The user enters comments about their worries and emotions and sends them to the server. The server sends these comments to a generative AI model with natural language processing capabilities (e.g., OpenAI's GPT-3). Simultaneously, the server uses an emotion engine (e.g., Affectiva) to recognize the user's emotions and extract emotion information. The server provides this emotion information to the generative AI model and requests it to generate the most appropriate response. The generative AI model generates a response that takes the emotion information into account, and the server sends that response to the user.

[0985] For example, a user might type and submit "I've been feeling a lot of stress at work lately." The server sends this input to a generative AI model and an emotion engine. The generative AI model, based on the emotion information "stress," generates a response asking "What specifically makes you feel stressed?" and sends it to the user.

[0986] Providing feedback and recording emotional data

[0987] The server records all interactions and emotional data during a dialogue session as a dialogue log. This dialogue log is used for later analysis and training of generative AI models. Users can review their own dialogue logs later and use them as data for self-analysis.

[0988] End of session

[0989] When a conversation session ends, the user sends an end request to the server. The server receives the request, saves the session log and sentiment data to the database, and terminates the session. The saved log is accessible to the user later and can be used for reflection.

[0990] For example, if a user types and submits "Please end the conversation session," the server saves the session log and returns the message "Session ended."

[0991] In summary, the present invention provides an effective means for users to objectively re-examine their own worries and emotions, and realizes a system that solves the problems of conventional counseling services.

[0992] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0993] Step 1: Receiving a user registration request

[0994] The server receives a new registration request from a user. The user enters their name, ID, and password on their terminal and sends them to the server. Upon receiving the input, the server hashes the password using a hashing algorithm (e.g., SHA-256). The hashed password and user information are then generated as output.

[0995] Specific example of operation: The user types "I want to create a new account" on their device and clicks the submit button.

[0996] Step 2: Saving User Information

[0997] The server stores the hashed password and other user information in the user authentication database. The input is the hashed password and user information generated in step 1, and the output is the success status.

[0998] Specific example of operation: User information is saved to the database, and the user receives a message saying "Account created."

[0999] Step 3: Receiving the login request

[1000] After registration, the user enters their ID and password on the login screen and sends a login request to the server. The input is the ID and password, which the server receives. The output is the generation of an authentication token.

[1001] Specific example of operation: The user enters their ID and password on their device and clicks the "Login" button.

[1002] Step 4: User Authentication

[1003] The server verifies the entered ID and password against the information in the user authentication database. The input is the ID and password provided by the user, and the process involves comparing the hashed password with the information in the database. The output is the generation of an authentication token.

[1004] Specific example of operation: If authentication is successful, "Login successful" will be displayed.

[1005] Step 5: Receiving a request for a dialogue session

[1006] The user sends a request to start a conversational session. Input is a click action or a prompt message such as "I want to start a conversational session." Output is the generation of a unique session ID.

[1007] Specific example of operation: The user types and submits "I want to start a conversation session."

[1008] Step 6: Generate a session ID

[1009] The server generates a unique session ID and provides it to the user. The input is a request for an interactive session, and the output is the session ID.

[1010] Specific example of operation: The server generates a new session ID, and the user's screen displays "Session started".

[1011] Step 7: User submits comment

[1012] Users enter comments about their worries and feelings and send them to the server. The input is the user's comment, and the output is the comment sent to the server.

[1013] Specific example of operation: The user types "I've been feeling a lot of stress at work lately" into the device and sends it.

[1014] Step 8: NLP Processing

[1015] The server sends user input to a generative AI model with natural language processing capabilities. The input is the user's comment, which the generative AI model analyzes. The output consists of the analysis result and the generated response.

[1016] Specific example of operation: The server sends the comment "I've been under a lot of stress at work lately" to the AI ​​model that generates the comment.

[1017] Step 9: Emotion Recognition

[1018] The server uses an emotion engine to recognize the user's emotions and extract emotion information. The input is the user's comment, and the emotion engine analyzes the emotion information. The output is the extracted emotion information.

[1019] Specific example of operation: The emotion engine recognizes the emotion "stress" from the user's comment.

[1020] Step 10: AI-powered response generation

[1021] The server provides sentiment information to an AI model and requests it to generate the optimal response. The input is sentiment information and the original comment, and the output is the optimized response.

[1022] Specific example of operation: The generating AI model considers emotional information such as "stress" and generates a response asking, "What specifically makes you feel stressed?"

[1023] Step 11: Sending a response to the user

[1024] The server sends a response from the AI ​​model to the user. The input is the response from the generating AI model, and the output is what is displayed to the user.

[1025] Specific example of operation: The response "What specifically is causing you stress?" will be displayed on the user's device.

[1026] Step 12: Recording the conversation log

[1027] The server records all interactions and sentiment data during a dialogue session as a dialogue log. The input is the interactions and sentiment data of the dialogue session, and the output is the dialogue log.

[1028] Specific example of operation: The server saves the interaction as a log in real time.

[1029] Step 13: Analysis and use of dialogue logs

[1030] The server analyzes the dialogue logs and uses the results to train the generative AI model. The input is the dialogue logs, and the output is the analysis results and updates to the generative AI model.

[1031] Specific example of operation: The server analyzes the saved dialogue logs and uses them to improve the performance of the generated AI model.

[1032] Step 14: Received a request to end the dialogue session.

[1033] The user requests the end of the interaction session. The input is the termination request, and the output is the start of the session termination process.

[1034] Specific example of operation: The user types and submits "Please end the conversation session."

[1035] Step 15: Saving session data

[1036] The server saves the session log and sentiment data to the database and then terminates the session. The inputs are the session log and sentiment data, and the outputs are "Saving complete" and "Session terminated."

[1037] Specific example of operation: The server returns the message "Session ended" to the user.

[1038] (Application Example 2)

[1039] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1040] Traditional in-store customer service often struggled to immediately understand and appropriately respond to customer emotions and opinions, potentially leading to decreased customer satisfaction. Furthermore, store staff lacked effective means to quickly grasp specific customer concerns and dissatisfactions. Therefore, there was a need for a system that could accurately understand customer emotions and provide appropriate responses in real time.

[1041] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for authenticating the user using the authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence; means for displaying the response from the artificial intelligence to the user; means for identifying the user's emotions using an emotion recognition engine, recording the identification result, and optimizing the corresponding response; means for saving a log of the dialogue session and performing analysis as necessary; and means for ending the dialogue session. This makes it possible to accurately grasp the emotions of customers in physical stores and respond quickly and appropriately.

