System

Abstract

A system is provided.SOLUTION: A system comprising: means for analyzing a question from a user by a generative AI model and generating an answer; means for providing the generated answer to the user; means for collecting feedback of the user and storing the feedback as training data of the generative AI model; means for performing goal setting and progress management of the user; and means for sending reminders or alerts to the user according to progress status.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Technological innovation is creating an increasing need for workers to acquire new skills and knowledge. However, problems exist, such as a lack of access to experts and the inability to consult easily. Lack of communication in particular exacerbates these issues. Time, psychological, and positional hurdles, as well as emotional issues (such as consideration and misinterpretation), are also factors that hinder worker growth. Given this background, there is a demand for an environment in which workers can focus on what they truly want to do. [Means for solving the problem]

[0005] The present invention provides a means for analyzing user questions using a generative AI model and generating answers. It also provides a system that includes a means for providing the generated answers to the user, a means for collecting user feedback and saving it as learning data for the generative AI model, a means for setting user goals and managing progress, and a means for sending reminders and alerts to the user based on their progress. Furthermore, these problems are solved by adding a means for authenticating users and a database for verifying authentication information, and a means for generating answers that take into account the user's past usage history and characteristics.

[0006] A "generative AI model" is a type of artificial intelligence that uses natural language processing technology to analyze user input and generate appropriate answers or information.

[0007] "Users" refer to workers and individuals who use the system to acquire new skills and knowledge.

[0008] A "question" is information or a question that a user inputs into the system.

[0009] An "answer" is the information or answer that a generative AI model generates in response to a user's question.

[0010] "Feedback" refers to the impressions and opinions that users give in response to the answers they receive.

[0011] "Goal setting" is the act of setting specific goals or objectives that a user wants to achieve.

[0012] "Progress management" is the process of tracking and managing progress toward goals set by a user.

[0013] A "reminder" is a message or alert sent to notify the user of goal progress or important matters.

[0014] "Authentication" is the process of verifying a user's identity when accessing a system.

[0015] A "database" is a system that stores and manages data such as user authentication information, usage history, and feedback.

[0016] "Usage history" is a record of how a user has used the system.

[0017] "Characteristics" are attributes based on a user's individual characteristics and past usage data. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11]FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention is a system that utilizes a generative AI model to provide an environment where users can easily ask for advice on anything, anytime. This system begins with user authentication and includes processes such as interactive assistance, providing responses, collecting feedback, goal setting, and progress management. A specific embodiment of this system is described below.

[0040] System Overview

[0041] The system includes the following elements:

[0042] 1. User device: The device accessed and operated by the user (e.g., smartphone, tablet, PC, etc.).

[0043] 2. Server: The central system that runs the generative AI model, analyzes user questions, and provides answers.

[0044] 3. Database: Stores user authentication information, usage history, feedback, and goal setting information.

[0045] Specific functions of the system

[0046] Authentication Process

[0047] Terminal: When a user accesses the system, a login screen is displayed. The user enters their user ID and password and presses the login button.

[0048] Server: Receives the authentication information sent from the device and compares it with the information in the database. If authentication is successful, it starts a user session and sends an instruction to the device to display the next screen.

[0049] Questions and Answers

[0050] Terminal: The user accesses the dialogue interface and enters a question. When the user has finished entering the question, he or she clicks the submit button.

[0051] Server: Receives the user's question and sends it to the generative AI model, which analyzes the question and generates an appropriate answer.

[0052] Server: Sends the generated answer to the user's device.

[0053] Terminal: Display the answer to the user.

[0054] Feedback collection

[0055] Terminal: A feedback interface is displayed where users can enter their thoughts on the answers or ask follow-up questions.

[0056] Server: Receives user-entered feedback and stores it as training data for the generative AI model.

[0057] Goal setting and progress management

[0058] Terminal: Displays the goal setting screen and provides an interface for the user to input the goals they wish to set.

[0059] Server: Stores the entered goals in a database and sets reminders and alerts.

[0060] Terminal: User periodically enters and updates progress.

[0061] Server: Checks progress and sends reminders and alerts accordingly.

[0062] Specific examples

[0063] Authentication Examples

[0064] Device: The user launches the Buddy AI app and the login screen appears.

[0065] User: Enter the user ID "user@example.com" and password "password123" and click the Login button.

[0066] Server: Checks the entered authentication information against the database, and displays the main interface if authentication is successful.

[0067] Specific examples of questions and answers

[0068] Terminal: The user types in "Please tell me about risk management for a new project" and submits.

[0069] Server: Sends questions to the generative AI model and generates answers for risk management.

[0070] Terminal: The answer "The basic outline of risk management is as follows..." is displayed.

[0071] Examples of feedback collection

[0072] Device: User types "This answer was very helpful" and submits.

[0073] Server: Saves the feedback as training data.

[0074] Specific examples of goal setting and progress management

[0075] Terminal: The user types in "Complete the project risk analysis within one month" and submits.

[0076] Server: Save your goals and set weekly progress reminders.

[0077] Users: Enter and update their progress regularly.

[0078] Server: Checks progress and sends necessary reminders and alerts.

[0079] As described above, this system provides an environment in which users can easily acquire new skills and knowledge by implementing the processes of authentication, question and answer, feedback collection, goal setting, and progress management.

[0080] The processing flow will be explained below.

[0081] Step 1: "Enter your credentials"

[0082] Terminal: Displays the login screen and provides an interface for the user to enter their user ID and password.

[0083] User: Enter your user ID and password and click the Login button.

[0084] Step 2: "Submit authentication information"

[0085] Terminal: Sends the authentication information (user ID and password) entered by the user to the server.

[0086] Step 3: "Verify Authentication"

[0087] Server: Compares the received authentication information with the information in the database and generates an authentication result.

[0088] Server: If authentication is successful, it starts a user session and sends instructions to the terminal to display the next screen. If authentication fails, it returns an error message.

[0089] Step 4: View authentication results

[0090] Terminal: If authentication is successful, display the main interface. If authentication fails, display an error message to the user.

[0091] Step 5: "Enter your question"

[0092] Terminal: displays a dialogue interface for users to enter questions.

[0093] User: Type in the question they need help with and click the submit button.

[0094] Step 6: Submit your question

[0095] Terminal: Sends the questions entered by the user to the server.

[0096] Step 7: Parse the Question

[0097] Server: Sends the received question to the generative AI model and analyzes the question.

[0098] Step 8: Generate answers

[0099] Server: The generative AI model analyzes the intent of the question and generates an appropriate answer, taking into account the user's past usage history and characteristics as necessary.

[0100] Server: Sends the generated answer to the device.

[0101] Step 9: "View Answers"

[0102] Terminal: Displays the answer received from the server to the user.

[0103] Step 10: "Enter your feedback"

[0104] Terminal: Displays an interface for users to enter feedback on their answers.

[0105] User: Enter their thoughts on the answer or any follow-up questions and click the submit button.

[0106] Step 11: "Submit Feedback"

[0107] Terminal: Sends the feedback entered by the user to the server.

[0108] Step 12: Analyze feedback

[0109] Server: Analyzes the received feedback and stores it as training data for the generative AI model.

[0110] Step 13: Enter your goal settings

[0111] Terminal: Displays an interface for users to set goals.

[0112] User: Enter the goal they want to achieve and click the submit button.

[0113] Step 14: "Save Goal"

[0114] Terminal: Sends the target information entered by the user to the server.

[0115] Server: Stores goal information in a database and sets reminders and alerts for goal achievement.

[0116] Step 15: Enter your progress

[0117] Terminal: Displays an interface for the user to enter progress.

[0118] Users: Enter progress and update regularly.

[0119] Step 16: "Progress Tracking and Notifications"

[0120] Server: Checks the progress in the database, generates reminders and alerts accordingly, and notifies the user.

[0121] On the device: Displays reminders and alerts received from the server to the user.

[0122] Through these steps, the system helps users acquire new skills and knowledge.

[0123] Example 1

[0124] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0125] In today's information environment, it is important for users to be able to quickly respond to a variety of questions and instantly obtain the information they need. However, conventional systems were unable to provide appropriate answers to users' questions and had difficulty effectively collecting and utilizing user feedback. Furthermore, they lacked functionality for user goal setting and progress management, making it difficult for users to effectively manage their own learning and work progress.

[0126] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0127] In this invention, the server includes means for analyzing questions from users and using a generative AI model to generate answers, means for providing the generated answers to the users, means for collecting user feedback and saving it as learning data for the generative AI model, means for setting user goals and managing progress, means for sending reminders and alerts to the user according to progress, means for verifying authentication information and starting a user session if authentication is successful, and means for sending the authentication result to the user terminal. This allows users to receive prompt and appropriate answers to their questions and further enables them to improve their learning and work efficiency through feedback and progress management.

[0128] A "generative AI model" is an artificial intelligence technology that analyzes questions from users and generates appropriate answers.

[0129] A "means" is a specific method or device for achieving a specific function or action.

[0130] "User" refers to a person who uses the system to enter questions or set goals.

[0131] "Feedback" refers to responses provided by users, such as their impressions of the answers and services, or any follow-up questions.

[0132] "Training data" is data that a generative AI model uses to improve and refine its accuracy.

[0133] "Goal setting" is the process by which a user inputs the goals they want to achieve and manages their progress.

[0134] "Progress management" refers to checking and managing the degree of achievement and progress toward set goals.

[0135] "Reminders" is a feature that sends notifications at specific times or situations to help users remember their set goals and tasks.

[0136] An "alert" is a notification sent to alert or warn the user.

[0137] "Authentication information" refers to information such as user ID and password that a user enters when accessing a system.

[0138] A "user session" refers to the series of operations a user performs from the time they log in to the time they log out of the system.

[0139] A "database" is a system for structuring, storing, and managing data such as authentication information and usage history.

[0140] The present invention is a system that provides an environment where users can easily ask any question at any time. This system utilizes a generative AI model and encompasses processes such as user authentication, interactive assistance, response provision, feedback collection, goal setting and progress management. A specific embodiment of this system is described below.

[0141] System configuration

[0142] The system includes the following elements:

[0143] 1. User device: The device accessed and operated by the user (e.g., smartphone, tablet, PC, etc.).

[0144] 2. Server: The central system that runs the generative AI model, analyzes user questions, and provides answers.

[0145] 3. Database: A system that stores user authentication information, usage history, feedback, and goal setting information.

[0146] Hardware and Software

[0147] User device: Provides the front-end interface that users access. For example, it can be a smartphone application running on iOS or Android, or a web application running on a web browser.

[0148] Server: Cloud-based computing resources for running generative AI models (e.g., GPT-4), such as virtual machines or container services provided by Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure.

[0149] Database: A database system for storing user information and usage history. Examples include Amazon RDS, Google Cloud SQL, and Microsoft SQL Server.

[0150] Specific steps in the process and data handling

[0151] Authentication Process

[0152] When a user accesses the system from a user terminal, a login screen is displayed. The user enters their user ID and password and presses the login button. The server receives the authentication information sent from the terminal and compares it with the information in the database. If authentication is successful, it starts a user session and sends an instruction to the terminal to display the next screen.

[0153] Handling Questions and Answers

[0154] The user accesses the dialogue interface and inputs a question. For example, the user inputs and submits "Please tell me about risk management for a new project." The server receives the user's question and sends it to the generative AI model. The generative AI model analyzes the question and generates an appropriate answer. The server sends the generated answer to the user's device, and the device displays the answer to the user.

[0155] Feedback collection

[0156] A feedback interface is displayed in which the user can enter their thoughts about the answer or any additional questions. For example, they can enter "This answer was very helpful" and submit. The server receives the user's feedback and stores it as training data for the generative AI model.

[0157] Goal setting and progress management

[0158] It displays a goal setting screen and provides an interface for users to input the goals they want to set. For example, a user can input "Complete project risk analysis within one month" and submit it. The server saves the input goals in a database and sets reminders and alerts. Users can periodically input and update their progress, and the server will check the progress and send reminders and alerts as appropriate.

[0159] This allows users to efficiently acquire their own skills and knowledge. The system provides interactive support to users, helping them manage their learning and work progress through feedback and goal setting, thereby enabling users to effectively manage themselves.

[0160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0161] Authentication Process

[0162] Step 1:

[0163] A user accesses the system using a user terminal, which displays the login screen.

[0164] Input: Enter your user ID and password.

[0165] Output: The input information is displayed on the login screen.

[0166] Step 2:

[0167] The user presses the login button.

[0168] Input: User ID and password.

[0169] Output: The input information is sent to the server.

[0170] Step 3:

[0171] The server receives the authentication information sent from the terminal and compares it with the authentication information stored in the database.

[0172] Input: User ID and password.

[0173] Data processing: Retrieve user information from the database and compare it with the received authentication information.

[0174] Output: Authentication result (success or failure).

[0175] Step 4:

[0176] The server sends the authentication result to the terminal.

[0177] Input: Authentication result.

[0178] Output: The authentication result is sent to the terminal.

[0179] Step 5:

[0180] The device will display the following screen based on the authentication result: the main interface if successful, or an error message if unsuccessful.

[0181] Input: Authentication result.

[0182] Output: The main interface or an error message is displayed.

[0183] Question and Answer Process

[0184] Step 1:

[0185] The user accesses the dialogue interface and enters a question.

[0186] Input: Question (e.g. "Please tell me about risk management for a new project").

[0187] Output: The entered question is displayed on the screen.

[0188] Step 2:

[0189] The user presses the send button.

[0190] Input: Question.

[0191] Output: The question is sent to the server.

[0192] Step 3:

[0193] The server receives the user's query.

[0194] Input: Question.

[0195] Output: The question is processed as a prompt that is sent to the generative AI model.

[0196] Step 4:

[0197] A generative AI model analyzes the question and generates an appropriate answer.

[0198] Input: The question as a prompt.

[0199] Data computation: Analyzing questions and generating answers based on relevant information.

[0200] Output: The generated answer.

[0201] Step 5:

[0202] The server receives the generated response and transmits it to the user terminal.

[0203] Input: The generated answer.

[0204] Output: The answer is sent to the user's device.

[0205] Step 6:

[0206] The device displays the answer to the user.

[0207] Input:Answer.

[0208] Output: The answer is displayed on the screen.

[0209] Feedback Collection Process

[0210] Step 1:

[0211] Users can enter their thoughts and follow-up questions into the feedback interface.

[0212] Input: Feedback (e.g., "This answer was very helpful").

[0213] Output: Feedback is displayed on the screen.

[0214] Step 2:

[0215] The user presses the send button.

[0216] Input: Feedback content.

[0217] Output: Feedback is sent to the server.

[0218] Step 3:

[0219] The server receives the feedback and stores it as training data for the generative AI model.

[0220] Input: Feedback content.

[0221] Data processing: The feedback content is organized and saved in a database as learning data.

[0222] Output: The feedback is stored in a database.

[0223] Goal setting and progress management process

[0224] Step 1:

[0225] The user accesses the goal setting screen and enters the goal.

[0226] Input: Objective (e.g., "Complete project risk analysis within one month").

[0227] Output: The entered goal is displayed on the screen.

[0228] Step 2:

[0229] The user presses the send button.

[0230] Input: Goal content.

[0231] Output: The goal is sent to the server.

[0232] Step 3:

[0233] The server receives the target content and stores it in a database.

[0234] Input: Goal content.

[0235] Data processing: Organize the target content and save it in a database.

[0236] Output: The goal is saved in the database.

[0237] Step 4:

[0238] The server sets reminders and alerts and sends them to the user accordingly.

[0239] Inputs: Progress data, alert conditions.

[0240] Output: Reminders and alerts are set and sent to the user's device.

[0241] Step 5:

[0242] Users periodically enter and update their progress.

[0243] Input: Progress data (e.g., "Risk analysis 50% complete").

[0244] Output: Progress is updated to the database.

[0245] Step 6:

[0246] The server receives progress data and sends reminders and alerts as needed.

[0247] Input: Progress data.

[0248] Output: Reminders and alerts are sent to the user's device as appropriate.

[0249] The above are the specific processing steps and details of the program for this system.

[0250] (Application example 1)

[0251] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0252] In traditional shopping experiences, customers have to put in a lot of effort to obtain product information and find the perfect product. It's also difficult for customers to provide feedback after a purchase, set goals, or track progress. It's also difficult for customers to receive personalized recommendations based on their past purchasing history and preferences. There's a need for a system that can improve this situation and provide a more efficient and personalized shopping experience.

[0253] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0254] In this invention, the server includes: means for analyzing customer inquiries using a generative AI model and generating responses; means for providing the generated responses to the customer; means for collecting customer opinions and saving them as training data for the generative AI model; means for setting customer goals and managing progress; means for sending reminders and alerts to the customer based on their progress; means for the customer to input information about products available and for the generative AI model to recommend optimal products; and means for providing a personalized shopping experience based on the customer's past purchase history and preferences. This not only enables customers to efficiently obtain product information and find optimal products, but also facilitates post-purchase feedback, goal setting, and progress management. Furthermore, customers can receive personalized suggestions based on their past purchase history and preferences.

[0255] A "generative AI model" is a program that uses artificial intelligence algorithms to analyze input data and generate answers or suggestions in natural language.

[0256] "Customer" refers to a user who uses the system to obtain information about products or purchase products.

[0257] An "inquiry" refers to a question or request that a customer makes to the system.

[0258] A "response" is an answer or suggestion that a generative AI model generates in response to a customer inquiry.

[0259] "Opinions" refers to the feedback or thoughts that customers provide about a response.

[0260] "Training data" refers to the dataset used by a generative AI model to learn, and may include information such as opinions.

[0261] "Goal setting" refers to the act of registering specific goals and tasks that a customer wants to achieve in the system.

[0262] "Progress management" refers to the process by which a system tracks and manages the degree of achievement and progress of set goals.

[0263] "Reminder" is a function that allows the system to notify and alert you about set goals.

[0264] An "alert" is a notification that the system sends to the customer based on a specific condition or progress.

[0265] "Products handled" refers to all products offered in physical stores and online shops.

[0266] "Recommendation" refers to the act of a generative AI model suggesting the most appropriate product or service based on customer inquiries and past data.

[0267] "Purchase history" refers to a record of a customer's past purchases of products or services.

[0268] A "personalized shopping experience" refers to the entire purchasing process being individually optimized based on the customer's past purchasing history and preferences.

[0269] To implement this invention, it is necessary to build a system that uses a server, a terminal, and a generative AI model.

[0270] First, users access the system through a device such as a smartphone or smart glasses. A dedicated application called "Smart Shopping Assistant" is installed on the device. Using this application, users can log in to the system and use various functions.

[0271] The server uses a generative AI model to analyze user queries and generate a response. For example, when a user uses a smartphone to ask, "Please tell me about the ingredients in this new shampoo," the server sends this query to the generative AI model. The generative AI model uses its internal algorithm to analyze the query and generate an appropriate response. The generated response is then sent from the server to the device and displayed to the user. This response process uses advanced generative AI models such as OpenAI's GPT-3.5.

[0272] Users can also provide feedback after their purchase. For example, if a user sends feedback such as "This product was very useful," the server stores this information and uses it as training data for the generative AI model, which continuously improves the model's accuracy.

[0273] Furthermore, users can set purchasing goals and manage their progress. For example, if a user sets a goal of "completing a grocery list within a month," the server stores this information and sends periodic reminders and alerts to help users achieve their goals.

[0274] The system can also provide a personalized shopping experience based on a customer's past purchase history and preferences. For example, if a customer has previously purchased a particular brand of shampoo, the generative AI model can use that information to recommend new products from that brand, helping customers find the perfect product efficiently.

[0275] The hardware used includes cloud servers (e.g., AWS or Google Cloud), users' smartphones, and smart glasses. The software used includes OpenAI's GPT-3.5, server-side programming using Flask, and a database management system using PostgreSQL.

[0276] Below are some examples of prompt sentences.

[0277] Prompt text during the authentication process:

[0278] Please log in with your user ID "user@example.com" and password "password123".

[0279] Prompt sentences in queries and responses:

[0280] Q: What are the ingredients in your new shampoo?

[0281] Feedback gathering prompt:

[0282] This product was very helpful.

[0283] Goal setting and progress tracking prompts:

[0284] Complete your grocery list within one month.

[0285] In this way, a system can be realized that not only allows customers to efficiently obtain product information and find the most suitable product, but also makes it easy to provide feedback after purchase, and enables goal setting and progress management.

[0286] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0287] Step 1: Authentication Process

[0288] When a user accesses the system, the terminal displays a login screen. The user enters their user ID and password and presses the login button.

[0289] Input: User ID and password

[0290] Data processing: The user ID and password are sent to the server and compared with the information in the database.

[0291] Output: Authentication success or failure result

[0292] Specific operation: The server compares the authentication information stored in the database with the information entered by the user, and if authentication is successful, starts a user session and sends the following screen to the terminal.

[0293] Step 2: Questions and Answers

[0294] The user inputs a question into the dialogue interface of the terminal and sends it, and the terminal sends the question to the server.

[0295] Input: User question text

[0296] Data processing: The server sends the question text to the generative AI model, which then analyzes the question and generates an appropriate answer.

[0297] Output: Answer text

[0298] Specific operation: The server sends the generated answer to the terminal, and the terminal displays the answer to the user.

[0299] Step 3: Gather feedback

[0300] The user inputs feedback on the response into the dialogue interface and sends it, and the terminal sends the feedback to the server.

[0301] Input: User feedback text

[0302] Data processing: The server stores the feedback as training data for the generative AI model.

[0303] Output: Feedback saving completion message

[0304] What it does: The server stores the feedback data in a database and uses it to improve the generative AI model.

[0305] Step 4: Set goals and track progress

[0306] The user inputs and submits a goal on the goal setting screen, and the device sends the goal to the server.

[0307] Input: User goal text

[0308] Data processing: The server stores the goals in a database and sets reminders and alerts.

[0309] Output: Goal setting confirmation message and reminder or alert setting status

[0310] Specific behavior: The server generates periodic reminders and alerts based on the set goals and notifies the user.

[0311] Step 5: Personalized recommendations

[0312] The user inputs information about the products they sell into the terminal and sends it to the server.

[0313] Input: Product information

[0314] Data processing: The server uses a generative AI model to perform the optimal product recommendation process, providing personalized recommendations based on the user's past purchase history and preferences.

[0315] Output: Recommended products list

[0316] Specific operation: The server provides the generative AI model with information about the products on offer and the user's past data, and then sends the recommendations generated by the model to the terminal, which then displays the recommendation list to the user.

[0317] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0318] The present invention is a system that combines a generative AI model and an emotion engine to analyze questions from users and provide appropriate answers and support based on the user's emotions. A specific embodiment of this system is described below.

[0319] System Overview

[0320] The system includes the following elements:

[0321] 1. User device: The device accessed and operated by the user (e.g., smartphone, tablet, PC, etc.).

[0322] 2. Server: The central system that runs the generative AI model and emotion engine, analyzes user questions and emotions, and provides answers.

[0323] 3. Database: Stores user authentication information, usage history, feedback, goal setting information, and emotional data.

[0324] Specific functions of the system

[0325] Authentication Process

[0326] Terminal: When a user accesses the system, a login screen is displayed. The user enters their user ID and password and presses the login button.

[0327] Server: Receives the authentication information sent from the device and compares it with the information in the database. If authentication is successful, it starts a user session and sends an instruction to the device to display the next screen.

[0328] Question and sentiment analysis

[0329] Terminal: The user accesses a dialogue interface and can enter questions and options for expressing emotions (e.g., emoticons and emotion labels).

[0330] User: Enter the question and feelings for which they need assistance and click the send button.

[0331] Server: Receives the user's question and sends it to the emotion engine to analyze the user's emotion. The analysis result and the question are sent to the generative AI model to generate an appropriate answer.

[0332] Server: Adjusts the tone and content of the response based on the user's sentiment and sends the final response to the device.

[0333] Providing answers and collecting feedback

[0334] Terminal: Displays the answers received from the server to the user, and also displays an interface for the user to enter feedback on the answers.

[0335] User: Enter their thoughts on the answer, any follow-up questions, and their emotional state (e.g., satisfied, dissatisfied, etc.), then click the submit button.

[0336] Server: Receives feedback and emotion data and stores it as training data for the generative AI model.

[0337] Goal setting and progress management

[0338] Terminal: Displays the goal setting screen and provides an interface for the user to input the goals they wish to set.

[0339] User: Enter the goal and emotional state they want to achieve and click the send button.

[0340] Server: Stores the entered goal and emotional state information in a database and sets reminders and alerts for goal achievement. These notifications are adjusted based on the user's emotional state.

[0341] Specific examples

[0342] Authentication Examples

[0343] Device: The user launches a system app and is presented with a login screen.

[0344] User: Enter the user ID "user@example.com" and password "password123" and click the Login button.

[0345] Server: Checks the entered authentication information against the database, and displays the main interface if authentication is successful.

[0346] Specific examples of question and sentiment analysis

[0347] Terminal: The user types "Please tell me about risk management for a new project," selects the emotion emoticon "anxiety," and sends.

[0348] Server: Analyzing the question and sentiment, the generative AI model generates an answer such as, "The basic outline of risk management is as follows..." The answer has a warm tone to allay the user's anxiety.

[0349] Terminal: The answer is displayed to the user.

[0350] Examples of feedback collection

[0351] Device: User types "This answer was very helpful," selects "Satisfied" as the emotional state, and submits.

[0352] Server: Stores feedback and emotional states as training data.

[0353] Specific examples of goal setting and progress management

[0354] Terminal: User types "Complete project risk analysis within one month," selects the emotion emoticon "Motivated," and submits.

[0355] Server: Store your goals and emotional state and set tone-sensitive reminders like, "You're on track!"

[0356] Users: Enter and update their progress regularly.

[0357] Server: Check in on progress and send positive reminders, such as "Great progress!"

[0358] The system helps users acquire new skills and knowledge by implementing the following processes: authentication, question and emotion analysis, answer provision and feedback collection, goal setting and progress management. By combining it with an emotion engine, it can respond to users' emotions and provide more personalized support.

[0359] The processing flow will be explained below.

[0360] Step 1: "Enter your credentials"

[0361] Terminal: Displays the login screen and provides an interface for the user to enter their user ID and password.

[0362] User: Enter your user ID and password and click the Login button.

[0363] Step 2: "Submit authentication information"

[0364] Terminal: Sends the authentication information (user ID and password) entered by the user to the server.

[0365] Step 3: "Verify Authentication"

[0366] Server: Compares the received authentication information with the information in the database and generates an authentication result.

[0367] Server: If authentication is successful, it starts a user session and sends instructions to the terminal to display the next screen. If authentication fails, it returns an error message.

[0368] Step 4: View authentication results

[0369] Terminal: If authentication is successful, display the main interface. If authentication fails, display an error message to the user.

[0370] Step 5: "Enter a question and emotion"

[0371] Terminal: Presents a dialogue interface for the user to enter a question and provides the option to select an emotion emoticon.

[0372] User: Enter a question along with an emotion emoticon or emotion label and click the submit button.

[0373] Step 6: "Submit your questions and feelings"

[0374] Terminal: Sends the questions and emotion data entered by the user to the server.

[0375] Step 7: "Analyze emotions"

[0376] Server: Uses an emotion engine to analyze the received emotion data and identify the user's emotional state.

[0377] Server: Sends the analysis results to the generative AI model.

[0378] Step 8: Parse the question and generate an answer

[0379] Server: The generative AI model generates an answer based on the received question and the results of sentiment analysis.

[0380] Server: Adjust the tone of your response to match the user's emotional state.

[0381] Server: Sends the final answer to the device.

[0382] Step 9: "View Answers"

[0383] Terminal: Displays the final answer received from the server to the user.

[0384] Step 10: "Enter your feedback"

[0385] Terminal: Displays an interface for users to enter feedback on their answers, with the option to also enter their emotional state.

[0386] User: Enter your thoughts, follow-up questions, or emotional state and click the send button.

[0387] Step 11: Send feedback and emotions

[0388] Terminal: Sends the user-entered feedback and emotional state to the server.

[0389] Step 12: Analyze feedback and sentiment

[0390] Server: Analyzes the received feedback and emotional state and stores it as training data for the generative AI model.

[0391] Step 13: Enter your goal settings

[0392] Terminal: Displays the goal setting screen and provides an interface for the user to set goals.

[0393] User: Enter the goal and emotional state they want to achieve and click the send button.

[0394] Step 14: "Preserve your goals and feelings"

[0395] Terminal: Sends the goal information and emotional state entered by the user to the server.

[0396] Server: Stores goal information and emotional state in a database and sets reminders and alerts for goal achievement.

[0397] Step 15: Enter your progress

[0398] Terminal: Displays an interface for the user to enter progress.

[0399] Users: Enter progress and update regularly.

[0400] Step 16: Progress Tracking and Emotion-Based Notifications

[0401] Server: Checks progress and emotional state in a database and generates reminders and alerts.

[0402] Server: Adjusts the content of notifications based on the emotional state and sends them to the user.

[0403] On the device: Displaying received reminders and alerts to the user.

[0404] Example 2

[0405] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0406] While systems using conventional generative AI models can provide answers based on user questions, it is difficult to provide personalized answers that take user emotions into account. It is also necessary to efficiently collect user feedback and use it as training data for generative AI models. Furthermore, it is also necessary to support user goal setting and progress management, and to appropriately send emotional reminders and alerts. It is necessary to solve these problems and improve the user experience.

[0407] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0408] In this invention, the server includes a means for analyzing user questions and emotional information using a generative AI model to generate answers, a means for providing the generated answers to the user and adjusting the tone and content based on the user's emotions, and a means for collecting user feedback and emotional data and saving it as training data for the generative AI model. This enables the provision of personalized answers to user questions that take emotions into account. The server also includes a means for setting user goals and managing progress, and a means for sending reminders and alerts to the user based on their progress and emotional state, thereby helping the user achieve their goals and maintaining their motivation at the appropriate time.

[0409] A "generative AI model" is an artificial intelligence algorithm that generates appropriate answers based on user questions and prompts.

[0410] "Emotional information" refers to the emotional expressions (e.g., emoticons or emotion labels) that users input into the system, and is data that represents the user's emotional state.

[0411] "Feedback" refers to the act and content of a user providing evaluation or additional feedback on an answer.

[0412] "Training data" is a dataset, such as collected feedback or sentiment data, that is used to improve the performance of a generative AI model.

[0413] "Goal setting" refers to the process of setting goals that users want to achieve and the content of those goals.

[0414] "Progress management" is the process of reviewing, tracking, and managing the progress of a user toward the goals they have set.

[0415] A "reminder" is a notification sent from the server to encourage the user to achieve their set goals or progress.

[0416] "Alerts" are important notifications or warnings that occur based on the user's progress or emotional state.

[0417] An "emotion engine" is a technology that analyzes a user's emotions and adjusts the tone and content of the generative AI model's responses based on the results.

[0418] "Tone" refers to the emotional nuance and style of expression of the responses generated.

[0419] MODE FOR CARRYING OUT THE INVENTION

[0420] System Overview

[0421] The present invention is a system that combines a generative AI model and an emotion engine to analyze user questions and provide appropriate answers and support based on the user's emotions. This system is designed to improve the user experience and provide personalized support. A specific embodiment of this system is described below.

[0422] Hardware and software used

[0423] User device: The device accessed and operated by a user (e.g., smartphone, tablet, PC).

[0424] Server: The central system that runs the generative AI model and emotion engine.

[0425] Database: Stores user authentication information, usage history, feedback, goal setting information, and emotional data.

[0426] Generative AI model: An artificial intelligence algorithm that generates answers based on user questions and prompts.

[0427] Emotion engine: A technology that analyzes user emotions and adjusts the tone and content of the generative AI model's responses.

[0428] Example

[0429] 1. User Authentication

[0430] Terminal: When a user accesses the system, a login screen is displayed. The user enters their user ID and password and presses the login button.

[0431] Server: Receives the authentication information sent from the terminal and compares it with the information in the database. If authentication is successful, it sends an instruction to the terminal to display the next screen.

[0432] Example: A user launches a system app, enters the user ID "user@example.com" and password "password123", and clicks the login button. The server checks the authentication information against the database, and if authentication is successful, displays the main interface.

[0433] 2. Question and Sentiment Analysis

[0434] Terminal: The user accesses a dialogue interface and can enter questions and options for expressing emotions (e.g., emoticons and emotion labels).

[0435] User: Enter the question and feelings for which they need assistance and click the send button.

[0436] Server: Receives the user's question and sends it to the emotion engine to analyze the user's emotion. The analysis result and the question are sent to the generative AI model to generate an appropriate answer.

[0437] Server: Adjusts the tone and content of the response based on the user's sentiment and sends the final response to the device.

[0438] Example: A user types, "Please tell me about risk management for a new project," selects the emotion emoticon "anxiety," and submits the question. The server analyzes the question and emotion, and the generative AI model generates an answer: "The basic outline of risk management is as follows..." The answer has a warm tone to alleviate the user's anxiety.

[0439] 3. Providing answers and collecting feedback

[0440] Terminal: Displays the answers received from the server to the user, and also displays an interface for the user to enter feedback on the answers.

[0441] User: Enter their thoughts on the answer, any follow-up questions, or their emotional state (e.g., satisfied, dissatisfied), and click the submit button.

[0442] Server: Receives feedback and emotion data and stores it as training data for the generative AI model.