[1042] "User information" refers to information necessary to identify and authenticate a user, such as the user's name, ID, and password.

[1043] A "user authentication database" is a database used to securely store user information and utilize it in the authentication process.

[1044] A "session ID" is an identifier generated to uniquely identify an interactive session.

[1045] "Natural language processing" refers to the technology used to analyze and understand user input in natural language.

[1046] "Artificial intelligence" is a system that possesses natural language processing capabilities and generates appropriate responses based on user input.

[1047] A "response" is the answer or message that artificial intelligence generates in response to user input.

[1048] An "emotion recognition engine" is a system that identifies emotions from user input and provides information about those emotions.

[1049] A "dialogue session log" is a record of the interactions that took place between the user and the system.

[1050] "Analysis" is the process of using dialogue session logs and identified sentiment data to find specific patterns and insights.

[1051] "System" refers to the entire computer system, including the means and functions described above.

[1052] This invention is a system that enables accurate understanding of customer emotions in physical stores and facilitates quick and appropriate responses. A specific embodiment of this system is described below.

[1053] User registration and login

[1054] The server receives a new registration request from a user and retrieves the name, ID, and password entered by the user.

[1055] The server hashes the password and stores the result in the user authentication database.

[1056] Once registration is complete, users enter their ID and password on the login screen and submit a login request.

[1057] The server authenticates the entered ID and password, and if successful, generates an authentication token and provides it to the user.

[1058] Start of the dialogue session

[1059] When a user initiates a conversational session, the server generates a unique session ID and provides it to the user.

[1060] Session IDs and user information are stored in the session management database.

[1061] Conversation progression and emotion recognition using natural language processing

[1062] Users input questions and comments about their worries and feelings via their smartphones or smart glasses and send them to the server.

[1063] The server sends this input to an artificial intelligence with natural language processing capabilities (such as the Google NLP API).

[1064] Simultaneously, the server uses an emotion recognition engine (such as the Microsoft Azure Emotion API) to identify the user's emotions.

[1065] The emotion recognition engine extracts the emotional state, provides that information to the artificial intelligence, and generates the optimal response.

[1066] The generated response is provided to the user via the server.

[1067] Providing feedback and recording emotional data

[1068] The server records all interactions and emotional data during a dialogue session as a dialogue log.

[1069] The dialogue logs will be used for later analysis and for training artificial intelligence.

[1070] Users can review their conversation logs later.

[1071] End of session

[1072] At the end of a conversational session, the user sends an termination request to the server.

[1073] The server saves the session log and sentiment data to the database and then terminates the session.

[1074] Specific example

[1075] The following are examples of prompt statements for a generative AI model:

[1076] User: "I've been dissatisfied with the service at this store recently."

[1077] System: "Could you please tell us specifically what caused your dissatisfaction?"

[1078] This system is expected to significantly improve customer service in physical stores and increase customer satisfaction. The server receives user input via smartphones and smart glasses, and uses artificial intelligence and an emotion recognition engine to provide effective responses. In this way, store staff can respond to customers quickly and appropriately.

[1079] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1080] Step 1:

[1081] This system accepts new user registration requests. Users enter their name, ID, and password via their device and send this data to the server. The server receives this user information, hashes the password, and stores it in the user authentication database. Input: User's name, ID, and password. Output: Hashed password, and a message confirming registration to the authentication database.

[1082] Step 2:

[1083] The user enters their ID and password on the login screen using their device and sends a login request to the server. The server verifies the user's ID and hashed password against the authentication database, and if authentication is successful, generates an authentication token and provides it to the user. Input: User ID, password. Output: Authentication token.

[1084] Step 3:

[1085] An authenticated user sends a request to the server to start an interactive session from their terminal. The server generates a unique session ID, provides it to the user, and stores the session ID and user information in the session management database. Input: Interactive session start request. Output: Session ID, registration in the session management database.

[1086] Step 4:

[1087] Users input questions and comments about their worries and emotions via their device and send them to the server. The server receives the input from the user and sends it to an artificial intelligence with natural language processing capabilities (such as the Google NLP API). Input: User's questions and comments. Output: Analysis results from natural language processing.

[1088] Step 5:

[1089] The server simultaneously uses an emotion recognition engine (such as the Microsoft Azure Emotion API) to identify the user's emotional state from their input. The emotion recognition engine extracts the user's emotions, provides this information to the artificial intelligence, and generates an appropriate response. Input: User's questions and comments. Output: Emotion recognition results, optimized response.

[1090] Step 6:

[1091] The server receives the generated response and provides it to the user through the terminal. This allows the user to receive feedback about their concerns and emotions. The server also records all interactions and emotional data during the conversation session as a dialogue log, preparing for analysis as needed. Input: Optimized response. Output: Providing the response to the user, recording the dialogue log.

[1092] Step 7:

[1093] When a user requests to end a conversation session, the server saves the session log and sentiment data to the database and terminates the session. The user can review the conversation log later. Input: Session termination request. Output: Saving of session log and sentiment data, session termination message.

[1094] By following the above steps, the system will be able to accurately understand the user's emotions and respond quickly and appropriately.

[1095] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1096] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1097] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1098] [Fourth Embodiment]

[1099] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1100] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1101] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1102] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1103] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1105] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1106] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1107] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1108] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1109] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1110] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1111] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1112] This invention is a system that uses artificial intelligence with natural language processing capabilities to help users objectively re-examine their own thoughts and feelings. The system consists of several main components, each with a specific function.

[1113] 1. User registration and login

[1114] The server receives a new user registration request. When the user enters their name, ID, and password and submits them, the server receives this information, hashes the password, and stores it in the user authentication database. The user then logs in using the registered information. During login, the server authenticates the entered ID and password and, if correct, issues an authentication token. This authentication token is used by the user for subsequent operations.

[1115] For example, when a user creates a new account, the server receives and stores the user information. Then, the user enters their ID and password on the login screen and sends a login request. The server receives this and, if authentication is successful, issues a token.

[1116] 2. Start of the dialogue session

[1117] When a user initiates an interactive session, the server generates a session ID and provides it to the user. The session ID is unique and used to identify the relevant interactive session. Upon receiving a session initiation request, the server stores the session ID and user information in the session management database.

[1118] 3. Conversation progression using natural language processing

[1119] Users input questions or comments about their worries and emotions and send them to the server. The server sends the user's input to an artificial intelligence with natural language processing capabilities, which then generates appropriate responses to the questions. These responses are then provided to the user via the server. This allows users to organize their emotions and thoughts through dialogue.