[0443] Example: A user types "This answer was very helpful," selects "Satisfied" as the emotional state, and submits it. The server saves the feedback and emotional state as training data.

[0444] 4. Goal setting and progress management

[0445] Terminal: Displays the goal setting screen and provides an interface for the user to input the goals they wish to set.

[0446] User: Enter the goal and emotional state they want to achieve and click the send button.

[0447] Server: Stores the entered goal and emotional state information in a database and sets reminders and alerts for goal achievement. These notifications are adjusted based on the user's emotional state.

[0448] Example: A user enters "Complete a risk analysis for the project within one month," selects the emotion emoticon "Motivated," and submits it. The server saves the goal and emotional state, and sets tone-based reminders such as "You're making good progress!" The user periodically enters and updates their progress. The server monitors their progress and sends positive reminders such as "Great progress!"

[0449] The system helps users acquire new skills and knowledge by implementing the following processes: authentication, question and emotion analysis, answer provision and feedback collection, goal setting and progress management. By combining it with an emotion engine, it can respond to users' emotions and provide more personalized support.

[0450] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0451] Step 1:

[0452] Displaying the login screen

[0453] Terminal: When a user accesses the system, a login screen is displayed, providing fields for entering a user ID and password, and a login button.

[0454] Input: User ID, Password

[0455] Output: Sending login information

[0456] Step 2:

[0457] Sending authentication information

[0458] User: Enter your user ID (e.g., "user@example.com") and password (e.g., "password123") and click the Login button.

[0459] Terminal: Sends the entered user ID and password to the server.

[0460] Input: The credentials entered by the user

[0461] Output: Sending authentication information to the server

[0462] Step 3:

[0463] Authentication Check

[0464] Server: Receives the user ID and password sent from the terminal and accesses the database to verify this information.

[0465] Server: If authentication is successful, it starts a user session and sends a signal to the terminal to display the following screen. If authentication fails, it sends an error message to the terminal.

[0466] Input: Authentication information sent from the device

[0467] Output: Authentication result (success or failure)

[0468] Step 4:

[0469] Viewing authentication results

[0470] Terminal: Based on the signal received from the server, if authentication is successful, it displays the main interface, otherwise it displays an error message (e.g. "Authentication failed. Please try again").

[0471] Input: Authentication result sent from the server

[0472] Output: Main interface or error message display

[0473] Step 5:

[0474] Enter your question

[0475] User: Enters a question using the dialogue interface (e.g., "Please tell me about risk management for a new project") and selects an emoticon option (e.g., emoticon "😟" for anxiety).

[0476] Terminal: Provides the user with a question and emotional expression options, and sends this data to the server when the user clicks the send button.

[0477] Input: Questions and sentiment information entered by the user

[0478] Output: Sends questions and emotion information to the server

[0479] Step 6:

[0480] Question and sentiment analysis

[0481] Server: The server receives questions and emotion data from the device and sends them to the emotion engine for analysis. The emotion engine analyzes the user's emotion (e.g., anxiety) and returns the analysis results to the generative AI model.

[0482] Server: Generates and sends a prompt to the generative AI model (e.g., "The user is curious and anxious about risk management for a new project. Please generate an appropriate answer to this question.").

[0483] Input: User question and sentiment information

[0484] Output: Sentiment analysis results and generated prompt sentences

[0485] Step 7:

[0486] Generate answers

[0487] Generative AI model: Generates an answer to a user's question based on the prompt sent (e.g., "A basic overview of risk management is as follows...") The answer incorporates sentiment analysis and adds a gentle tone to allay any anxiety.

[0488] Input: Generated prompt text

[0489] Output: The generated answer

[0490] Step 8:

[0491] Providing answers

[0492] Server: Sends the generated answer to the device.

[0493] Terminal: Display the answer to the user.

[0494] Input: Generated Answer

[0495] Output: Show answer

[0496] Step 9:

[0497] Enter your feedback

[0498] User: Enters their thoughts about the provided answer (e.g., "This answer was very helpful") and emotional state (e.g., "Satisfied"), and clicks the submit button.

[0499] Terminal: Sends input feedback and emotional state to the server.

[0500] Input: Feedback and emotional state entered by the user

[0501] Output: Sending feedback and emotional state to the server

[0502] Step 10:

[0503] Save your feedback

[0504] Server: The feedback and emotion data received from the device is stored in a database and used as training data for the generative AI model.

[0505] Input: Feedback and emotion data sent from the device

[0506] Output: Save to database

[0507] Step 11:

[0508] Enter your goal settings

[0509] User: Uses the goal setting screen to enter a new goal (e.g., "Complete project risk analysis within one month") and emotional state (e.g., "Motivated"), then clicks the submit button.

[0510] Terminal: Sends goals and emotional state to the server.

[0511] Input: Goals and emotional states entered by the user

[0512] Output: Sending goals and emotional states to the server

[0513] Step 12:

[0514] Saving goals and emotional states

[0515] Server: Stores the received goal and emotional state information in a database. Sets reminders and alerts for goal achievement and adjusts them based on the user's emotional state.

[0516] Input: User's goal and emotional state

[0517] Output: Save to database and set reminder

[0518] Step 13:

[0519] Progress management

[0520] Users: Periodically enter and update progress (e.g., "50% of risk analysis is complete").

[0521] Server: Stores the received progress in a database and sends context-appropriate positive reminders and supportive messages (e.g., "Great progress!") to the device.

[0522] On the device: Display reminders and support messages to the user.

[0523] Input: progress

[0524] Output: Saving progress information to a database and displaying messages

[0525] The above is the specific flow of the system's program processing, which provides personalized support to users and enables efficient operation of the system.

[0526] (Application example 2)

[0527] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0528] On existing online shopping sites, responses to user questions are often limited to standard phrases or basic guidance, lacking personalized responses that take into account the individual feelings and circumstances of the user. Furthermore, there is no mechanism to adequately alleviate the uncertainty and anxiety users may feel when choosing a product. As a result, users are experiencing a decline in satisfaction and a decrease in their desire to purchase.

[0529] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0530] In this invention, the server includes a means for analyzing a user's emotions and adjusting the content and tone of answers and advice based on the results, a means for recommending products based on the content of the user's question and the results of the emotion analysis, and a means for analyzing the user's question using a generative AI model and generating an answer, thereby enabling personalized responses that take into account not only the content of the user's question but also their emotions.

[0531] A "generative AI model" is a type of artificial intelligence that generates natural language responses based on given text data, and is a model with advanced language processing capabilities.

[0532] "Analyzing emotions" means analyzing the emotional information contained in the text data collected from the user using specific algorithms or software to determine the user's emotional state.

[0533] "Analyzing a question" means using text analysis techniques to analyze the question text provided by the user and understand its content and purpose.

[0534] "Generating an answer" means automatically generating an appropriate response based on the analyzed question data and providing it to the user.

[0535] "Providing to the user" means displaying the generated answers and recommendations on the user's device.

[0536] "Gathering feedback" means collecting data on opinions and feelings about responses provided by users.

[0537] "Storing as training data" means accumulating collected feedback and other data in a database as training data for improving the performance of generative AI models.

[0538] "Setting goals and managing progress" means managing the progress of the goals set by the user and recording appropriate progress.

[0539] "Send reminders and alerts" means sending timely notifications about the progress of goals set by the user and important events.

[0540] "Adjusting your tone" means changing the wording and presentation of your responses depending on the user's emotional state.

[0541] "Making product recommendations" means selecting and recommending appropriate products based on the user's questions and emotional state.

[0542] System Overview

[0543] The system for implementing this invention consists of a terminal used by the user, a server connected to it, and a database. The user accesses an online shopping site application using a smartphone and inputs a question about a product. This question and the user's emotional information are sent to the server, where it is analyzed and answered.

[0544] Technology used

[0545] Hardware: Smartphones, servers

[0546] Software: Generative AI models (e.g., OpenAI's GPT-3.5), emotion engines (e.g., IBM Watson), databases (e.g., MySQL)

[0547] Question and sentiment analysis

[0548] The device sends the user's question and emotional information to the server, which uses an emotion engine to analyze the user's emotions and passes the results to the generative AI model, which then generates the optimal answer based on the user's question and emotions.

[0549] Specific examples

[0550] The user types, "What size is this shirt?" and selects the emotion emoticon "unsure." The server analyzes the question and emotion, and the generative AI model responds, "Please refer to the shirt size chart. If you're unsure, we recommend you choose your usual size."

[0551] Product recommendation function

[0552] The server makes product recommendations based on the user's questions and sentiment analysis results. A generative AI model selects products based on the user's preferences and sentiment, and provides them to the user's device as a list.

[0553] Specific examples

[0554] The user types "What's your recommendation for a birthday present?", selects the emoticon "Joy," and submits the question. The server analyzes the question and emotion, and the generative AI model responds with "Choose a gift that matches the recipient's tastes! Here are some recommended gifts," making product recommendations in a tone that conveys joy.

[0555] Gathering feedback and learning

[0556] The user's device displays a feedback interface, and the user inputs their thoughts on the answers and recommendations. The server receives this feedback and stores it as training data for the generative AI model, which improves the accuracy of future answers.

[0557] Prompt Sentence Examples

[0558] What would you recommend as a birthday gift? Emotion: Joy. Generate an appropriate answer based on this.

[0559] Goal setting and progress management

[0560] The user's device displays a goal setting screen and the user inputs the goal they want to set. The server stores the input goal and emotional state information in a database and sets reminders and alerts for goal achievement. These notifications are adjusted based on the user's emotional state.

[0561] Specific examples

[0562] A user types "Complete a risk analysis of the project within one month," selects the emotion emoticon "Motivated," and submits the goal. The server saves the goal and emotional state and sets a reminder with a tone, such as "You're on track!"

[0563] In this way, the system provides highly personalized support that responds to the user's individual circumstances and emotions.

[0564] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0565] Step 1:

[0566] When a user launches an online shopping site application on their smartphone, the login screen appears. The user enters their user ID and password and clicks the login button.

[0567] Input: User ID "user@example.com" and password "password123"

[0568] Output: Authentication information sent to the server

[0569] Step 2:

[0570] The server receives the authentication information submitted and checks it against its database. If authentication is successful, it starts a user session and displays the following screen:

[0571] Input: Credentials

[0572] Output: User authentication result and session start instruction

[0573] Step 3:

[0574] User types a question about the product, selects an emotion emoticon, and submits it. Example: "Please tell me about risk management for a new project" (emotion emoticon: "anxiety")

[0575] Input: Question text and emotion emoticons

[0576] Output: The user's question and emotion information are sent to the server.

[0577] Step 4:

[0578] The server receives the user's question and emotional information, sends it to the emotion engine for emotional analysis, and passes the analyzed emotional data to the generative AI model, which then generates the optimal answer to the question.

[0579] Input: Question text and sentiment information

[0580] Output: Parsed sentiment data and generated answers

[0581] Step 5:

[0582] The server receives the generated answers, adjusts the tone and content of the answers based on the user's feelings, and sends the final answers to the user's smartphone.

[0583] Input: Generated answers and sentiment data

[0584] Output: Adjusted answer

[0585] Step 6:

[0586] The device displays the tailored answer to the user, who then enters their feedback or emotional state (e.g., "Satisfied") about the answer and clicks the submit button.

[0587] Input: User feedback and emotional state

[0588] Output: Feedback and emotional state are sent to the server

[0589] Step 7:

[0590] The server stores the received feedback and emotional state in a database as training data for the generative AI model.

[0591] Input: Feedback and emotional state

[0592] Output: Save as training data

[0593] Step 8:

[0594] The user uses the goal setting screen to input the goal and emotional state they want to set (e.g., "Complete the project risk analysis within one month," emotional emoticon: "motivated").

[0595] Input: Target text and emotion emoticons

[0596] Output: Goal and emotion information is sent to the server

[0597] Step 9:

[0598] The server stores the received goal and emotional information in a database and sets reminders and alerts for goal achievement, which are adjusted based on the user's emotional state.

[0599] Input: goal and emotion information

[0600] Output: Instructions for setting reminders and alerts

[0601] Step 10:

[0602] The device periodically prompts the user for progress input and updates, and the server monitors the progress and sends appropriate reminders and alerts (e.g., "Great progress!").

[0603] Input: Progress data

[0604] Output: Reminders and alerts

[0605] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0606] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0607] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0608] [Second embodiment]

[0609] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0610] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0611] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0612] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0613] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0614] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0615] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0616] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0617] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0618] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0619] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0620] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0621] The present invention is a system that utilizes a generative AI model to provide an environment where users can easily ask for advice on anything, anytime. This system begins with user authentication and includes processes such as interactive assistance, providing responses, collecting feedback, goal setting, and progress management. A specific embodiment of this system is described below.

[0622] System Overview

[0623] The system includes the following elements:

[0624] 1. User device: The device accessed and operated by the user (e.g., smartphone, tablet, PC, etc.).

[0625] 2. Server: The central system that runs the generative AI model, analyzes user questions, and provides answers.

[0626] 3. Database: Stores user authentication information, usage history, feedback, and goal setting information.

[0627] Specific functions of the system

[0628] Authentication Process

[0629] Terminal: When a user accesses the system, a login screen is displayed. The user enters their user ID and password and presses the login button.

[0630] Server: Receives the authentication information sent from the device and compares it with the information in the database. If authentication is successful, it starts a user session and sends an instruction to the device to display the next screen.

[0631] Questions and Answers

[0632] Terminal: The user accesses the dialogue interface and enters a question. When the user has finished entering the question, he or she clicks the submit button.

[0633] Server: Receives the user's question and sends it to the generative AI model, which analyzes the question and generates an appropriate answer.

[0634] Server: Sends the generated answer to the user's device.

[0635] Terminal: Display the answer to the user.

[0636] Feedback collection

[0637] Terminal: A feedback interface is displayed where users can enter their thoughts on the answers or ask follow-up questions.

[0638] Server: Receives user-entered feedback and stores it as training data for the generative AI model.

[0639] Goal setting and progress management

[0640] Terminal: Displays the goal setting screen and provides an interface for the user to input the goals they wish to set.

[0641] Server: Stores the entered goals in a database and sets reminders and alerts.

[0642] Terminal: User periodically enters and updates progress.

[0643] Server: Checks progress and sends reminders and alerts accordingly.

[0644] Specific examples

[0645] Authentication Examples

[0646] Device: The user launches the Buddy AI app and the login screen appears.

[0647] User: Enter the user ID "user@example.com" and password "password123" and click the Login button.

[0648] Server: Checks the entered authentication information against the database, and displays the main interface if authentication is successful.

[0649] Specific examples of questions and answers

[0650] Terminal: The user types in "Please tell me about risk management for a new project" and submits.

[0651] Server: Sends questions to the generative AI model and generates answers for risk management.

[0652] Terminal: The answer "The basic outline of risk management is as follows..." is displayed.

[0653] Examples of feedback collection

[0654] Device: User types "This answer was very helpful" and submits.

[0655] Server: Saves the feedback as training data.

[0656] Specific examples of goal setting and progress management

[0657] Terminal: The user types in "Complete the project risk analysis within one month" and submits.

[0658] Server: Save your goals and set weekly progress reminders.

[0659] Users: Enter and update their progress regularly.

[0660] Server: Checks progress and sends necessary reminders and alerts.

[0661] As described above, this system provides an environment in which users can easily acquire new skills and knowledge by implementing the processes of authentication, question and answer, feedback collection, goal setting, and progress management.

[0662] The processing flow will be explained below.

[0663] Step 1: "Enter your credentials"

[0664] Terminal: Displays the login screen and provides an interface for the user to enter their user ID and password.

[0665] User: Enter your user ID and password and click the Login button.

[0666] Step 2: "Submit authentication information"

[0667] Terminal: Sends the authentication information (user ID and password) entered by the user to the server.

[0668] Step 3: "Verify Authentication"

[0669] Server: Compares the received authentication information with the information in the database and generates an authentication result.

[0670] Server: If authentication is successful, it starts a user session and sends instructions to the terminal to display the next screen. If authentication fails, it returns an error message.

[0671] Step 4: View authentication results

[0672] Terminal: If authentication is successful, display the main interface. If authentication fails, display an error message to the user.

[0673] Step 5: "Enter your question"

[0674] Terminal: displays a dialogue interface for users to enter questions.

[0675] User: Type in the question they need help with and click the submit button.

[0676] Step 6: Submit your question

[0677] Terminal: Sends the questions entered by the user to the server.

[0678] Step 7: Parse the Question

[0679] Server: Sends the received question to the generative AI model and analyzes the question.

[0680] Step 8: Generate answers

[0681] Server: The generative AI model analyzes the intent of the question and generates an appropriate answer, taking into account the user's past usage history and characteristics as necessary.

[0682] Server: Sends the generated answer to the device.

[0683] Step 9: "View Answers"

[0684] Terminal: Displays the answer received from the server to the user.

[0685] Step 10: "Enter your feedback"

[0686] Terminal: Displays an interface for users to enter feedback on their answers.

[0687] User: Enter their thoughts on the answer or any follow-up questions and click the submit button.

[0688] Step 11: "Submit Feedback"

[0689] Terminal: Sends the feedback entered by the user to the server.

[0690] Step 12: Analyze feedback

[0691] Server: Analyzes the received feedback and stores it as training data for the generative AI model.

[0692] Step 13: Enter your goal settings

[0693] Terminal: Displays an interface for users to set goals.

[0694] User: Enter the goal they want to achieve and click the submit button.

[0695] Step 14: "Save Goal"

[0696] Terminal: Sends the target information entered by the user to the server.

[0697] Server: Stores goal information in a database and sets reminders and alerts for goal achievement.

[0698] Step 15: Enter your progress

[0699] Terminal: Displays an interface for the user to enter progress.

[0700] Users: Enter progress and update regularly.

[0701] Step 16: "Progress Tracking and Notifications"

[0702] Server: Checks the progress in the database, generates reminders and alerts accordingly, and notifies the user.

[0703] On the device: Displays reminders and alerts received from the server to the user.

[0704] Through these steps, the system helps users acquire new skills and knowledge.

[0705] Example 1

[0706] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0707] In today's information environment, it is important for users to be able to quickly respond to a variety of questions and instantly obtain the information they need. However, conventional systems were unable to provide appropriate answers to users' questions and had difficulty effectively collecting and utilizing user feedback. Furthermore, they lacked functionality for user goal setting and progress management, making it difficult for users to effectively manage their own learning and work progress.

[0708] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0709] In this invention, the server includes means for analyzing questions from users and using a generative AI model to generate answers, means for providing the generated answers to the users, means for collecting user feedback and saving it as learning data for the generative AI model, means for setting user goals and managing progress, means for sending reminders and alerts to the user according to progress, means for verifying authentication information and starting a user session if authentication is successful, and means for sending the authentication result to the user terminal. This allows users to receive prompt and appropriate answers to their questions and further enables them to improve their learning and work efficiency through feedback and progress management.

[0710] A "generative AI model" is an artificial intelligence technology that analyzes questions from users and generates appropriate answers.

[0711] A "means" is a specific method or device for achieving a specific function or action.

[0712] "User" refers to a person who uses the system to enter questions or set goals.

[0713] "Feedback" refers to responses provided by users, such as their impressions of the answers and services, or any follow-up questions.

[0714] "Training data" is data that a generative AI model uses to improve and refine its accuracy.

[0715] "Goal setting" is the process by which a user inputs the goals they want to achieve and manages their progress.

[0716] "Progress management" refers to checking and managing the degree of achievement and progress toward set goals.

[0717] "Reminders" is a feature that sends notifications at specific times or situations to help users remember their set goals and tasks.

[0718] An "alert" is a notification sent to alert or warn the user.

[0719] "Authentication information" refers to information such as user ID and password that a user enters when accessing a system.

[0720] A "user session" refers to the series of operations a user performs from the time they log in to the time they log out of the system.

[0721] A "database" is a system for structuring, storing, and managing data such as authentication information and usage history.

[0722] The present invention is a system that provides an environment where users can easily ask any question at any time. This system utilizes a generative AI model and encompasses processes such as user authentication, interactive assistance, response provision, feedback collection, goal setting and progress management. A specific embodiment of this system is described below.

[0723] System configuration

[0724] The system includes the following elements:

[0725] 1. User device: The device accessed and operated by the user (e.g., smartphone, tablet, PC, etc.).

[0726] 2. Server: The central system that runs the generative AI model, analyzes user questions, and provides answers.

[0727] 3. Database: A system that stores user authentication information, usage history, feedback, and goal setting information.

[0728] Hardware and Software

[0729] User device: Provides the front-end interface that users access. For example, it can be a smartphone application running on iOS or Android, or a web application running on a web browser.

[0730] Server: Cloud-based computing resources for running generative AI models (e.g., GPT-4), such as virtual machines or container services provided by Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure.

[0731] Database: A database system for storing user information and usage history. Examples include Amazon RDS, Google Cloud SQL, and Microsoft SQL Server.

[0732] Specific steps in the process and data handling

[0733] Authentication Process

[0734] When a user accesses the system from a user terminal, a login screen is displayed. The user enters their user ID and password and presses the login button. The server receives the authentication information sent from the terminal and compares it with the information in the database. If authentication is successful, it starts a user session and sends an instruction to the terminal to display the next screen.

[0735] Handling Questions and Answers

[0736] The user accesses the dialogue interface and inputs a question. For example, the user inputs and submits "Please tell me about risk management for a new project." The server receives the user's question and sends it to the generative AI model. The generative AI model analyzes the question and generates an appropriate answer. The server sends the generated answer to the user's device, and the device displays the answer to the user.

[0737] Feedback collection

[0738] A feedback interface is displayed in which the user can enter their thoughts about the answer or any additional questions. For example, they can enter "This answer was very helpful" and submit. The server receives the user's feedback and stores it as training data for the generative AI model.

[0739] Goal setting and progress management

[0740] It displays a goal setting screen and provides an interface for users to input the goals they want to set. For example, a user can input "Complete project risk analysis within one month" and submit it. The server saves the input goals in a database and sets reminders and alerts. Users can periodically input and update their progress, and the server will check the progress and send reminders and alerts as appropriate.

[0741] This allows users to efficiently acquire their own skills and knowledge. The system provides interactive support to users, helping them manage their learning and work progress through feedback and goal setting, thereby enabling users to effectively manage themselves.

[0742] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0743] Authentication Process

[0744] Step 1:

[0745] A user accesses the system using a user terminal, which displays the login screen.

[0746] Input: Enter your user ID and password.

[0747] Output: The input information is displayed on the login screen.

[0748] Step 2:

[0749] The user presses the login button.

[0750] Input: User ID and password.

[0751] Output: The input information is sent to the server.

[0752] Step 3:

[0753] The server receives the authentication information sent from the terminal and compares it with the authentication information stored in the database.

[0754] Input: User ID and password.

[0755] Data processing: Retrieve user information from the database and compare it with the received authentication information.

[0756] Output: Authentication result (success or failure).

[0757] Step 4:

[0758] The server sends the authentication result to the terminal.

[0759] Input: Authentication result.

[0760] Output: The authentication result is sent to the terminal.

[0761] Step 5:

[0762] The device will display the following screen based on the authentication result: the main interface if successful, or an error message if unsuccessful.

[0763] Input: Authentication result.

[0764] Output: The main interface or an error message is displayed.

[0765] Question and Answer Process

[0766] Step 1:

[0767] The user accesses the dialogue interface and enters a question.

[0768] Input: Question (e.g. "Please tell me about risk management for a new project").

[0769] Output: The entered question is displayed on the screen.

[0770] Step 2:

[0771] The user presses the send button.

[0772] Input: Question.

[0773] Output: The question is sent to the server.

[0774] Step 3:

[0775] The server receives the user's query.

[0776] Input: Question.

[0777] Output: The question is processed as a prompt that is sent to the generative AI model.

[0778] Step 4:

[0779] A generative AI model analyzes the question and generates an appropriate answer.

[0780] Input: The question as a prompt.

[0781] Data computation: Analyzing questions and generating answers based on relevant information.

[0782] Output: The generated answer.

[0783] Step 5:

[0784] The server receives the generated response and transmits it to the user terminal.

[0785] Input: The generated answer.

[0786] Output: The answer is sent to the user's device.

[0787] Step 6:

[0788] The device displays the answer to the user.

[0789] Input:Answer.

[0790] Output: The answer is displayed on the screen.

[0791] Feedback Collection Process

[0792] Step 1:

[0793] Users can enter their thoughts and follow-up questions into the feedback interface.

[0794] Input: Feedback (e.g., "This answer was very helpful").

[0795] Output: Feedback is displayed on the screen.

[0796] Step 2:

[0797] The user presses the send button.

[0798] Input: Feedback content.

[0799] Output: Feedback is sent to the server.

[0800] Step 3:

[0801] The server receives the feedback and stores it as training data for the generative AI model.

[0802] Input: Feedback content.

[0803] Data processing: The feedback content is organized and saved in a database as learning data.

[0804] Output: The feedback is stored in a database.

[0805] Goal setting and progress management process

[0806] Step 1:

[0807] The user accesses the goal setting screen and enters the goal.

[0808] Input: Objective (e.g., "Complete project risk analysis within one month").

[0809] Output: The entered goal is displayed on the screen.

[0810] Step 2:

[0811] The user presses the send button.

[0812] Input: Goal content.

[0813] Output: The goal is sent to the server.

[0814] Step 3:

[0815] The server receives the target content and stores it in a database.

[0816] Input: Goal content.

[0817] Data processing: Organize the target content and save it in a database.

[0818] Output: The goal is saved in the database.

[0819] Step 4:

[0820] The server sets reminders and alerts and sends them to the user accordingly.

[0821] Inputs: Progress data, alert conditions.

[0822] Output: Reminders and alerts are set and sent to the user's device.

[0823] Step 5:

[0824] Users periodically enter and update their progress.

[0825] Input: Progress data (e.g., "Risk analysis 50% complete").

[0826] Output: Progress is updated to the database.

[0827] Step 6:

[0828] The server receives progress data and sends reminders and alerts as needed.

[0829] Input: Progress data.

[0830] Output: Reminders and alerts are sent to the user's device as appropriate.

[0831] The above are the specific processing steps and details of the program for this system.

[0832] (Application example 1)

[0833] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0834] In traditional shopping experiences, customers have to put in a lot of effort to obtain product information and find the perfect product. It's also difficult for customers to provide feedback after a purchase, set goals, or track progress. It's also difficult for customers to receive personalized recommendations based on their past purchasing history and preferences. There's a need for a system that can improve this situation and provide a more efficient and personalized shopping experience.

[0835] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0836] In this invention, the server includes: means for analyzing customer inquiries using a generative AI model and generating responses; means for providing the generated responses to the customer; means for collecting customer opinions and saving them as training data for the generative AI model; means for setting customer goals and managing progress; means for sending reminders and alerts to the customer based on their progress; means for the customer to input information about products available and for the generative AI model to recommend optimal products; and means for providing a personalized shopping experience based on the customer's past purchase history and preferences. This not only enables customers to efficiently obtain product information and find optimal products, but also facilitates post-purchase feedback, goal setting, and progress management. Furthermore, customers can receive personalized suggestions based on their past purchase history and preferences.

[0837] A "generative AI model" is a program that uses artificial intelligence algorithms to analyze input data and generate answers or suggestions in natural language.

[0838] "Customer" refers to a user who uses the system to obtain information about products or purchase products.

[0839] An "inquiry" refers to a question or request that a customer makes to the system.

[0840] A "response" is an answer or suggestion that a generative AI model generates in response to a customer inquiry.

[0841] "Opinions" refers to the feedback or thoughts that customers provide about a response.

[0842] "Training data" refers to the dataset used by a generative AI model to learn, and may include information such as opinions.

[0843] "Goal setting" refers to the act of registering specific goals and tasks that a customer wants to achieve in the system.

[0844] "Progress management" refers to the process by which a system tracks and manages the degree of achievement and progress of set goals.

[0845] "Reminder" is a function that allows the system to notify and alert you about set goals.

[0846] An "alert" is a notification that the system sends to the customer based on a specific condition or progress.

[0847] "Products handled" refers to all products offered in physical stores and online shops.

[0848] "Recommendation" refers to the act of a generative AI model suggesting the most appropriate product or service based on customer inquiries and past data.

[0849] "Purchase history" refers to a record of a customer's past purchases of products or services.

[0850] A "personalized shopping experience" refers to the entire purchasing process being individually optimized based on the customer's past purchasing history and preferences.

[0851] To implement this invention, it is necessary to build a system that uses a server, a terminal, and a generative AI model.

[0852] First, users access the system through a device such as a smartphone or smart glasses. A dedicated application called "Smart Shopping Assistant" is installed on the device. Using this application, users can log in to the system and use various functions.

[0853] The server uses a generative AI model to analyze user queries and generate a response. For example, when a user uses a smartphone to ask, "Please tell me about the ingredients in this new shampoo," the server sends this query to the generative AI model. The generative AI model uses its internal algorithm to analyze the query and generate an appropriate response. The generated response is then sent from the server to the device and displayed to the user. This response process uses advanced generative AI models such as OpenAI's GPT-3.5.

[0854] Users can also provide feedback after their purchase. For example, if a user sends feedback such as "This product was very useful," the server stores this information and uses it as training data for the generative AI model, which continuously improves the model's accuracy.

[0855] Furthermore, users can set purchasing goals and manage their progress. For example, if a user sets a goal of "completing a grocery list within a month," the server stores this information and sends periodic reminders and alerts to help users achieve their goals.

[0856] The system can also provide a personalized shopping experience based on a customer's past purchase history and preferences. For example, if a customer has previously purchased a particular brand of shampoo, the generative AI model can use that information to recommend new products from that brand, helping customers find the perfect product efficiently.

[0857] The hardware used includes cloud servers (e.g., AWS or Google Cloud), users' smartphones, and smart glasses. The software used includes OpenAI's GPT-3.5, server-side programming using Flask, and a database management system using PostgreSQL.

[0858] Below are some examples of prompt sentences.

[0859] Prompt text during the authentication process:

[0860] Please log in with your user ID "user@example.com" and password "password123".

[0861] Prompt sentences in queries and responses:

[0862] Q: What are the ingredients in your new shampoo?

[0863] Feedback gathering prompt:

[0864] This product was very helpful.

[0865] Goal setting and progress tracking prompts:

[0866] Complete your grocery list within one month.

[0867] In this way, a system can be realized that not only allows customers to efficiently obtain product information and find the most suitable product, but also makes it easy to provide feedback after purchase, and enables goal setting and progress management.

[0868] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0869] Step 1: Authentication Process

[0870] When a user accesses the system, the terminal displays a login screen. The user enters their user ID and password and presses the login button.

[0871] Input: User ID and password

[0872] Data processing: The user ID and password are sent to the server and compared with the information in the database.

[0873] Output: Authentication success or failure result

[0874] Specific operation: The server compares the authentication information stored in the database with the information entered by the user, and if authentication is successful, starts a user session and sends the following screen to the terminal.

[0875] Step 2: Questions and Answers

[0876] The user inputs a question into the dialogue interface of the terminal and sends it, and the terminal sends the question to the server.

[0877] Input: User question text

[0878] Data processing: The server sends the question text to the generative AI model, which then analyzes the question and generates an appropriate answer.

[0879] Output: Answer text

[0880] Specific operation: The server sends the generated answer to the terminal, and the terminal displays the answer to the user.

[0881] Step 3: Gather feedback

[0882] The user inputs feedback on the response into the dialogue interface and sends it, and the terminal sends the feedback to the server.

[0883] Input: User feedback text

[0884] Data processing: The server stores the feedback as training data for the generative AI model.

[0885] Output: Feedback saving completion message

[0886] What it does: The server stores the feedback data in a database and uses it to improve the generative AI model.

[0887] Step 4: Set goals and track progress

[0888] The user inputs and submits a goal on the goal setting screen, and the device sends the goal to the server.

[0889] Input: User goal text

[0890] Data processing: The server stores the goals in a database and sets reminders and alerts.

[0891] Output: Goal setting confirmation message and reminder or alert setting status

[0892] Specific behavior: The server generates periodic reminders and alerts based on the set goals and notifies the user.

[0893] Step 5: Personalized recommendations

[0894] The user inputs information about the products they sell into the terminal and sends it to the server.

[0895] Input: Product information

[0896] Data processing: The server uses a generative AI model to perform the optimal product recommendation process, providing personalized recommendations based on the user's past purchase history and preferences.

[0897] Output: Recommended products list

[0898] Specific operation: The server provides the generative AI model with information about the products on offer and the user's past data, and then sends the recommendations generated by the model to the terminal, which then displays the recommendation list to the user.

[0899] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0900] The present invention is a system that combines a generative AI model and an emotion engine to analyze questions from users and provide appropriate answers and support based on the user's emotions. A specific embodiment of this system is described below.

[0901] System Overview

[0902] The system includes the following elements:

[0903] 1. User device: The device accessed and operated by the user (e.g., smartphone, tablet, PC, etc.).

[0904] 2. Server: The central system that runs the generative AI model and emotion engine, analyzes user questions and emotions, and provides answers.

[0905] 3. Database: Stores user authentication information, usage history, feedback, goal setting information, and emotional data.

[0906] Specific functions of the system

[0907] Authentication Process

[0908] Terminal: When a user accesses the system, a login screen is displayed. The user enters their user ID and password and presses the login button.