[1120] For example, if a user submits a question such as "I've been feeling stressed at work lately," the server sends the question to the artificial intelligence and waits for a response. The AI ​​generates a response such as "What specifically is causing you stress?", and the server forwards this response to the user.

[1121] 4. Providing and recording feedback

[1122] The server records all interactions during a dialogue session as a dialogue log. This dialogue log is used for later analysis and AI training. Users can also review their own dialogue logs later, providing them with valuable data for self-analysis.

[1123] 5. End of session

[1124] At the end of a conversational session, the user sends an termination request to the server. The server receives the request, saves the session log to the database, and terminates the session. The saved log can be accessed later and used by the user for review.

[1125] In this way, the present invention provides an effective means for users to objectively re-examine their worries and emotions, and realizes a system that solves the problems of conventional counseling services.

[1126] The following describes the processing flow.

[1127] Step 1:

[1128] The user enters their name, ID, and password and sends a user registration request to the server.

[1129] Step 2:

[1130] The server stores the received user information in a database. Passwords are stored in hashed form.

[1131] Step 3:

[1132] The user enters their ID and password on the login screen and sends a login request to the server.

[1133] Step 4:

[1134] The server retrieves the corresponding hashed password from the database and checks if it matches the entered password. If they match, it issues an authentication token to the user.

[1135] Step 5:

[1136] The user uses an authentication token to send a request to the server to start an interactive session.

[1137] Step 6:

[1138] The server generates a new session ID and provides it to the user. The session ID, user information, and session log are stored for management purposes.

[1139] Step 7:

[1140] Users enter questions and comments within the dialogue session and send them to the server.

[1141] Step 8:

[1142] The server sends the questions received from the user to the artificial intelligence and receives a response from the AI.

[1143] Step 9:

[1144] The server displays the response from the artificial intelligence to the user.

[1145] Step 10:

[1146] Users can continue to enter further questions and comments based on the AI's responses.

[1147] Step 11:

[1148] The server saves logs of all dialogue sessions for future analysis and use in training artificial intelligence.

[1149] Step 12:

[1150] If a user wishes to end a conversation session, they send a session termination request to the server.

[1151] Step 13:

[1152] The server saves the session log to the database and terminates the interactive session. The user can then review the interactive log later.

[1153] (Example 1)

[1154] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1155] Traditional counseling services have many shortcomings. In particular, they lack appropriate means for users to objectively re-examine their own emotions and problems. Furthermore, the counseling process and feedback are inconsistent, and sufficient data for later review is not stored. As a result, there is a lack of useful information for users to conduct self-analysis, making effective counseling difficult.

[1156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1157] In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for authenticating the user using the authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to a generative AI model having natural language processing capabilities and receiving a response from the generative AI model; means for displaying the response from the generative AI model to the user; means for recording a log of the dialogue session and performing analysis as necessary; means for allowing the user to review the recorded dialogue session log later; and means for ending the dialogue session. This makes it possible to provide a means for the user to objectively re-examine their emotions and worries and to conduct effective self-analysis. Furthermore, it enables a consistent counseling process and allows for sufficient storage of data for feedback and reflection.

[1158] "User information" refers to identification information such as the user's name, ID, and password that they register in the system.

[1159] A "user authentication database" is a database used to store user information and authenticate users.

[1160] "Authentication information" refers to information such as a user ID and password that a user uses to access the system.

[1161] A "session ID" is a unique identifier generated by the server and provided to the user to identify an interactive session.

[1162] "Natural language processing capabilities" refer to the technology used to understand and process human language, and generative AI models possess this capability.

[1163] A "generative AI model" is an artificial intelligence model that generates appropriate responses to user input.

[1164] A "response" is the answer that a generative AI model generates based on the user's input.

[1165] A "dialogue session" is a series of processes in which a user interacts with a generative AI model.

[1166] A "dialogue log" is data that records all interactions during a dialogue session.

[1167] "Analysis" is the process of analyzing data using dialogue logs and extracting useful information.

[1168] "Reflection" is the process of reviewing saved conversation logs later and conducting self-analysis and re-evaluation.

[1169] This invention is a system that helps users objectively re-examine their own thoughts and feelings using artificial intelligence (generative AI model) with natural language processing capabilities. This system consists of several main components, each with a specific function.

[1170] The system's hardware includes servers, user terminals (such as personal computers and smartphones), and a database. The software includes a user authentication system, a generative AI model with natural language processing capabilities, and a database management system.

[1171] User registration and login

[1172] The user first enters their name, ID, and password and sends them to the server. The server receives this information, hashes the password, and stores it in the user authentication database. The user then enters their ID and password on the login screen and receives an authentication token. This token is used to perform subsequent operations.

[1173] Start of the dialogue session

[1174] When a user initiates an interactive session, the server generates a new session ID and provides it to the user. The server stores the session ID and user information in the session management database and then initiates the interactive session.

[1175] Conversation progression using natural language processing

[1176] Users input questions or comments about their worries or feelings and send them to the server. The server sends the user's input to a generating AI model and generates an appropriate response. The generated response is then provided to the user via the server.

[1177] Specific example:

[1178] If a user enters "I've been under a lot of stress at work lately," the server sends this message to a generating AI model. The generating AI model then generates a response asking "What specifically is causing you stress?", which the server then sends to the user.

[1179] Providing and recording feedback

[1180] The server records all interactions during a dialogue session as a dialogue log and stores it in a database. This dialogue log can be reviewed by the user later and used to train generative AI models. Users can also review their own dialogue logs later.

[1181] End of session

[1182] At the end of an interactive session, the user sends a session termination request to the server. The server receives this request, saves the session log to the database, and terminates the session.

[1183] Example of a prompt:

[1184] User: "I've been under a lot of stress at work lately."

[1185] AI: "Specifically, what aspects are causing you stress?"

[1186] In this way, the present invention is a system that provides an effective means for users to objectively re-examine their worries and emotions and to conduct self-analysis. Furthermore, this system solves the problems of conventional counseling services and supports users in maintaining better mental health.

[1187] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1188] Step 1: User Registration

[1189] 1. Input: The user enters their name, ID, and password.

[1190] 2. Specific operation: The terminal sends the entered information to the server in JSON format.

[1191] 3. Data Processing and Calculation: The server processes the received information and hashes the password using the bcrypt algorithm.

[1192] 4. Output: The hashed password and user information are stored in the user authentication database. The server sends a registration completion message to the user.

[1193] Step 2: Log in

[1194] 1. Input: The user enters their ID and password on the login screen.

[1195] 2. Specific operation: The terminal sends the input information to the server in JSON format.

[1196] 3. Data Processing and Calculation: The server receives the user ID and password and compares them against the hashed password in the database.

[1197] 4. Output: If authentication is successful, the server generates an authentication token and sends it to the user.

[1198] Step 3: Start the dialogue session

[1199] 1. Input: The user sends a request to start a conversation session.

[1200] 2. Specific action: The terminal sends a request to the server.

[1201] 3. Data processing and calculation: The server generates a new session ID and saves that ID to the session management database.

[1202] 4. Output: The server provides the user with a session ID.

[1203] Step 4: Conversation progression using natural language processing

[1204] 1. Input: The user enters a message about their worries or feelings.

[1205] 2. Specific action: The terminal sends the entered message to the server.

[1206] 3. Data Processing and Calculation: The server sends the message to the AI ​​model that generates the appropriate response.

[1207] 4. Output: The server sends the generated response to the user. For example, if the user inputs "I've been under a lot of stress at work lately," the AI ​​model will generate the response "What specifically is causing you stress?" and the server will send this to the user.

[1208] Step 5: Provide and record feedback

[1209] 1. Input: The user sends a request to review the conversation log later.

[1210] 2. Specific operation: The server records all interactions during the conversation session as a conversation log.

[1211] 3. Data Processing and Calculation: Recorded dialogue logs are saved to a database and used to train the generated AI model as needed.

[1212] 4. Output: The server will provide the user with the recorded conversation log upon request.

[1213] Step 6: Ending the session

[1214] 1. Input: The user sends a request to end the conversation session.

[1215] 2. Specific action: The terminal sends a termination request to the server.

[1216] 3. Data processing and calculation: The server saves the session log to the database.

[1217] 4. Output: The server sends a session termination notification to the user.

[1218] Through the steps outlined above, a system will be created that allows users to objectively re-examine their worries and emotions and conduct effective self-analysis.

[1219] (Application Example 1)

[1220] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1221] In modern virtual stores, customers are expected to receive appropriate advice and personalized product recommendations when making purchases. However, conventional systems do not adequately provide immediate natural language processing responses to customer inquiries or individualized recommendations based on past logs. As a result, customers lack sufficient means to have a satisfying purchasing experience, which diminishes their desire to buy.

[1222] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1223] In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for authenticating the user using the authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence; means for displaying the response from the artificial intelligence to the user; means for storing a log of the dialogue session and performing analysis as necessary; means for ending the dialogue session; means for receiving consultation from the customer regarding product selection within the virtual store and making product suggestions based on natural language processing; and means for learning the user's preferences using past dialogue logs and generating personalized suggestions. As a result, customers can receive consultation regarding appropriate product selection within the virtual store, receive personalized services, and obtain a more satisfying purchasing experience.

[1224] "User information" refers to information that a user enters for their own identification and authentication, and includes names, IDs, passwords, etc.

[1225] A "user authentication database" is a database that securely stores user authentication information and supports the authentication process.

[1226] A "session ID" is a unique identifier that is generated and provided to the user to identify a specific interaction session.

[1227] "Artificial intelligence with natural language processing capabilities" refers to an artificial intelligence system that understands human language and has the ability to generate appropriate responses based on that input.

[1228] A "dialogue session log" is data that records all interactions that took place during a dialogue session with a user, and is used for later analysis and training of artificial intelligence.

[1229] A "virtual store" is a virtual store that exists on the internet, a platform where customers can select and purchase products online.

[1230] "Personalized suggestions" is an approach that individually proposes the most suitable products and services to each user based on past conversation logs and user preferences.

[1231] The system for implementing this invention performs user information input and authentication, dialogue session management, response generation using natural language processing, and dialogue log recording and analysis. To realize these functions, the following steps are taken between the server, terminal, and user.

[1232] User information input and authentication

[1233] The user enters user information such as their name, ID, and password using a device (smartphone or head-mounted display). The server receives this information, hashes the password, and stores it in the user authentication database. During login, the server verifies the entered ID and password, and if correct, issues an authentication token (session ID). This authentication token is required for the user to perform subsequent operations.

[1234] Managing dialogue sessions

[1235] When an authenticated user initiates an interactive session, the server generates a unique session ID and provides it to the user. This session ID is used to identify a specific interactive session and is stored in the session management database. Upon receiving a session initiation request, the server manages the session ID and user information internally.

[1236] Response generation using natural language processing

[1237] Users input questions and inquiries about product selection through their terminals. The server sends this input to an artificial intelligence (AI) with natural language processing capabilities, which generates appropriate responses. The generated responses are provided to the user via the server, allowing the user to receive product information and suggestions through the dialogue. The AI ​​uses natural language processing, such as NLTK.

[1238] Specific example

[1239] For example, if a user enters "I'm looking for a summer shirt," the server sends that input to the artificial intelligence, which then generates a response such as "What kind of design do you prefer?" This response is then provided to the user via the server.

[1240] Recording and analysis of dialogue logs

[1241] All interactions during a conversation session are recorded as conversation logs. The server saves these logs for later analysis and use in training the artificial intelligence. The logs are stored in a database and can be reviewed by the user later. Furthermore, past conversation logs are used to learn the user's preferences and generate more personalized suggestions.

[1242] End of session

[1243] At the end of a conversational session, the user sends an termination request to the server. The server receives this request, saves the session log to the database, and terminates the session. The saved log can be used for later analysis and review.

[1244] Example of a prompt

[1245] For example, if the user input is "I'm looking for a summer shirt," the following prompt message is sent to the AI ​​model.

[1246] User input: I'm looking for a summer shirt.

[1247] The above describes a specific form for carrying out the invention. This system allows users to enjoy a comfortable shopping experience within a virtual store.

[1248] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1249] Step 1:

[1250] The user enters user information such as name, ID, and password using a terminal. The entered data includes name and ID in text format, while the password is hashed for security.

[1251] Step 2:

[1252] The server receives information sent by the user and hashes the password. The hashed password and other user information are stored in the user authentication database. The specific data processing used here is password hashing.

[1253] Step 3:

[1254] The user enters their ID and password on the login screen via their device. The entered ID and password are sent to the server as a login request.

[1255] Step 4:

[1256] The server verifies the entered ID and password against the user authentication database. During this process, the password is hashed again and compared to the hash value in the database. If the correct authentication information is confirmed, an authentication token (session ID) is issued. The authentication token is unique and stored in the session management database.