[0909] Server: Receives the authentication information sent from the device and compares it with the information in the database. If authentication is successful, it starts a user session and sends an instruction to the device to display the next screen.

[0910] Question and sentiment analysis

[0911] Terminal: The user accesses a dialogue interface and can enter questions and options for expressing emotions (e.g., emoticons and emotion labels).

[0912] User: Enter the question and feelings for which they need assistance and click the send button.

[0913] Server: Receives the user's question and sends it to the emotion engine to analyze the user's emotion. The analysis result and the question are sent to the generative AI model to generate an appropriate answer.

[0914] Server: Adjusts the tone and content of the response based on the user's sentiment and sends the final response to the device.

[0915] Providing answers and collecting feedback

[0916] Terminal: Displays the answers received from the server to the user, and also displays an interface for the user to enter feedback on the answers.

[0917] User: Enter their thoughts on the answer, any follow-up questions, and their emotional state (e.g., satisfied, dissatisfied, etc.), then click the submit button.

[0918] Server: Receives feedback and emotion data and stores it as training data for the generative AI model.

[0919] Goal setting and progress management

[0920] Terminal: Displays the goal setting screen and provides an interface for the user to input the goals they wish to set.

[0921] User: Enter the goal and emotional state they want to achieve and click the send button.

[0922] Server: Stores the entered goal and emotional state information in a database and sets reminders and alerts for goal achievement. These notifications are adjusted based on the user's emotional state.

[0923] Specific examples

[0924] Authentication Examples

[0925] Device: The user launches a system app and is presented with a login screen.

[0926] User: Enter the user ID "user@example.com" and password "password123" and click the Login button.

[0927] Server: Checks the entered authentication information against the database, and displays the main interface if authentication is successful.

[0928] Specific examples of question and sentiment analysis

[0929] Terminal: The user types "Please tell me about risk management for a new project," selects the emotion emoticon "anxiety," and sends.

[0930] Server: Analyzing the question and sentiment, the generative AI model generates an answer such as, "The basic outline of risk management is as follows..." The answer has a warm tone to allay the user's anxiety.

[0931] Terminal: The answer is displayed to the user.

[0932] Examples of feedback collection

[0933] Device: User types "This answer was very helpful," selects "Satisfied" as the emotional state, and submits.

[0934] Server: Stores feedback and emotional states as training data.

[0935] Specific examples of goal setting and progress management

[0936] Terminal: User types "Complete project risk analysis within one month," selects the emotion emoticon "Motivated," and submits.

[0937] Server: Store your goals and emotional state and set tone-sensitive reminders like, "You're on track!"

[0938] Users: Enter and update their progress regularly.

[0939] Server: Check in on progress and send positive reminders, such as "Great progress!"

[0940] The system helps users acquire new skills and knowledge by implementing the following processes: authentication, question and emotion analysis, answer provision and feedback collection, goal setting and progress management. By combining it with an emotion engine, it can respond to users' emotions and provide more personalized support.

[0941] The processing flow will be explained below.

[0942] Step 1: "Enter your credentials"

[0943] Terminal: Displays the login screen and provides an interface for the user to enter their user ID and password.

[0944] User: Enter your user ID and password and click the Login button.

[0945] Step 2: "Submit authentication information"

[0946] Terminal: Sends the authentication information (user ID and password) entered by the user to the server.

[0947] Step 3: "Verify Authentication"

[0948] Server: Compares the received authentication information with the information in the database and generates an authentication result.

[0949] Server: If authentication is successful, it starts a user session and sends instructions to the terminal to display the next screen. If authentication fails, it returns an error message.

[0950] Step 4: View authentication results

[0951] Terminal: If authentication is successful, display the main interface. If authentication fails, display an error message to the user.

[0952] Step 5: "Enter a question and emotion"

[0953] Terminal: Presents a dialogue interface for the user to enter a question and provides the option to select an emotion emoticon.

[0954] User: Enter a question along with an emotion emoticon or emotion label and click the submit button.

[0955] Step 6: "Submit your questions and feelings"

[0956] Terminal: Sends the questions and emotion data entered by the user to the server.

[0957] Step 7: "Analyze emotions"

[0958] Server: Uses an emotion engine to analyze the received emotion data and identify the user's emotional state.

[0959] Server: Sends the analysis results to the generative AI model.

[0960] Step 8: Parse the question and generate an answer

[0961] Server: The generative AI model generates an answer based on the received question and the results of sentiment analysis.

[0962] Server: Adjust the tone of your response to match the user's emotional state.

[0963] Server: Sends the final answer to the device.

[0964] Step 9: "View Answers"

[0965] Terminal: Displays the final answer received from the server to the user.

[0966] Step 10: "Enter your feedback"

[0967] Terminal: Displays an interface for users to enter feedback on their answers, with the option to also enter their emotional state.

[0968] User: Enter your thoughts, follow-up questions, or emotional state and click the send button.

[0969] Step 11: Send feedback and emotions

[0970] Terminal: Sends the user-entered feedback and emotional state to the server.

[0971] Step 12: Analyze feedback and sentiment

[0972] Server: Analyzes the received feedback and emotional state and stores it as training data for the generative AI model.

[0973] Step 13: Enter your goal settings

[0974] Terminal: Displays the goal setting screen and provides an interface for the user to set goals.

[0975] User: Enter the goal and emotional state they want to achieve and click the send button.

[0976] Step 14: "Preserve your goals and feelings"

[0977] Terminal: Sends the goal information and emotional state entered by the user to the server.

[0978] Server: Stores goal information and emotional state in a database and sets reminders and alerts for goal achievement.

[0979] Step 15: Enter your progress

[0980] Terminal: Displays an interface for the user to enter progress.

[0981] Users: Enter progress and update regularly.

[0982] Step 16: Progress Tracking and Emotion-Based Notifications

[0983] Server: Checks progress and emotional state in a database and generates reminders and alerts.

[0984] Server: Adjusts the content of notifications based on the emotional state and sends them to the user.

[0985] On the device: Displaying received reminders and alerts to the user.

[0986] Example 2

[0987] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0988] While systems using conventional generative AI models can provide answers based on user questions, it is difficult to provide personalized answers that take user emotions into account. It is also necessary to efficiently collect user feedback and use it as training data for generative AI models. Furthermore, it is also necessary to support user goal setting and progress management, and to appropriately send emotional reminders and alerts. It is necessary to solve these problems and improve the user experience.

[0989] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0990] In this invention, the server includes a means for analyzing user questions and emotional information using a generative AI model to generate answers, a means for providing the generated answers to the user and adjusting the tone and content based on the user's emotions, and a means for collecting user feedback and emotional data and saving it as training data for the generative AI model. This enables the provision of personalized answers to user questions that take emotions into account. The server also includes a means for setting user goals and managing progress, and a means for sending reminders and alerts to the user based on their progress and emotional state, thereby helping the user achieve their goals and maintaining their motivation at the appropriate time.

[0991] A "generative AI model" is an artificial intelligence algorithm that generates appropriate answers based on user questions and prompts.

[0992] "Emotional information" refers to the emotional expressions (e.g., emoticons or emotion labels) that users input into the system, and is data that represents the user's emotional state.

[0993] "Feedback" refers to the act and content of a user providing evaluation or additional feedback on an answer.

[0994] "Training data" is a dataset, such as collected feedback or sentiment data, that is used to improve the performance of a generative AI model.

[0995] "Goal setting" refers to the process of setting goals that users want to achieve and the content of those goals.

[0996] "Progress management" is the process of reviewing, tracking, and managing the progress of a user toward the goals they have set.

[0997] A "reminder" is a notification sent from the server to encourage the user to achieve their set goals or progress.

[0998] "Alerts" are important notifications or warnings that occur based on the user's progress or emotional state.

[0999] An "emotion engine" is a technology that analyzes a user's emotions and adjusts the tone and content of the generative AI model's responses based on the results.

[1000] "Tone" refers to the emotional nuance and style of expression of the responses generated.

[1001] MODE FOR CARRYING OUT THE INVENTION

[1002] System Overview

[1003] The present invention is a system that combines a generative AI model and an emotion engine to analyze user questions and provide appropriate answers and support based on the user's emotions. This system is designed to improve the user experience and provide personalized support. A specific embodiment of this system is described below.

[1004] Hardware and software used

[1005] User device: The device accessed and operated by a user (e.g., smartphone, tablet, PC).

[1006] Server: The central system that runs the generative AI model and emotion engine.

[1007] Database: Stores user authentication information, usage history, feedback, goal setting information, and emotional data.

[1008] Generative AI model: An artificial intelligence algorithm that generates answers based on user questions and prompts.

[1009] Emotion engine: A technology that analyzes user emotions and adjusts the tone and content of the generative AI model's responses.

[1010] Example

[1011] 1. User Authentication

[1012] Terminal: When a user accesses the system, a login screen is displayed. The user enters their user ID and password and presses the login button.

[1013] Server: Receives the authentication information sent from the terminal and compares it with the information in the database. If authentication is successful, it sends an instruction to the terminal to display the next screen.

[1014] Example: A user launches a system app, enters the user ID "user@example.com" and password "password123", and clicks the login button. The server checks the authentication information against the database, and if authentication is successful, displays the main interface.

[1015] 2. Question and Sentiment Analysis

[1016] Terminal: The user accesses a dialogue interface and can enter questions and options for expressing emotions (e.g., emoticons and emotion labels).

[1017] User: Enter the question and feelings for which they need assistance and click the send button.

[1018] Server: Receives the user's question and sends it to the emotion engine to analyze the user's emotion. The analysis result and the question are sent to the generative AI model to generate an appropriate answer.

[1019] Server: Adjusts the tone and content of the response based on the user's sentiment and sends the final response to the device.

[1020] Example: A user types, "Please tell me about risk management for a new project," selects the emotion emoticon "anxiety," and submits the question. The server analyzes the question and emotion, and the generative AI model generates an answer: "The basic outline of risk management is as follows..." The answer has a warm tone to alleviate the user's anxiety.

[1021] 3. Providing answers and collecting feedback

[1022] Terminal: Displays the answers received from the server to the user, and also displays an interface for the user to enter feedback on the answers.

[1023] User: Enter their thoughts on the answer, any follow-up questions, or their emotional state (e.g., satisfied, dissatisfied), and click the submit button.

[1024] Server: Receives feedback and emotion data and stores it as training data for the generative AI model.

[1025] Example: A user types "This answer was very helpful," selects "Satisfied" as the emotional state, and submits it. The server saves the feedback and emotional state as training data.

[1026] 4. Goal setting and progress management

[1027] Terminal: Displays the goal setting screen and provides an interface for the user to input the goals they wish to set.

[1028] User: Enter the goal and emotional state they want to achieve and click the send button.

[1029] Server: Stores the entered goal and emotional state information in a database and sets reminders and alerts for goal achievement. These notifications are adjusted based on the user's emotional state.

[1030] Example: A user enters "Complete a risk analysis for the project within one month," selects the emotion emoticon "Motivated," and submits it. The server saves the goal and emotional state, and sets tone-based reminders such as "You're making good progress!" The user periodically enters and updates their progress. The server monitors their progress and sends positive reminders such as "Great progress!"

[1031] The system helps users acquire new skills and knowledge by implementing the following processes: authentication, question and emotion analysis, answer provision and feedback collection, goal setting and progress management. By combining it with an emotion engine, it can respond to users' emotions and provide more personalized support.

[1032] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1033] Step 1:

[1034] Displaying the login screen

[1035] Terminal: When a user accesses the system, a login screen is displayed, providing fields for entering a user ID and password, and a login button.

[1036] Input: User ID, Password

[1037] Output: Sending login information

[1038] Step 2:

[1039] Sending authentication information

[1040] User: Enter your user ID (e.g., "user@example.com") and password (e.g., "password123") and click the Login button.

[1041] Terminal: Sends the entered user ID and password to the server.

[1042] Input: The credentials entered by the user

[1043] Output: Sending authentication information to the server

[1044] Step 3:

[1045] Authentication Check

[1046] Server: Receives the user ID and password sent from the terminal and accesses the database to verify this information.

[1047] Server: If authentication is successful, it starts a user session and sends a signal to the terminal to display the following screen. If authentication fails, it sends an error message to the terminal.

[1048] Input: Authentication information sent from the device

[1049] Output: Authentication result (success or failure)

[1050] Step 4:

[1051] Viewing authentication results

[1052] Terminal: Based on the signal received from the server, if authentication is successful, it displays the main interface, otherwise it displays an error message (e.g. "Authentication failed. Please try again").

[1053] Input: Authentication result sent from the server

[1054] Output: Main interface or error message display

[1055] Step 5:

[1056] Enter your question

[1057] User: Enters a question using the dialogue interface (e.g., "Please tell me about risk management for a new project") and selects an emoticon option (e.g., emoticon "😟" for anxiety).

[1058] Terminal: Provides the user with a question and emotional expression options, and sends this data to the server when the user clicks the send button.

[1059] Input: Questions and sentiment information entered by the user

[1060] Output: Sends questions and emotion information to the server

[1061] Step 6:

[1062] Question and sentiment analysis

[1063] Server: The server receives questions and emotion data from the device and sends them to the emotion engine for analysis. The emotion engine analyzes the user's emotion (e.g., anxiety) and returns the analysis results to the generative AI model.

[1064] Server: Generates and sends a prompt to the generative AI model (e.g., "The user is curious and anxious about risk management for a new project. Please generate an appropriate answer to this question.").

[1065] Input: User question and sentiment information

[1066] Output: Sentiment analysis results and generated prompt sentences

[1067] Step 7:

[1068] Generate answers

[1069] Generative AI model: Generates an answer to a user's question based on the prompt sent (e.g., "A basic overview of risk management is as follows...") The answer incorporates sentiment analysis and adds a gentle tone to allay any anxiety.

[1070] Input: Generated prompt text

[1071] Output: The generated answer

[1072] Step 8:

[1073] Providing answers

[1074] Server: Sends the generated answer to the device.

[1075] Terminal: Display the answer to the user.

[1076] Input: Generated Answer

[1077] Output: Show answer

[1078] Step 9:

[1079] Enter your feedback

[1080] User: Enters their thoughts about the provided answer (e.g., "This answer was very helpful") and emotional state (e.g., "Satisfied"), and clicks the submit button.

[1081] Terminal: Sends input feedback and emotional state to the server.

[1082] Input: Feedback and emotional state entered by the user

[1083] Output: Sending feedback and emotional state to the server

[1084] Step 10:

[1085] Save your feedback

[1086] Server: The feedback and emotion data received from the device is stored in a database and used as training data for the generative AI model.

[1087] Input: Feedback and emotion data sent from the device

[1088] Output: Save to database

[1089] Step 11:

[1090] Enter your goal settings

[1091] User: Uses the goal setting screen to enter a new goal (e.g., "Complete project risk analysis within one month") and emotional state (e.g., "Motivated"), then clicks the submit button.

[1092] Terminal: Sends goals and emotional state to the server.

[1093] Input: Goals and emotional states entered by the user

[1094] Output: Sending goals and emotional states to the server

[1095] Step 12:

[1096] Saving goals and emotional states

[1097] Server: Stores the received goal and emotional state information in a database. Sets reminders and alerts for goal achievement and adjusts them based on the user's emotional state.

[1098] Input: User's goal and emotional state

[1099] Output: Save to database and set reminder

[1100] Step 13:

[1101] Progress management

[1102] Users: Periodically enter and update progress (e.g., "50% of risk analysis is complete").

[1103] Server: Stores the received progress in a database and sends context-appropriate positive reminders and supportive messages (e.g., "Great progress!") to the device.

[1104] On the device: Display reminders and support messages to the user.

[1105] Input: progress

[1106] Output: Saving progress information to a database and displaying messages

[1107] The above is the specific flow of the system's program processing, which provides personalized support to users and enables efficient operation of the system.

[1108] (Application example 2)

[1109] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1110] On existing online shopping sites, responses to user questions are often limited to standard phrases or basic guidance, lacking personalized responses that take into account the individual feelings and circumstances of the user. Furthermore, there is no mechanism to adequately alleviate the uncertainty and anxiety users may feel when choosing a product. As a result, users are experiencing a decline in satisfaction and a decrease in their desire to purchase.

[1111] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1112] In this invention, the server includes a means for analyzing a user's emotions and adjusting the content and tone of answers and advice based on the results, a means for recommending products based on the content of the user's question and the results of the emotion analysis, and a means for analyzing the user's question using a generative AI model and generating an answer, thereby enabling personalized responses that take into account not only the content of the user's question but also their emotions.

[1113] A "generative AI model" is a type of artificial intelligence that generates natural language responses based on given text data, and is a model with advanced language processing capabilities.