[1257] Step 5:

[1258] An authenticated user sends a request to start a conversation session through their terminal. The server generates a unique session ID and provides it to the user. The session ID is an identifier used to identify a specific conversation session.

[1259] Step 6:

[1260] Users input inquiries and questions regarding product selection through their terminals. The entered inquiries are sent to the server. The specific data format used here is text data.

[1261] Step 7:

[1262] The server sends user input to an artificial intelligence (AI) with natural language processing capabilities. The AI ​​analyzes the user input and generates an appropriate response. NLTK or generative AI models are used in this process. A prompt such as "User input: I'm looking for a summer shirt" is sent to the AI.

[1263] Step 8:

[1264] The response generated by the artificial intelligence is sent back to the server. The server receives this response and displays it to the user. The output data format is text data.

[1265] Step 9:

[1266] All interactions during a dialogue session are recorded as a dialogue log. The server stores the dialogue log in a database and analyzes it as needed. The dialogue log is used later for AI training and user feedback.

[1267] Step 10:

[1268] The user sends a request to end the conversation session through their terminal. The server receives the termination request, saves the session log to the database, and terminates the session. The session log is accessible later and can be used for review.

[1269] The above outlines the specific processing steps of the system. This system allows users to smoothly consult about product selection within the virtual store and receive personalized suggestions.

[1270] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1271] This invention is a system that helps users objectively re-examine their own thoughts and feelings using artificial intelligence with natural language processing capabilities and an emotion engine. The system consists of several main components, each with a specific function.

[1272] 1. User registration and login

[1273] The server receives a new user registration request. When the user enters their name, ID, and password and submits them, the server receives this information, hashes the password, and stores it in the user authentication database. The user then logs in using the registered information. During login, the server authenticates the entered ID and password and, if correct, issues an authentication token. This authentication token is used by the user for subsequent operations.

[1274] For example, when a user creates a new account, the server receives and stores the user information. Then, the user enters their ID and password on the login screen and sends a login request. The server receives this and, if authentication is successful, issues a token.

[1275] 2. Start of the dialogue session

[1276] When a user initiates an interactive session, the server generates a session ID and provides it to the user. The session ID is unique and used to identify the relevant interactive session. Upon receiving a session initiation request, the server stores the session ID and user information in the session management database.

[1277] 3. Conversation progression and emotion recognition using natural language processing.

[1278] Users input questions or comments about their worries and emotions and send them to the server. The server sends the user's input to an artificial intelligence with natural language processing capabilities and simultaneously uses an emotion engine to recognize the user's emotions. The emotion engine extracts the emotional state from the user's input and provides this emotional information to the AI ​​to optimize the response. The AI ​​generates a response that takes the emotional information into account, and this response is provided to the user via the server.

[1279] For example, if a user submits a question like, "I've been feeling a lot of stress at work lately," the server sends that question to the artificial intelligence, and the emotion engine recognizes the user's emotion as "stress." Based on that information, the AI ​​generates a response asking, "What specifically is causing you stress?" and the server forwards that response to the user.

[1280] 4. Providing feedback and recording emotional data

[1281] The server records all interactions and emotional data during a dialogue session as a dialogue log. This dialogue log is used for later analysis and AI training. Users can also review their own dialogue logs later, providing them with valuable data for self-analysis.

[1282] 5. End of session

[1283] At the end of a conversational session, the user sends an termination request to the server. The server receives the request, saves the session log and sentiment data to the database, and terminates the session. The saved log can be accessed later and used by the user for reflection.

[1284] In this way, the present invention provides an effective means for users to objectively re-examine their worries and emotions, and realizes a system that solves the problems of conventional counseling services. By introducing an emotion engine, it becomes possible to accurately grasp the user's emotional state and provide appropriate feedback based on that.

[1285] The following describes the processing flow.

[1286] Step 1:

[1287] The user enters their name, ID, and password and sends a user registration request to the server.

[1288] Step 2:

[1289] The server stores the received user information in a database. Passwords are stored in hashed form.

[1290] Step 3:

[1291] The user enters their ID and password on the login screen and sends a login request to the server.

[1292] Step 4:

[1293] The server retrieves the corresponding hashed password from the database and checks if it matches the entered password. If they match, it issues an authentication token to the user.

[1294] Step 5:

[1295] The user uses an authentication token to send a request to the server to start an interactive session.

[1296] Step 6:

[1297] The server generates a new session ID and provides it to the user. The session ID, user information, and session log are stored for management purposes.

[1298] Step 7:

[1299] Users enter questions and comments within the dialogue session and send them to the server.

[1300] Step 8:

[1301] The server sends the questions received from the user to the emotion engine, which then analyzes the user's emotional state.

[1302] Step 9:

[1303] The emotion engine recognizes the user's emotions from the text of the question and generates emotion data.

[1304] Step 10:

[1305] The server sends the question along with sentiment data to the artificial intelligence, which then generates an appropriate response.

[1306] Step 11:

[1307] The artificial intelligence considers emotional data to generate the optimal response for the user and sends it to the server.

[1308] Step 12:

[1309] The server displays the response from the artificial intelligence to the user.

[1310] Step 13:

[1311] Users can continue to enter further questions and comments based on the AI's responses.

[1312] Step 14:

[1313] The server stores logs and sentiment data from all dialogue sessions, which will be used for future analysis and training of artificial intelligence.

[1314] Step 15:

[1315] If a user wishes to end a conversation session, they send a session termination request to the server.

[1316] Step 16:

[1317] The server saves the session log and sentiment data to the database and ends the conversation session. The user can then review the conversation log and sentiment data later.

[1318] (Example 2)

[1319] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1320] Traditional systems struggled to accurately understand users' emotions and psychological states and provide appropriate feedback based on that understanding. Furthermore, they lacked sufficient management and analysis of dialogue logs, failing to offer effective means for user self-analysis and improvement. As a result, conventional counseling services and support systems were subject to numerous limitations.

[1321] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1322] In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for performing authentication using the user authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence; means for recognizing the user's emotions using an emotion engine and providing the emotion information to the artificial intelligence to optimize the response; means for displaying the response from the artificial intelligence to the user; means for saving a log of the dialogue session and performing analysis as necessary; and means for ending the dialogue session. As a result, the user can simply input their emotions and concerns in natural language, and the artificial intelligence and emotion engine will work together to provide optimal feedback, and the dialogue log can be reviewed and analyzed later.

[1323] "User information" refers to information such as the name, ID, and password that a user needs to register with the system.

[1324] A "user authentication database" is a database used to store and verify user authentication information.

[1325] "User authentication information" refers to information such as the ID and password that a user uses to access the system.

[1326] A "dialogue session" refers to a series of interactions in which a user and artificial intelligence engage in dialogue.

[1327] A "session ID" is an identifier used to uniquely identify a specific dialogue session.

[1328] "Natural language processing functionality" refers to the technology used to analyze and understand natural language input from users.

[1329] "Artificial intelligence" is a computer system that possesses the knowledge to generate appropriate responses to user input.

[1330] An "emotion engine" is a technology that extracts and recognizes emotional states from user input.

[1331] A "dialogue log" is a record of the interactions and emotional data during a dialogue session.

[1332] "Analysis" refers to the act of analyzing the user's emotional state and the content of conversations based on saved dialogue logs.

[1333] "Feedback" refers to information that a system provides based on the user's input and emotional state.

[1334] This invention is a system that helps users objectively re-examine their own thoughts and feelings using artificial intelligence with natural language processing capabilities and an emotion engine. The system consists of several main components, each with a specific function.

[1335] User registration and login

[1336] The server receives a new user registration request. The user enters their name, ID, and password into the terminal and submits them. The server hashes the received password using a hashing algorithm (e.g., SHA-256) and stores the user information in the user authentication database. The user then logs in using the registered information. During login, the server compares the entered ID and password with the information in the database, and if authentication is successful, generates an authentication token and sends it to the user. This authentication token is used by the user when performing subsequent operations.

[1337] For example, a user might type "I want to create a new account" and click the submit button. The server receives this request, saves the user's information, and returns a message to the user saying "Your account has been created."

[1338] Start of the dialogue session

[1339] When a user initiates an interactive session, the server generates a unique session ID and provides it to the user. Upon receiving the session initiation request, the server stores the session ID and user information in the session management database.

[1340] For example, if a user types and submits "I want to start a conversation session," the server generates a new session ID and displays "Session started" on the user's screen.

[1341] Conversation progression and emotion recognition using natural language processing

[1342] The user enters comments about their worries and emotions and sends them to the server. The server sends these comments to a generative AI model with natural language processing capabilities (e.g., OpenAI's GPT-3). Simultaneously, the server uses an emotion engine (e.g., Affectiva) to recognize the user's emotions and extract emotion information. The server provides this emotion information to the generative AI model and requests it to generate the most appropriate response. The generative AI model generates a response that takes the emotion information into account, and the server sends that response to the user.

[1343] For example, a user might type and submit "I've been feeling a lot of stress at work lately." The server sends this input to a generative AI model and an emotion engine. The generative AI model, based on the emotion information "stress," generates a response asking "What specifically makes you feel stressed?" and sends it to the user.

[1344] Providing feedback and recording emotional data

[1345] The server records all interactions and emotional data during a dialogue session as a dialogue log. This dialogue log is used for later analysis and training of generative AI models. Users can review their own dialogue logs later and use them as data for self-analysis.

[1346] End of session

[1347] When a conversation session ends, the user sends an end request to the server. The server receives the request, saves the session log and sentiment data to the database, and terminates the session. The saved log is accessible to the user later and can be used for reflection.

[1348] For example, if a user types and submits "Please end the conversation session," the server saves the session log and returns the message "Session ended."

[1349] In summary, the present invention provides an effective means for users to objectively re-examine their own worries and emotions, and realizes a system that solves the problems of conventional counseling services.

[1350] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1351] Step 1: Receiving a user registration request

[1352] The server receives a new registration request from a user. The user enters their name, ID, and password on their terminal and sends them to the server. Upon receiving the input, the server hashes the password using a hashing algorithm (e.g., SHA-256). The hashed password and user information are then generated as output.

[1353] Specific example of operation: The user types "I want to create a new account" on their device and clicks the submit button.

[1354] Step 2: Saving User Information

[1355] The server stores the hashed password and other user information in the user authentication database. The input is the hashed password and user information generated in step 1, and the output is the success status.

[1356] Specific example of operation: User information is saved to the database, and the user receives a message saying "Account created."

[1357] Step 3: Receiving the login request

[1358] After registration, the user enters their ID and password on the login screen and sends a login request to the server. The input is the ID and password, which the server receives. The output is the generation of an authentication token.

[1359] Specific example of operation: The user enters their ID and password on their device and clicks the "Login" button.

[1360] Step 4: User Authentication

[1361] The server verifies the entered ID and password against the information in the user authentication database. The input is the ID and password provided by the user, and the process involves comparing the hashed password with the information in the database. The output is the generation of an authentication token.

[1362] Specific example of operation: If authentication is successful, "Login successful" will be displayed.

[1363] Step 5: Receiving a request for a dialogue session

[1364] The user sends a request to start a conversational session. Input is a click action or a prompt message such as "I want to start a conversational session." Output is the generation of a unique session ID.

[1365] Specific example of operation: The user types and submits "I want to start a conversation session."

[1366] Step 6: Generate a session ID

[1367] The server generates a unique session ID and provides it to the user. The input is a request for an interactive session, and the output is the session ID.

[1368] Specific example of operation: The server generates a new session ID, and the user's screen displays "Session started".

[1369] Step 7: User submits comment

[1370] Users enter comments about their worries and feelings and send them to the server. The input is the user's comment, and the output is the comment sent to the server.

[1371] Specific example of operation: The user types "I've been feeling a lot of stress at work lately" into the device and sends it.

[1372] Step 8: NLP Processing

[1373] The server sends user input to a generative AI model with natural language processing capabilities. The input is the user's comment, which the generative AI model analyzes. The output consists of the analysis result and the generated response.

[1374] Specific example of operation: The server sends the comment "I've been under a lot of stress at work lately" to the AI ​​model that generates the comment.

[1375] Step 9: Emotion Recognition

[1376] The server uses an emotion engine to recognize the user's emotions and extract emotion information. The input is the user's comment, and the emotion engine analyzes the emotion information. The output is the extracted emotion information.

[1377] Specific example of operation: The emotion engine recognizes the emotion "stress" from the user's comment.

[1378] Step 10: AI-powered response generation

[1379] The server provides sentiment information to an AI model and requests it to generate the optimal response. The input is sentiment information and the original comment, and the output is the optimized response.

[1380] Specific example of operation: The generating AI model considers emotional information such as "stress" and generates a response asking, "What specifically makes you feel stressed?"

[1381] Step 11: Sending a response to the user

[1382] The server sends a response from the AI ​​model to the user. The input is the response from the generating AI model, and the output is what is displayed to the user.

[1383] Specific example of operation: The response "What specifically is causing you stress?" will be displayed on the user's device.