[1114] "Analyzing emotions" means analyzing the emotional information contained in the text data collected from the user using specific algorithms or software to determine the user's emotional state.

[1115] "Analyzing a question" means using text analysis techniques to analyze the question text provided by the user and understand its content and purpose.

[1116] "Generating an answer" means automatically generating an appropriate response based on the analyzed question data and providing it to the user.

[1117] "Providing to the user" means displaying the generated answers and recommendations on the user's device.

[1118] "Gathering feedback" means collecting data on opinions and feelings about responses provided by users.

[1119] "Storing as training data" means accumulating collected feedback and other data in a database as training data for improving the performance of generative AI models.

[1120] "Setting goals and managing progress" means managing the progress of the goals set by the user and recording appropriate progress.

[1121] "Send reminders and alerts" means sending timely notifications about the progress of goals set by the user and important events.

[1122] "Adjusting your tone" means changing the wording and presentation of your responses depending on the user's emotional state.

[1123] "Making product recommendations" means selecting and recommending appropriate products based on the user's questions and emotional state.

[1124] System Overview

[1125] The system for implementing this invention consists of a terminal used by the user, a server connected to it, and a database. The user accesses an online shopping site application using a smartphone and inputs a question about a product. This question and the user's emotional information are sent to the server, where it is analyzed and answered.

[1126] Technology used

[1127] Hardware: Smartphones, servers

[1128] Software: Generative AI models (e.g., OpenAI's GPT-3.5), emotion engines (e.g., IBM Watson), databases (e.g., MySQL)

[1129] Question and sentiment analysis

[1130] The device sends the user's question and emotional information to the server, which uses an emotion engine to analyze the user's emotions and passes the results to the generative AI model, which then generates the optimal answer based on the user's question and emotions.

[1131] Specific examples

[1132] The user types, "What size is this shirt?" and selects the emotion emoticon "unsure." The server analyzes the question and emotion, and the generative AI model responds, "Please refer to the shirt size chart. If you're unsure, we recommend you choose your usual size."

[1133] Product recommendation function

[1134] The server makes product recommendations based on the user's questions and sentiment analysis results. A generative AI model selects products based on the user's preferences and sentiment, and provides them to the user's device as a list.

[1135] Specific examples

[1136] The user types "What's your recommendation for a birthday present?", selects the emoticon "Joy," and submits the question. The server analyzes the question and emotion, and the generative AI model responds with "Choose a gift that matches the recipient's tastes! Here are some recommended gifts," making product recommendations in a tone that conveys joy.

[1137] Gathering feedback and learning

[1138] The user's device displays a feedback interface, and the user inputs their thoughts on the answers and recommendations. The server receives this feedback and stores it as training data for the generative AI model, which improves the accuracy of future answers.

[1139] Prompt Sentence Examples

[1140] What would you recommend as a birthday gift? Emotion: Joy. Generate an appropriate answer based on this.

[1141] Goal setting and progress management

[1142] The user's device displays a goal setting screen and the user inputs the goal they want to set. The server stores the input goal and emotional state information in a database and sets reminders and alerts for goal achievement. These notifications are adjusted based on the user's emotional state.

[1143] Specific examples

[1144] A user types "Complete a risk analysis of the project within one month," selects the emotion emoticon "Motivated," and submits the goal. The server saves the goal and emotional state and sets a reminder with a tone, such as "You're on track!"

[1145] In this way, the system provides highly personalized support that responds to the user's individual circumstances and emotions.

[1146] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1147] Step 1:

[1148] When a user launches an online shopping site application on their smartphone, the login screen appears. The user enters their user ID and password and clicks the login button.

[1149] Input: User ID "user@example.com" and password "password123"

[1150] Output: Authentication information sent to the server

[1151] Step 2:

[1152] The server receives the authentication information submitted and checks it against its database. If authentication is successful, it starts a user session and displays the following screen:

[1153] Input: Credentials

[1154] Output: User authentication result and session start instruction

[1155] Step 3:

[1156] User types a question about the product, selects an emotion emoticon, and submits it. Example: "Please tell me about risk management for a new project" (emotion emoticon: "anxiety")

[1157] Input: Question text and emotion emoticons

[1158] Output: The user's question and emotion information are sent to the server.

[1159] Step 4:

[1160] The server receives the user's question and emotional information, sends it to the emotion engine for emotional analysis, and passes the analyzed emotional data to the generative AI model, which then generates the optimal answer to the question.

[1161] Input: Question text and sentiment information

[1162] Output: Parsed sentiment data and generated answers

[1163] Step 5:

[1164] The server receives the generated answers, adjusts the tone and content of the answers based on the user's feelings, and sends the final answers to the user's smartphone.

[1165] Input: Generated answers and sentiment data

[1166] Output: Adjusted answer

[1167] Step 6:

[1168] The device displays the tailored answer to the user, who then enters their feedback or emotional state (e.g., "Satisfied") about the answer and clicks the submit button.

[1169] Input: User feedback and emotional state

[1170] Output: Feedback and emotional state are sent to the server

[1171] Step 7:

[1172] The server stores the received feedback and emotional state in a database as training data for the generative AI model.

[1173] Input: Feedback and emotional state

[1174] Output: Save as training data

[1175] Step 8:

[1176] The user uses the goal setting screen to input the goal and emotional state they want to set (e.g., "Complete the project risk analysis within one month," emotional emoticon: "motivated").

[1177] Input: Target text and emotion emoticons

[1178] Output: Goal and emotion information is sent to the server

[1179] Step 9:

[1180] The server stores the received goal and emotional information in a database and sets reminders and alerts for goal achievement, which are adjusted based on the user's emotional state.

[1181] Input: goal and emotion information

[1182] Output: Instructions for setting reminders and alerts

[1183] Step 10:

[1184] The device periodically prompts the user for progress input and updates, and the server monitors the progress and sends appropriate reminders and alerts (e.g., "Great progress!").

[1185] Input: Progress data

[1186] Output: Reminders and alerts

[1187] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1188] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1189] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1190] [Third embodiment]

[1191] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1192] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1193] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1194] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1195] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1196] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1197] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1198] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1199] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1200] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1201] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1202] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1203] The present invention is a system that utilizes a generative AI model to provide an environment where users can easily ask for advice on anything, anytime. This system begins with user authentication and includes processes such as interactive assistance, providing responses, collecting feedback, goal setting, and progress management. A specific embodiment of this system is described below.

[1204] System Overview

[1205] The system includes the following elements:

[1206] 1. User device: The device accessed and operated by the user (e.g., smartphone, tablet, PC, etc.).

[1207] 2. Server: The central system that runs the generative AI model, analyzes user questions, and provides answers.

[1208] 3. Database: Stores user authentication information, usage history, feedback, and goal setting information.

[1209] Specific functions of the system

[1210] Authentication Process

[1211] Terminal: When a user accesses the system, a login screen is displayed. The user enters their user ID and password and presses the login button.

[1212] Server: Receives the authentication information sent from the device and compares it with the information in the database. If authentication is successful, it starts a user session and sends an instruction to the device to display the next screen.

[1213] Questions and Answers

[1214] Terminal: The user accesses the dialogue interface and enters a question. When the user has finished entering the question, he or she clicks the submit button.

[1215] Server: Receives the user's question and sends it to the generative AI model, which analyzes the question and generates an appropriate answer.

[1216] Server: Sends the generated answer to the user's device.

[1217] Terminal: Display the answer to the user.

[1218] Feedback collection

[1219] Terminal: A feedback interface is displayed where users can enter their thoughts on the answers or ask follow-up questions.

[1220] Server: Receives user-entered feedback and stores it as training data for the generative AI model.

[1221] Goal setting and progress management

[1222] Terminal: Displays the goal setting screen and provides an interface for the user to input the goals they wish to set.

[1223] Server: Stores the entered goals in a database and sets reminders and alerts.

[1224] Terminal: User periodically enters and updates progress.

[1225] Server: Checks progress and sends reminders and alerts accordingly.

[1226] Specific examples

[1227] Authentication Examples

[1228] Device: The user launches the Buddy AI app and the login screen appears.

[1229] User: Enter the user ID "user@example.com" and password "password123" and click the Login button.

[1230] Server: Checks the entered authentication information against the database, and displays the main interface if authentication is successful.

[1231] Specific examples of questions and answers

[1232] Terminal: The user types in "Please tell me about risk management for a new project" and submits.

[1233] Server: Sends questions to the generative AI model and generates answers for risk management.

[1234] Terminal: The answer "The basic outline of risk management is as follows..." is displayed.

[1235] Examples of feedback collection

[1236] Device: User types "This answer was very helpful" and submits.

[1237] Server: Saves the feedback as training data.

[1238] Specific examples of goal setting and progress management

[1239] Terminal: The user types in "Complete the project risk analysis within one month" and submits.

[1240] Server: Save your goals and set weekly progress reminders.

[1241] Users: Enter and update their progress regularly.

[1242] Server: Checks progress and sends necessary reminders and alerts.

[1243] As described above, this system provides an environment in which users can easily acquire new skills and knowledge by implementing the processes of authentication, question and answer, feedback collection, goal setting, and progress management.

[1244] The processing flow will be explained below.

[1245] Step 1: "Enter your credentials"

[1246] Terminal: Displays the login screen and provides an interface for the user to enter their user ID and password.

[1247] User: Enter your user ID and password and click the Login button.

[1248] Step 2: "Submit authentication information"

[1249] Terminal: Sends the authentication information (user ID and password) entered by the user to the server.

[1250] Step 3: "Verify Authentication"

[1251] Server: Compares the received authentication information with the information in the database and generates an authentication result.

[1252] Server: If authentication is successful, it starts a user session and sends instructions to the terminal to display the next screen. If authentication fails, it returns an error message.

[1253] Step 4: View authentication results

[1254] Terminal: If authentication is successful, display the main interface. If authentication fails, display an error message to the user.

[1255] Step 5: "Enter your question"

[1256] Terminal: displays a dialogue interface for users to enter questions.

[1257] User: Type in the question they need help with and click the submit button.

[1258] Step 6: Submit your question

[1259] Terminal: Sends the questions entered by the user to the server.

[1260] Step 7: Parse the Question

[1261] Server: Sends the received question to the generative AI model and analyzes the question.

[1262] Step 8: Generate answers

[1263] Server: The generative AI model analyzes the intent of the question and generates an appropriate answer, taking into account the user's past usage history and characteristics as necessary.

[1264] Server: Sends the generated answer to the device.

[1265] Step 9: "View Answers"

[1266] Terminal: Displays the answer received from the server to the user.

[1267] Step 10: "Enter your feedback"

[1268] Terminal: Displays an interface for users to enter feedback on their answers.

[1269] User: Enter their thoughts on the answer or any follow-up questions and click the submit button.

[1270] Step 11: "Submit Feedback"

[1271] Terminal: Sends the feedback entered by the user to the server.

[1272] Step 12: Analyze feedback

[1273] Server: Analyzes the received feedback and stores it as training data for the generative AI model.

[1274] Step 13: Enter your goal settings

[1275] Terminal: Displays an interface for users to set goals.

[1276] User: Enter the goal they want to achieve and click the submit button.

[1277] Step 14: "Save Goal"

[1278] Terminal: Sends the target information entered by the user to the server.

[1279] Server: Stores goal information in a database and sets reminders and alerts for goal achievement.

[1280] Step 15: Enter your progress

[1281] Terminal: Displays an interface for the user to enter progress.

[1282] Users: Enter progress and update regularly.

[1283] Step 16: "Progress Tracking and Notifications"

[1284] Server: Checks the progress in the database, generates reminders and alerts accordingly, and notifies the user.

[1285] On the device: Displays reminders and alerts received from the server to the user.

[1286] Through these steps, the system helps users acquire new skills and knowledge.

[1287] Example 1

[1288] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1289] In today's information environment, it is important for users to be able to quickly respond to a variety of questions and instantly obtain the information they need. However, conventional systems were unable to provide appropriate answers to users' questions and had difficulty effectively collecting and utilizing user feedback. Furthermore, they lacked functionality for user goal setting and progress management, making it difficult for users to effectively manage their own learning and work progress.

[1290] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1291] In this invention, the server includes means for analyzing questions from users and using a generative AI model to generate answers, means for providing the generated answers to the users, means for collecting user feedback and saving it as learning data for the generative AI model, means for setting user goals and managing progress, means for sending reminders and alerts to the user according to progress, means for verifying authentication information and starting a user session if authentication is successful, and means for sending the authentication result to the user terminal. This allows users to receive prompt and appropriate answers to their questions and further enables them to improve their learning and work efficiency through feedback and progress management.

[1292] A "generative AI model" is an artificial intelligence technology that analyzes questions from users and generates appropriate answers.

[1293] A "means" is a specific method or device for achieving a specific function or action.

[1294] "User" refers to a person who uses the system to enter questions or set goals.

[1295] "Feedback" refers to responses provided by users, such as their impressions of the answers and services, or any follow-up questions.

[1296] "Training data" is data that a generative AI model uses to improve and refine its accuracy.

[1297] "Goal setting" is the process by which a user inputs the goals they want to achieve and manages their progress.

[1298] "Progress management" refers to checking and managing the degree of achievement and progress toward set goals.

[1299] "Reminders" is a feature that sends notifications at specific times or situations to help users remember their set goals and tasks.

[1300] An "alert" is a notification sent to alert or warn the user.

[1301] "Authentication information" refers to information such as user ID and password that a user enters when accessing a system.

[1302] A "user session" refers to the series of operations a user performs from the time they log in to the time they log out of the system.

[1303] A "database" is a system for structuring, storing, and managing data such as authentication information and usage history.

[1304] The present invention is a system that provides an environment where users can easily ask any question at any time. This system utilizes a generative AI model and encompasses processes such as user authentication, interactive assistance, response provision, feedback collection, goal setting and progress management. A specific embodiment of this system is described below.

[1305] System configuration

[1306] The system includes the following elements:

[1307] 1. User device: The device accessed and operated by the user (e.g., smartphone, tablet, PC, etc.).

[1308] 2. Server: The central system that runs the generative AI model, analyzes user questions, and provides answers.

[1309] 3. Database: A system that stores user authentication information, usage history, feedback, and goal setting information.

[1310] Hardware and Software

[1311] User device: Provides the front-end interface that users access. For example, it can be a smartphone application running on iOS or Android, or a web application running on a web browser.

[1312] Server: Cloud-based computing resources for running generative AI models (e.g., GPT-4), such as virtual machines or container services provided by Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure.

[1313] Database: A database system for storing user information and usage history. Examples include Amazon RDS, Google Cloud SQL, and Microsoft SQL Server.

[1314] Specific steps in the process and data handling

[1315] Authentication Process

[1316] When a user accesses the system from a user terminal, a login screen is displayed. The user enters their user ID and password and presses the login button. The server receives the authentication information sent from the terminal and compares it with the information in the database. If authentication is successful, it starts a user session and sends an instruction to the terminal to display the next screen.

[1317] Handling Questions and Answers

[1318] The user accesses the dialogue interface and inputs a question. For example, the user inputs and submits "Please tell me about risk management for a new project." The server receives the user's question and sends it to the generative AI model. The generative AI model analyzes the question and generates an appropriate answer. The server sends the generated answer to the user's device, and the device displays the answer to the user.

[1319] Feedback collection

[1320] A feedback interface is displayed in which the user can enter their thoughts about the answer or any additional questions. For example, they can enter "This answer was very helpful" and submit. The server receives the user's feedback and stores it as training data for the generative AI model.

[1321] Goal setting and progress management

[1322] It displays a goal setting screen and provides an interface for users to input the goals they want to set. For example, a user can input "Complete project risk analysis within one month" and submit it. The server saves the input goals in a database and sets reminders and alerts. Users can periodically input and update their progress, and the server will check the progress and send reminders and alerts as appropriate.

[1323] This allows users to efficiently acquire their own skills and knowledge. The system provides interactive support to users, helping them manage their learning and work progress through feedback and goal setting, thereby enabling users to effectively manage themselves.

[1324] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1325] Authentication Process

[1326] Step 1:

[1327] A user accesses the system using a user terminal, which displays the login screen.

[1328] Input: Enter your user ID and password.

[1329] Output: The input information is displayed on the login screen.

[1330] Step 2:

[1331] The user presses the login button.

[1332] Input: User ID and password.

[1333] Output: The input information is sent to the server.

[1334] Step 3:

[1335] The server receives the authentication information sent from the terminal and compares it with the authentication information stored in the database.

[1336] Input: User ID and password.

[1337] Data processing: Retrieve user information from the database and compare it with the received authentication information.

[1338] Output: Authentication result (success or failure).

[1339] Step 4:

[1340] The server sends the authentication result to the terminal.

[1341] Input: Authentication result.

[1342] Output: The authentication result is sent to the terminal.