[1384] Step 12: Recording the conversation log

[1385] The server records all interactions and sentiment data during a dialogue session as a dialogue log. The input is the interactions and sentiment data of the dialogue session, and the output is the dialogue log.

[1386] Specific example of operation: The server saves the interaction as a log in real time.

[1387] Step 13: Analysis and use of dialogue logs

[1388] The server analyzes the dialogue logs and uses the results to train the generative AI model. The input is the dialogue logs, and the output is the analysis results and updates to the generative AI model.

[1389] Specific example of operation: The server analyzes the saved dialogue logs and uses them to improve the performance of the generated AI model.

[1390] Step 14: Received a request to end the dialogue session.

[1391] The user requests the end of the interaction session. The input is the termination request, and the output is the start of the session termination process.

[1392] Specific example of operation: The user types and submits "Please end the conversation session."

[1393] Step 15: Saving session data

[1394] The server saves the session log and sentiment data to the database and then terminates the session. The inputs are the session log and sentiment data, and the outputs are "Saving complete" and "Session terminated."

[1395] Specific example of operation: The server returns the message "Session ended" to the user.

[1396] (Application Example 2)

[1397] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1398] Traditional in-store customer service often struggled to immediately understand and appropriately respond to customer emotions and opinions, potentially leading to decreased customer satisfaction. Furthermore, store staff lacked effective means to quickly grasp specific customer concerns and dissatisfactions. Therefore, there was a need for a system that could accurately understand customer emotions and provide appropriate responses in real time.

[1399] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user information input from a user and storing it in a user authentication database; means for authenticating the user using the authentication information; means for generating a session ID for starting a dialogue session for the authenticated user and providing the session ID to the user; means for transmitting the user's input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence; means for displaying the response from the artificial intelligence to the user; means for identifying the user's emotions using an emotion recognition engine, recording the identification result, and optimizing the corresponding response; means for saving a log of the dialogue session and performing analysis as necessary; and means for ending the dialogue session. This makes it possible to accurately grasp the emotions of customers in physical stores and respond quickly and appropriately.

[1400] "User information" refers to information necessary to identify and authenticate a user, such as the user's name, ID, and password.

[1401] A "user authentication database" is a database used to securely store user information and utilize it in the authentication process.

[1402] A "session ID" is an identifier generated to uniquely identify an interactive session.

[1403] "Natural language processing" refers to the technology used to analyze and understand user input in natural language.

[1404] "Artificial intelligence" is a system that possesses natural language processing capabilities and generates appropriate responses based on user input.

[1405] A "response" is the answer or message that artificial intelligence generates in response to user input.

[1406] An "emotion recognition engine" is a system that identifies emotions from user input and provides information about those emotions.

[1407] A "dialogue session log" is a record of the interactions that took place between the user and the system.

[1408] "Analysis" is the process of using dialogue session logs and identified sentiment data to find specific patterns and insights.

[1409] "System" refers to the entire computer system, including the means and functions described above.

[1410] This invention is a system that enables accurate understanding of customer emotions in physical stores and facilitates quick and appropriate responses. A specific embodiment of this system is described below.

[1411] User registration and login

[1412] The server receives a new registration request from a user and retrieves the name, ID, and password entered by the user.

[1413] The server hashes the password and stores the result in the user authentication database.

[1414] Once registration is complete, users enter their ID and password on the login screen and submit a login request.

[1415] The server authenticates the entered ID and password, and if successful, generates an authentication token and provides it to the user.

[1416] Start of the dialogue session

[1417] When a user initiates a conversational session, the server generates a unique session ID and provides it to the user.

[1418] Session IDs and user information are stored in the session management database.

[1419] Conversation progression and emotion recognition using natural language processing

[1420] Users input questions and comments about their worries and feelings via their smartphones or smart glasses and send them to the server.

[1421] The server sends this input to an artificial intelligence with natural language processing capabilities (such as the Google NLP API).

[1422] Simultaneously, the server uses an emotion recognition engine (such as the Microsoft Azure Emotion API) to identify the user's emotions.

[1423] The emotion recognition engine extracts the emotional state, provides that information to the artificial intelligence, and generates the optimal response.

[1424] The generated response is provided to the user via the server.

[1425] Providing feedback and recording emotional data

[1426] The server records all interactions and emotional data during a dialogue session as a dialogue log.

[1427] The dialogue logs will be used for later analysis and for training artificial intelligence.

[1428] Users can review their conversation logs later.

[1429] End of session

[1430] At the end of a conversational session, the user sends an termination request to the server.

[1431] The server saves the session log and sentiment data to the database and then terminates the session.

[1432] Specific example

[1433] The following are examples of prompt statements for a generative AI model:

[1434] User: "I've been dissatisfied with the service at this store recently."

[1435] System: "Could you please tell us specifically what caused your dissatisfaction?"

[1436] This system is expected to significantly improve customer service in physical stores and increase customer satisfaction. The server receives user input via smartphones and smart glasses, and uses artificial intelligence and an emotion recognition engine to provide effective responses. In this way, store staff can respond to customers quickly and appropriately.

[1437] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1438] Step 1:

[1439] This system accepts new user registration requests. Users enter their name, ID, and password via their device and send this data to the server. The server receives this user information, hashes the password, and stores it in the user authentication database. Input: User's name, ID, and password. Output: Hashed password, and a message confirming registration to the authentication database.

[1440] Step 2:

[1441] The user enters their ID and password on the login screen using their device and sends a login request to the server. The server verifies the user's ID and hashed password against the authentication database, and if authentication is successful, generates an authentication token and provides it to the user. Input: User ID, password. Output: Authentication token.

[1442] Step 3:

[1443] An authenticated user sends a request to the server to start an interactive session from their terminal. The server generates a unique session ID, provides it to the user, and stores the session ID and user information in the session management database. Input: Interactive session start request. Output: Session ID, registration in the session management database.

[1444] Step 4:

[1445] Users input questions and comments about their worries and emotions via their device and send them to the server. The server receives the input from the user and sends it to an artificial intelligence with natural language processing capabilities (such as the Google NLP API). Input: User's questions and comments. Output: Analysis results from natural language processing.

[1446] Step 5:

[1447] The server simultaneously uses an emotion recognition engine (such as the Microsoft Azure Emotion API) to identify the user's emotional state from their input. The emotion recognition engine extracts the user's emotions, provides this information to the artificial intelligence, and generates an appropriate response. Input: User's questions and comments. Output: Emotion recognition results, optimized response.

[1448] Step 6:

[1449] The server receives the generated response and provides it to the user through the terminal. This allows the user to receive feedback about their concerns and emotions. The server also records all interactions and emotional data during the conversation session as a dialogue log, preparing for analysis as needed. Input: Optimized response. Output: Providing the response to the user, recording the dialogue log.

[1450] Step 7:

[1451] When a user requests to end a conversation session, the server saves the session log and sentiment data to the database and terminates the session. The user can review the conversation log later. Input: Session termination request. Output: Saving of session log and sentiment data, session termination message.

[1452] By following the above steps, the system will be able to accurately understand the user's emotions and respond quickly and appropriately.

[1453] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1454] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1455] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1456] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1457] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1458] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1459] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1460] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1461] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1462] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1463] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1464] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1465] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1466] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1467] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1468] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1469] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1470] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1471] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1472] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1473] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1474] The following is further disclosed regarding the embodiments described above.

[1475] (Claim 1)

[1476] A means of receiving user information input from a user and storing it in a user authentication database,

[1477] A means of authenticating a user using authentication information,

[1478] A means for generating a session ID to initiate a conversational session for an authenticated user and providing the user with the session ID,

[1479] A means for transmitting user input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence,

[1480] A means for displaying the response from the artificial intelligence to the user,

[1481] A means to save the logs of the dialogue session and analyze them as needed,

[1482] Means of ending a dialogue session,

[1483] A system that includes this.

[1484] (Claim 2)

[1485] The system according to claim 1, wherein logs of dialogue sessions are stored in a database and used for later analysis and training of artificial intelligence.

[1486] (Claim 3)

[1487] The system according to claim 1, wherein when an authenticated user ends an interactive session, the session log is saved to a database, and the user can later review the interactive log.

[1488] "Example 1"

[1489] (Claim 1)

[1490] A means of receiving user information input from a user and storing it in a user authentication database,

[1491] A means of authenticating a user using authentication information,

[1492] A means for generating a session ID to initiate a conversational session for an authenticated user and providing the user with the session ID,

[1493] A means for sending user input to a generative AI model having natural language processing capabilities and receiving a response from the generative AI model,

[1494] A means for displaying the response from the generated AI model to the user,

[1495] A means of recording the logs of dialogue sessions and performing analysis as needed,

[1496] A means to allow users to later review the logs of recorded conversation sessions,

[1497] Means of ending a dialogue session,

[1498] A system that includes this.

[1499] (Claim 2)

[1500] The system according to claim 1, wherein the logs of the dialogue session are stored in a database and used for later analysis and training of a generative AI model.

[1501] (Claim 3)

[1502] The system according to claim 1, wherein when an authenticated user ends an interactive session, the session log is saved to a database, and the user can later review the interactive log.

[1503] "Application Example 1"

[1504] (Claim 1)

[1505] A means of receiving user information input from a user and storing it in a user authentication database,

[1506] A means of authenticating a user using authentication information,

[1507] A means for generating a session ID to initiate a conversational session for an authenticated user and providing the user with the session ID,

[1508] A means for transmitting user input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence,

[1509] A means for displaying the response from the artificial intelligence to the user,

[1510] A means to save the logs of the dialogue session and analyze them as needed,

[1511] Means of ending a dialogue session,

[1512] A method for receiving customer consultations regarding product selection within a virtual store and providing product suggestions based on natural language processing,

[1513] A means of learning user preferences using past conversation logs and generating personalized suggestions,

[1514] A system that includes this.

[1515] (Claim 2)

[1516] The system according to claim 1, wherein logs of dialogue sessions are stored in a database and used for later analysis and training of artificial intelligence.

[1517] (Claim 3)

[1518] The system according to claim 1, wherein when an authenticated user ends an interactive session, the session log is saved to a database, and the user can later review the interactive log.

[1519] "Example 2 of combining an emotion engine"

[1520] (Claim 1)

[1521] A means of receiving user information input from a user and storing it in a user authentication database,

[1522] A means of performing authentication using user authentication information,

[1523] A means for generating a session ID to initiate a conversational session for an authenticated user and providing that session ID to the user,

[1524] A means for transmitting user input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence,

[1525] A means for recognizing a user's emotions using an emotion engine, providing the emotion information to artificial intelligence, and optimizing the response,

[1526] A means for displaying the response from the artificial intelligence to the user,

[1527] A means to save the logs of the dialogue session and analyze them as needed,

[1528] Means of ending a dialogue session,

[1529] A system that includes this.

[1530] (Claim 2)

[1531] The system according to claim 1, wherein logs of dialogue sessions are stored in a database and used for later analysis and training of artificial intelligence.

[1532] (Claim 3)

[1533] The system according to claim 1, wherein when an authenticated user ends an interactive session, the session log is saved to a database, and the user can later review the interactive log.

[1534] "Application example 2 when combining with an emotional engine"

[1535] (Claim 1)

[1536] A means of receiving user information input from a user and storing it in a user authentication database,

[1537] A means of authenticating a user using authentication information,

[1538] A means for generating a session ID to initiate a conversational session for an authenticated user and providing the user with the session ID,

[1539] A means for transmitting user input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence,

[1540] A means for displaying the response from the artificial intelligence to the user,

[1541] A means for identifying a user's emotions using an emotion recognition engine, recording the identification results, and optimizing the corresponding response,

[1542] A means to save the logs of the dialogue session and analyze them as needed,

[1543] Means of ending a dialogue session,

[1544] A system that includes this.

[1545] (Claim 2)

[1546] The system according to claim 1, wherein logs of dialogue sessions are stored in a database and used for later analysis and training of artificial intelligence.

[1547] (Claim 3)

[1548] The system according to claim 1, wherein when an authenticated user ends an interactive session, the session log is saved to a database, and the user can later review the interactive log. [Explanation of Symbols]

[1549] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving user information input from a user and storing it in a user authentication database, A means of authenticating a user using authentication information, A means for generating a session ID to initiate a conversational session for an authenticated user and providing the user with the session ID, A means for transmitting user input to an artificial intelligence having natural language processing capabilities and receiving a response from the artificial intelligence, A means for displaying the response from the artificial intelligence to the user, A means to save the logs of the dialogue session and analyze them as needed, Means of ending a dialogue session, A system that includes this.

2. The system according to claim 1, wherein the logs of the dialogue session are stored in a database and used for later analysis and training of artificial intelligence.

3. The system according to claim 1, wherein when an authenticated user ends an interactive session, the session log is saved to a database, and the user can later review the interactive log.

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