[1343] Step 5:

[1344] The device will display the following screen based on the authentication result: the main interface if successful, or an error message if unsuccessful.

[1345] Input: Authentication result.

[1346] Output: The main interface or an error message is displayed.

[1347] Question and Answer Process

[1348] Step 1:

[1349] The user accesses the dialogue interface and enters a question.

[1350] Input: Question (e.g. "Please tell me about risk management for a new project").

[1351] Output: The entered question is displayed on the screen.

[1352] Step 2:

[1353] The user presses the send button.

[1354] Input: Question.

[1355] Output: The question is sent to the server.

[1356] Step 3:

[1357] The server receives the user's query.

[1358] Input: Question.

[1359] Output: The question is processed as a prompt that is sent to the generative AI model.

[1360] Step 4:

[1361] A generative AI model analyzes the question and generates an appropriate answer.

[1362] Input: The question as a prompt.

[1363] Data computation: Analyzing questions and generating answers based on relevant information.

[1364] Output: The generated answer.

[1365] Step 5:

[1366] The server receives the generated response and transmits it to the user terminal.

[1367] Input: The generated answer.

[1368] Output: The answer is sent to the user's device.

[1369] Step 6:

[1370] The device displays the answer to the user.

[1371] Input:Answer.

[1372] Output: The answer is displayed on the screen.

[1373] Feedback Collection Process

[1374] Step 1:

[1375] Users can enter their thoughts and follow-up questions into the feedback interface.

[1376] Input: Feedback (e.g., "This answer was very helpful").

[1377] Output: Feedback is displayed on the screen.

[1378] Step 2:

[1379] The user presses the send button.

[1380] Input: Feedback content.

[1381] Output: Feedback is sent to the server.

[1382] Step 3:

[1383] The server receives the feedback and stores it as training data for the generative AI model.

[1384] Input: Feedback content.

[1385] Data processing: The feedback content is organized and saved in a database as learning data.

[1386] Output: The feedback is stored in a database.

[1387] Goal setting and progress management process

[1388] Step 1:

[1389] The user accesses the goal setting screen and enters the goal.

[1390] Input: Objective (e.g., "Complete project risk analysis within one month").

[1391] Output: The entered goal is displayed on the screen.

[1392] Step 2:

[1393] The user presses the send button.

[1394] Input: Goal content.

[1395] Output: The goal is sent to the server.

[1396] Step 3:

[1397] The server receives the target content and stores it in a database.

[1398] Input: Goal content.

[1399] Data processing: Organize the target content and save it in a database.

[1400] Output: The goal is saved in the database.

[1401] Step 4:

[1402] The server sets reminders and alerts and sends them to the user accordingly.

[1403] Inputs: Progress data, alert conditions.

[1404] Output: Reminders and alerts are set and sent to the user's device.

[1405] Step 5:

[1406] Users periodically enter and update their progress.

[1407] Input: Progress data (e.g., "Risk analysis 50% complete").

[1408] Output: Progress is updated to the database.

[1409] Step 6:

[1410] The server receives progress data and sends reminders and alerts as needed.

[1411] Input: Progress data.

[1412] Output: Reminders and alerts are sent to the user's device as appropriate.

[1413] The above are the specific processing steps and details of the program for this system.

[1414] (Application example 1)

[1415] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1416] In traditional shopping experiences, customers have to put in a lot of effort to obtain product information and find the perfect product. It's also difficult for customers to provide feedback after a purchase, set goals, or track progress. It's also difficult for customers to receive personalized recommendations based on their past purchasing history and preferences. There's a need for a system that can improve this situation and provide a more efficient and personalized shopping experience.

[1417] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1418] In this invention, the server includes: means for analyzing customer inquiries using a generative AI model and generating responses; means for providing the generated responses to the customer; means for collecting customer opinions and saving them as training data for the generative AI model; means for setting customer goals and managing progress; means for sending reminders and alerts to the customer based on their progress; means for the customer to input information about products available and for the generative AI model to recommend optimal products; and means for providing a personalized shopping experience based on the customer's past purchase history and preferences. This not only enables customers to efficiently obtain product information and find optimal products, but also facilitates post-purchase feedback, goal setting, and progress management. Furthermore, customers can receive personalized suggestions based on their past purchase history and preferences.

[1419] A "generative AI model" is a program that uses artificial intelligence algorithms to analyze input data and generate answers or suggestions in natural language.

[1420] "Customer" refers to a user who uses the system to obtain information about products or purchase products.

[1421] An "inquiry" refers to a question or request that a customer makes to the system.

[1422] A "response" is an answer or suggestion that a generative AI model generates in response to a customer inquiry.

[1423] "Opinions" refers to the feedback or thoughts that customers provide about a response.

[1424] "Training data" refers to the dataset used by a generative AI model to learn, and may include information such as opinions.

[1425] "Goal setting" refers to the act of registering specific goals and tasks that a customer wants to achieve in the system.

[1426] "Progress management" refers to the process by which a system tracks and manages the degree of achievement and progress of set goals.

[1427] "Reminder" is a function that allows the system to notify and alert you about set goals.

[1428] An "alert" is a notification that the system sends to the customer based on a specific condition or progress.

[1429] "Products handled" refers to all products offered in physical stores and online shops.

[1430] "Recommendation" refers to the act of a generative AI model suggesting the most appropriate product or service based on customer inquiries and past data.

[1431] "Purchase history" refers to a record of a customer's past purchases of products or services.

[1432] A "personalized shopping experience" refers to the entire purchasing process being individually optimized based on the customer's past purchasing history and preferences.

[1433] To implement this invention, it is necessary to build a system that uses a server, a terminal, and a generative AI model.

[1434] First, users access the system through a device such as a smartphone or smart glasses. A dedicated application called "Smart Shopping Assistant" is installed on the device. Using this application, users can log in to the system and use various functions.

[1435] The server uses a generative AI model to analyze user queries and generate a response. For example, when a user uses a smartphone to ask, "Please tell me about the ingredients in this new shampoo," the server sends this query to the generative AI model. The generative AI model uses its internal algorithm to analyze the query and generate an appropriate response. The generated response is then sent from the server to the device and displayed to the user. This response process uses advanced generative AI models such as OpenAI's GPT-3.5.

[1436] Users can also provide feedback after their purchase. For example, if a user sends feedback such as "This product was very useful," the server stores this information and uses it as training data for the generative AI model, which continuously improves the model's accuracy.

[1437] Furthermore, users can set purchasing goals and manage their progress. For example, if a user sets a goal of "completing a grocery list within a month," the server stores this information and sends periodic reminders and alerts to help users achieve their goals.

[1438] The system can also provide a personalized shopping experience based on a customer's past purchase history and preferences. For example, if a customer has previously purchased a particular brand of shampoo, the generative AI model can use that information to recommend new products from that brand, helping customers find the perfect product efficiently.

[1439] The hardware used includes cloud servers (e.g., AWS or Google Cloud), users' smartphones, and smart glasses. The software used includes OpenAI's GPT-3.5, server-side programming using Flask, and a database management system using PostgreSQL.

[1440] Below are some examples of prompt sentences.

[1441] Prompt text during the authentication process:

[1442] Please log in with your user ID "user@example.com" and password "password123".

[1443] Prompt sentences in queries and responses:

[1444] Q: What are the ingredients in your new shampoo?

[1445] Feedback gathering prompt:

[1446] This product was very helpful.

[1447] Goal setting and progress tracking prompts:

[1448] Complete your grocery list within one month.

[1449] In this way, a system can be realized that not only allows customers to efficiently obtain product information and find the most suitable product, but also makes it easy to provide feedback after purchase, and enables goal setting and progress management.

[1450] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1451] Step 1: Authentication Process

[1452] When a user accesses the system, the terminal displays a login screen. The user enters their user ID and password and presses the login button.

[1453] Input: User ID and password

[1454] Data processing: The user ID and password are sent to the server and compared with the information in the database.

[1455] Output: Authentication success or failure result

[1456] Specific operation: The server compares the authentication information stored in the database with the information entered by the user, and if authentication is successful, starts a user session and sends the following screen to the terminal.

[1457] Step 2: Questions and Answers

[1458] The user inputs a question into the dialogue interface of the terminal and sends it, and the terminal sends the question to the server.

[1459] Input: User question text

[1460] Data processing: The server sends the question text to the generative AI model, which then analyzes the question and generates an appropriate answer.

[1461] Output: Answer text

[1462] Specific operation: The server sends the generated answer to the terminal, and the terminal displays the answer to the user.

[1463] Step 3: Gather feedback

[1464] The user inputs feedback on the response into the dialogue interface and sends it, and the terminal sends the feedback to the server.

[1465] Input: User feedback text

[1466] Data processing: The server stores the feedback as training data for the generative AI model.

[1467] Output: Feedback saving completion message

[1468] What it does: The server stores the feedback data in a database and uses it to improve the generative AI model.

[1469] Step 4: Set goals and track progress

[1470] The user inputs and submits a goal on the goal setting screen, and the device sends the goal to the server.

[1471] Input: User goal text

[1472] Data processing: The server stores the goals in a database and sets reminders and alerts.

[1473] Output: Goal setting confirmation message and reminder or alert setting status

[1474] Specific behavior: The server generates periodic reminders and alerts based on the set goals and notifies the user.

[1475] Step 5: Personalized recommendations

[1476] The user inputs information about the products they sell into the terminal and sends it to the server.

[1477] Input: Product information

[1478] Data processing: The server uses a generative AI model to perform the optimal product recommendation process, providing personalized recommendations based on the user's past purchase history and preferences.

[1479] Output: Recommended products list

[1480] Specific operation: The server provides the generative AI model with information about the products on offer and the user's past data, and then sends the recommendations generated by the model to the terminal, which then displays the recommendation list to the user.

[1481] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1482] The present invention is a system that combines a generative AI model and an emotion engine to analyze questions from users and provide appropriate answers and support based on the user's emotions. A specific embodiment of this system is described below.

[1483] System Overview

[1484] The system includes the following elements:

[1485] 1. User device: The device accessed and operated by the user (e.g., smartphone, tablet, PC, etc.).

[1486] 2. Server: The central system that runs the generative AI model and emotion engine, analyzes user questions and emotions, and provides answers.

[1487] 3. Database: Stores user authentication information, usage history, feedback, goal setting information, and emotional data.

[1488] Specific functions of the system

[1489] Authentication Process

[1490] Terminal: When a user accesses the system, a login screen is displayed. The user enters their user ID and password and presses the login button.

[1491] Server: Receives the authentication information sent from the device and compares it with the information in the database. If authentication is successful, it starts a user session and sends an instruction to the device to display the next screen.

[1492] Question and sentiment analysis

[1493] Terminal: The user accesses a dialogue interface and can enter questions and options for expressing emotions (e.g., emoticons and emotion labels).

[1494] User: Enter the question and feelings for which they need assistance and click the send button.

[1495] Server: Receives the user's question and sends it to the emotion engine to analyze the user's emotion. The analysis result and the question are sent to the generative AI model to generate an appropriate answer.

[1496] Server: Adjusts the tone and content of the response based on the user's sentiment and sends the final response to the device.

[1497] Providing answers and collecting feedback

[1498] Terminal: Displays the answers received from the server to the user, and also displays an interface for the user to enter feedback on the answers.

[1499] User: Enter their thoughts on the answer, any follow-up questions, and their emotional state (e.g., satisfied, dissatisfied, etc.), then click the submit button.

[1500] Server: Receives feedback and emotion data and stores it as training data for the generative AI model.

[1501] Goal setting and progress management

[1502] Terminal: Displays the goal setting screen and provides an interface for the user to input the goals they wish to set.

[1503] User: Enter the goal and emotional state they want to achieve and click the send button.

[1504] Server: Stores the entered goal and emotional state information in a database and sets reminders and alerts for goal achievement. These notifications are adjusted based on the user's emotional state.

[1505] Specific examples

[1506] Authentication Examples

[1507] Device: The user launches a system app and is presented with a login screen.

[1508] User: Enter the user ID "user@example.com" and password "password123" and click the Login button.

[1509] Server: Checks the entered authentication information against the database, and displays the main interface if authentication is successful.

[1510] Specific examples of question and sentiment analysis

[1511] Terminal: The user types "Please tell me about risk management for a new project," selects the emotion emoticon "anxiety," and sends.

[1512] Server: Analyzing the question and sentiment, the generative AI model generates an answer such as, "The basic outline of risk management is as follows..." The answer has a warm tone to allay the user's anxiety.

[1513] Terminal: The answer is displayed to the user.

[1514] Examples of feedback collection

[1515] Device: User types "This answer was very helpful," selects "Satisfied" as the emotional state, and submits.

[1516] Server: Stores feedback and emotional states as training data.

[1517] Specific examples of goal setting and progress management

[1518] Terminal: User types "Complete project risk analysis within one month," selects the emotion emoticon "Motivated," and submits.

[1519] Server: Store your goals and emotional state and set tone-sensitive reminders like, "You're on track!"

[1520] Users: Enter and update their progress regularly.

[1521] Server: Check in on progress and send positive reminders, such as "Great progress!"

[1522] The system helps users acquire new skills and knowledge by implementing the following processes: authentication, question and emotion analysis, answer provision and feedback collection, goal setting and progress management. By combining it with an emotion engine, it can respond to users' emotions and provide more personalized support.

[1523] The processing flow will be explained below.

[1524] Step 1: "Enter your credentials"

[1525] Terminal: Displays the login screen and provides an interface for the user to enter their user ID and password.

[1526] User: Enter your user ID and password and click the Login button.

[1527] Step 2: "Submit authentication information"

[1528] Terminal: Sends the authentication information (user ID and password) entered by the user to the server.

[1529] Step 3: "Verify Authentication"

[1530] Server: Compares the received authentication information with the information in the database and generates an authentication result.

[1531] Server: If authentication is successful, it starts a user session and sends instructions to the terminal to display the next screen. If authentication fails, it returns an error message.

[1532] Step 4: View authentication results

[1533] Terminal: If authentication is successful, display the main interface. If authentication fails, display an error message to the user.

[1534] Step 5: "Enter a question and emotion"

[1535] Terminal: Presents a dialogue interface for the user to enter a question and provides the option to select an emotion emoticon.

[1536] User: Enter a question along with an emotion emoticon or emotion label and click the submit button.

[1537] Step 6: "Submit your questions and feelings"

[1538] Terminal: Sends the questions and emotion data entered by the user to the server.

[1539] Step 7: "Analyze emotions"

[1540] Server: Uses an emotion engine to analyze the received emotion data and identify the user's emotional state.

[1541] Server: Sends the analysis results to the generative AI model.

[1542] Step 8: Parse the question and generate an answer

[1543] Server: The generative AI model generates an answer based on the received question and the results of sentiment analysis.

[1544] Server: Adjust the tone of your response to match the user's emotional state.

[1545] Server: Sends the final answer to the device.

[1546] Step 9: "View Answers"

[1547] Terminal: Displays the final answer received from the server to the user.

[1548] Step 10: "Enter your feedback"

[1549] Terminal: Displays an interface for users to enter feedback on their answers, with the option to also enter their emotional state.

[1550] User: Enter your thoughts, follow-up questions, or emotional state and click the send button.

[1551] Step 11: Send feedback and emotions

[1552] Terminal: Sends the user-entered feedback and emotional state to the server.

[1553] Step 12: Analyze feedback and sentiment

[1554] Server: Analyzes the received feedback and emotional state and stores it as training data for the generative AI model.

[1555] Step 13: Enter your goal settings

[1556] Terminal: Displays the goal setting screen and provides an interface for the user to set goals.

[1557] User: Enter the goal and emotional state they want to achieve and click the send button.

[1558] Step 14: "Preserve your goals and feelings"

[1559] Terminal: Sends the goal information and emotional state entered by the user to the server.

[1560] Server: Stores goal information and emotional state in a database and sets reminders and alerts for goal achievement.

[1561] Step 15: Enter your progress

[1562] Terminal: Displays an interface for the user to enter progress.

[1563] Users: Enter progress and update regularly.

[1564] Step 16: Progress Tracking and Emotion-Based Notifications

[1565] Server: Checks progress and emotional state in a database and generates reminders and alerts.

[1566] Server: Adjusts the content of notifications based on the emotional state and sends them to the user.

[1567] On the device: Displaying received reminders and alerts to the user.

[1568] Example 2

[1569] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1570] While systems using conventional generative AI models can provide answers based on user questions, it is difficult to provide personalized answers that take user emotions into account. It is also necessary to efficiently collect user feedback and use it as training data for generative AI models. Furthermore, it is also necessary to support user goal setting and progress management, and to appropriately send emotional reminders and alerts. It is necessary to solve these problems and improve the user experience.

[1571] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1572] In this invention, the server includes a means for analyzing user questions and emotional information using a generative AI model to generate answers, a means for providing the generated answers to the user and adjusting the tone and content based on the user's emotions, and a means for collecting user feedback and emotional data and saving it as training data for the generative AI model. This enables the provision of personalized answers to user questions that take emotions into account. The server also includes a means for setting user goals and managing progress, and a means for sending reminders and alerts to the user based on their progress and emotional state, thereby helping the user achieve their goals and maintaining their motivation at the appropriate time.

[1573] A "generative AI model" is an artificial intelligence algorithm that generates appropriate answers based on user questions and prompts.

[1574] "Emotional information" refers to the emotional expressions (e.g., emoticons or emotion labels) that users input into the system, and is data that represents the user's emotional state.

[1575] "Feedback" refers to the act and content of a user providing evaluation or additional feedback on an answer.

[1576] "Training data" is a dataset, such as collected feedback or sentiment data, that is used to improve the performance of a generative AI model.

[1577] "Goal setting" refers to the process of setting goals that users want to achieve and the content of those goals.

[1578] "Progress management" is the process of reviewing, tracking, and managing the progress of a user toward the goals they have set.

[1579] A "reminder" is a notification sent from the server to encourage the user to achieve their set goals or progress.

[1580] "Alerts" are important notifications or warnings that occur based on the user's progress or emotional state.

[1581] An "emotion engine" is a technology that analyzes a user's emotions and adjusts the tone and content of the generative AI model's responses based on the results.

[1582] "Tone" refers to the emotional nuance and style of expression of the responses generated.

[1583] MODE FOR CARRYING OUT THE INVENTION

[1584] System Overview

[1585] The present invention is a system that combines a generative AI model and an emotion engine to analyze user questions and provide appropriate answers and support based on the user's emotions. This system is designed to improve the user experience and provide personalized support. A specific embodiment of this system is described below.

[1586] Hardware and software used

[1587] User device: The device accessed and operated by a user (e.g., smartphone, tablet, PC).

[1588] Server: The central system that runs the generative AI model and emotion engine.

[1589] Database: Stores user authentication information, usage history, feedback, goal setting information, and emotional data.

[1590] Generative AI model: An artificial intelligence algorithm that generates answers based on user questions and prompts.

[1591] Emotion engine: A technology that analyzes user emotions and adjusts the tone and content of the generative AI model's responses.

[1592] Example

[1593] 1. User Authentication

[1594] Terminal: When a user accesses the system, a login screen is displayed. The user enters their user ID and password and presses the login button.

[1595] Server: Receives the authentication information sent from the terminal and compares it with the information in the database. If authentication is successful, it sends an instruction to the terminal to display the next screen.

[1596] Example: A user launches a system app, enters the user ID "user@example.com" and password "password123", and clicks the login button. The server checks the authentication information against the database, and if authentication is successful, displays the main interface.

[1597] 2. Question and Sentiment Analysis

[1598] Terminal: The user accesses a dialogue interface and can enter questions and options for expressing emotions (e.g., emoticons and emotion labels).

[1599] User: Enter the question and feelings for which they need assistance and click the send button.

[1600] Server: Receives the user's question and sends it to the emotion engine to analyze the user's emotion. The analysis result and the question are sent to the generative AI model to generate an appropriate answer.

[1601] Server: Adjusts the tone and content of the response based on the user's sentiment and sends the final response to the device.

[1602] Example: A user types, "Please tell me about risk management for a new project," selects the emotion emoticon "anxiety," and submits the question. The server analyzes the question and emotion, and the generative AI model generates an answer: "The basic outline of risk management is as follows..." The answer has a warm tone to alleviate the user's anxiety.

[1603] 3. Providing answers and collecting feedback

[1604] Terminal: Displays the answers received from the server to the user, and also displays an interface for the user to enter feedback on the answers.

[1605] User: Enter their thoughts on the answer, any follow-up questions, or their emotional state (e.g., satisfied, dissatisfied), and click the submit button.

[1606] Server: Receives feedback and emotion data and stores it as training data for the generative AI model.

[1607] Example: A user types "This answer was very helpful," selects "Satisfied" as the emotional state, and submits it. The server saves the feedback and emotional state as training data.

[1608] 4. Goal setting and progress management

[1609] Terminal: Displays the goal setting screen and provides an interface for the user to input the goals they wish to set.

[1610] User: Enter the goal and emotional state they want to achieve and click the send button.

[1611] Server: Stores the entered goal and emotional state information in a database and sets reminders and alerts for goal achievement. These notifications are adjusted based on the user's emotional state.

[1612] Example: A user enters "Complete a risk analysis for the project within one month," selects the emotion emoticon "Motivated," and submits it. The server saves the goal and emotional state, and sets tone-based reminders such as "You're making good progress!" The user periodically enters and updates their progress. The server monitors their progress and sends positive reminders such as "Great progress!"

[1613] The system helps users acquire new skills and knowledge by implementing the following processes: authentication, question and emotion analysis, answer provision and feedback collection, goal setting and progress management. By combining it with an emotion engine, it can respond to users' emotions and provide more personalized support.

[1614] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1615] Step 1:

[1616] Displaying the login screen

[1617] Terminal: When a user accesses the system, a login screen is displayed, providing fields for entering a user ID and password, and a login button.

[1618] Input: User ID, Password

[1619] Output: Sending login information

[1620] Step 2:

[1621] Sending authentication information

[1622] User: Enter your user ID (e.g., "user@example.com") and password (e.g., "password123") and click the Login button.

[1623] Terminal: Sends the entered user ID and password to the server.

[1624] Input: The credentials entered by the user

[1625] Output: Sending authentication information to the server

[1626] Step 3:

[1627] Authentication Check

[1628] Server: Receives the user ID and password sent from the terminal and accesses the database to verify this information.

[1629] Server: If authentication is successful, it starts a user session and sends a signal to the terminal to display the following screen. If authentication fails, it sends an error message to the terminal.

[1630] Input: Authentication information sent from the device

[1631] Output: Authentication result (success or failure)

[1632] Step 4:

[1633] Viewing authentication results

[1634] Terminal: Based on the signal received from the server, if authentication is successful, it displays the main interface, otherwise it displays an error message (e.g. "Authentication failed. Please try again").

[1635] Input: Authentication result sent from the server

[1636] Output: Main interface or error message display

[1637] Step 5:

[1638] Enter your question

[1639] User: Enters a question using the dialogue interface (e.g., "Please tell me about risk management for a new project") and selects an emoticon option (e.g., emoticon "😟" for anxiety).

[1640] Terminal: Provides the user with a question and emotional expression options, and sends this data to the server when the user clicks the send button.

[1641] Input: Questions and sentiment information entered by the user

[1642] Output: Sends questions and emotion information to the server

[1643] Step 6:

[1644] Question and sentiment analysis

[1645] Server: The server receives questions and emotion data from the device and sends them to the emotion engine for analysis. The emotion engine analyzes the user's emotion (e.g., anxiety) and returns the analysis results to the generative AI model.

[1646] Server: Generates and sends a prompt to the generative AI model (e.g., "The user is curious and anxious about risk management for a new project. Please generate an appropriate answer to this question.").

[1647] Input: User question and sentiment information

[1648] Output: Sentiment analysis results and generated prompt sentences

[1649] Step 7:

[1650] Generate answers

[1651] Generative AI model: Generates an answer to a user's question based on the prompt sent (e.g., "A basic overview of risk management is as follows...") The answer incorporates sentiment analysis and adds a gentle tone to allay any anxiety.

[1652] Input: Generated prompt text

[1653] Output: The generated answer

[1654] Step 8:

[1655] Providing answers

[1656] Server: Sends the generated answer to the device.

[1657] Terminal: Display the answer to the user.

[1658] Input: Generated Answer

[1659] Output: Show answer

[1660] Step 9:

[1661] Enter your feedback

[1662] User: Enters their thoughts about the provided answer (e.g., "This answer was very helpful") and emotional state (e.g., "Satisfied"), and clicks the submit button.

[1663] Terminal: Sends input feedback and emotional state to the server.

[1664] Input: Feedback and emotional state entered by the user

[1665] Output: Sending feedback and emotional state to the server

[1666] Step 10:

[1667] Save your feedback

[1668] Server: The feedback and emotion data received from the device is stored in a database and used as training data for the generative AI model.

[1669] Input: Feedback and emotion data sent from the device

[1670] Output: Save to database

[1671] Step 11:

[1672] Enter your goal settings

[1673] User: Uses the goal setting screen to enter a new goal (e.g., "Complete project risk analysis within one month") and emotional state (e.g., "Motivated"), then clicks the submit button.

[1674] Terminal: Sends goals and emotional state to the server.

[1675] Input: Goals and emotional states entered by the user

[1676] Output: Sending goals and emotional states to the server

[1677] Step 12:

[1678] Saving goals and emotional states

[1679] Server: Stores the received goal and emotional state information in a database. Sets reminders and alerts for goal achievement and adjusts them based on the user's emotional state.

[1680] Input: User's goal and emotional state

[1681] Output: Save to database and set reminder

[1682] Step 13:

[1683] Progress management

[1684] Users: Periodically enter and update progress (e.g., "50% of risk analysis is complete").

[1685] Server: Stores the received progress in a database and sends context-appropriate positive reminders and supportive messages (e.g., "Great progress!") to the device.

[1686] On the device: Display reminders and support messages to the user.

[1687] Input: progress

[1688] Output: Saving progress information to a database and displaying messages

[1689] The above is the specific flow of the system's program processing, which provides personalized support to users and enables efficient operation of the system.

[1690] (Application example 2)

[1691] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1692] On existing online shopping sites, responses to user questions are often limited to standard phrases or basic guidance, lacking personalized responses that take into account the individual feelings and circumstances of the user. Furthermore, there is no mechanism to adequately alleviate the uncertainty and anxiety users may feel when choosing a product. As a result, users are experiencing a decline in satisfaction and a decrease in their desire to purchase.

[1693] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1694] In this invention, the server includes a means for analyzing a user's emotions and adjusting the content and tone of answers and advice based on the results, a means for recommending products based on the content of the user's question and the results of the emotion analysis, and a means for analyzing the user's question using a generative AI model and generating an answer, thereby enabling personalized responses that take into account not only the content of the user's question but also their emotions.

[1695] A "generative AI model" is a type of artificial intelligence that generates natural language responses based on given text data, and is a model with advanced language processing capabilities.

[1696] "Analyzing emotions" means analyzing the emotional information contained in the text data collected from the user using specific algorithms or software to determine the user's emotional state.

[1697] "Analyzing a question" means using text analysis techniques to analyze the question text provided by the user and understand its content and purpose.

[1698] "Generating an answer" means automatically generating an appropriate response based on the analyzed question data and providing it to the user.

[1699] "Providing to the user" means displaying the generated answers and recommendations on the user's device.

[1700] "Gathering feedback" means collecting data on opinions and feelings about responses provided by users.

[1701] "Storing as training data" means accumulating collected feedback and other data in a database as training data for improving the performance of generative AI models.

[1702] "Setting goals and managing progress" means managing the progress of the goals set by the user and recording appropriate progress.

[1703] "Send reminders and alerts" means sending timely notifications about the progress of goals set by the user and important events.

[1704] "Adjusting your tone" means changing the wording and presentation of your responses depending on the user's emotional state.

[1705] "Making product recommendations" means selecting and recommending appropriate products based on the user's questions and emotional state.

[1706] System Overview

[1707] The system for implementing this invention consists of a terminal used by the user, a server connected to it, and a database. The user accesses an online shopping site application using a smartphone and inputs a question about a product. This question and the user's emotional information are sent to the server, where it is analyzed and answered.

[1708] Technology used

[1709] Hardware: Smartphones, servers

[1710] Software: Generative AI models (e.g., OpenAI's GPT-3.5), emotion engines (e.g., IBM Watson), databases (e.g., MySQL)

[1711] Question and sentiment analysis

[1712] The device sends the user's question and emotional information to the server, which uses an emotion engine to analyze the user's emotions and passes the results to the generative AI model, which then generates the optimal answer based on the user's question and emotions.

[1713] Specific examples

[1714] The user types, "What size is this shirt?" and selects the emotion emoticon "unsure." The server analyzes the question and emotion, and the generative AI model responds, "Please refer to the shirt size chart. If you're unsure, we recommend you choose your usual size."

[1715] Product recommendation function

[1716] The server makes product recommendations based on the user's questions and sentiment analysis results. A generative AI model selects products based on the user's preferences and sentiment, and provides them to the user's device as a list.

[1717] Specific examples

[1718] The user types "What's your recommendation for a birthday present?", selects the emoticon "Joy," and submits the question. The server analyzes the question and emotion, and the generative AI model responds with "Choose a gift that matches the recipient's tastes! Here are some recommended gifts," making product recommendations in a tone that conveys joy.

[1719] Gathering feedback and learning

[1720] The user's device displays a feedback interface, and the user inputs their thoughts on the answers and recommendations. The server receives this feedback and stores it as training data for the generative AI model, which improves the accuracy of future answers.

[1721] Prompt Sentence Examples

[1722] What would you recommend as a birthday gift? Emotion: Joy. Generate an appropriate answer based on this.

[1723] Goal setting and progress management

[1724] The user's device displays a goal setting screen and the user inputs the goal they want to set. The server stores the input goal and emotional state information in a database and sets reminders and alerts for goal achievement. These notifications are adjusted based on the user's emotional state.

[1725] Specific examples

[1726] A user types "Complete a risk analysis of the project within one month," selects the emotion emoticon "Motivated," and submits the goal. The server saves the goal and emotional state and sets a reminder with a tone, such as "You're on track!"

[1727] In this way, the system provides highly personalized support that responds to the user's individual circumstances and emotions.

[1728] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1729] Step 1:

[1730] When a user launches an online shopping site application on their smartphone, the login screen appears. The user enters their user ID and password and clicks the login button.

[1731] Input: User ID "user@example.com" and password "password123"

[1732] Output: Authentication information sent to the server

[1733] Step 2:

[1734] The server receives the authentication information submitted and checks it against its database. If authentication is successful, it starts a user session and displays the following screen:

[1735] Input: Credentials

[1736] Output: User authentication result and session start instruction

[1737] Step 3:

[1738] User types a question about the product, selects an emotion emoticon, and submits it. Example: "Please tell me about risk management for a new project" (emotion emoticon: "anxiety")

[1739] Input: Question text and emotion emoticons

[1740] Output: The user's question and emotion information are sent to the server.

[1741] Step 4:

[1742] The server receives the user's question and emotional information, sends it to the emotion engine for emotional analysis, and passes the analyzed emotional data to the generative AI model, which then generates the optimal answer to the question.

[1743] Input: Question text and sentiment information

[1744] Output: Parsed sentiment data and generated answers

[1745] Step 5:

[1746] The server receives the generated answers, adjusts the tone and content of the answers based on the user's feelings, and sends the final answers to the user's smartphone.

[1747] Input: Generated answers and sentiment data

[1748] Output: Adjusted answer

[1749] Step 6:

[1750] The device displays the tailored answer to the user, who then enters their feedback or emotional state (e.g., "Satisfied") about the answer and clicks the submit button.

[1751] Input: User feedback and emotional state

[1752] Output: Feedback and emotional state are sent to the server

[1753] Step 7:

[1754] The server stores the received feedback and emotional state in a database as training data for the generative AI model.

[1755] Input: Feedback and emotional state

[1756] Output: Save as training data

[1757] Step 8:

[1758] The user uses the goal setting screen to input the goal and emotional state they want to set (e.g., "Complete the project risk analysis within one month," emotional emoticon: "motivated").

[1759] Input: Target text and emotion emoticons

[1760] Output: Goal and emotion information is sent to the server

[1761] Step 9:

[1762] The server stores the received goal and emotional information in a database and sets reminders and alerts for goal achievement, which are adjusted based on the user's emotional state.

[1763] Input: goal and emotion information

[1764] Output: Instructions for setting reminders and alerts

[1765] Step 10:

[1766] The device periodically prompts the user for progress input and updates, and the server monitors the progress and sends appropriate reminders and alerts (e.g., "Great progress!").

[1767] Input: Progress data

[1768] Output: Reminders and alerts

[1769] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1770] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1771] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1772] [Fourth embodiment]

[1773] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1774] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1775] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1776] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1777] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1778] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1779] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1780] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1781] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1782] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1783] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1784] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1785] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1786] The present invention is a system that utilizes a generative AI model to provide an environment where users can easily ask for advice on anything, anytime. This system begins with user authentication and includes processes such as interactive assistance, providing responses, collecting feedback, goal setting, and progress management. A specific embodiment of this system is described below.

[1787] System Overview

[1788] The system includes the following elements:

[1789] 1. User device: The device accessed and operated by the user (e.g., smartphone, tablet, PC, etc.).

[1790] 2. Server: The central system that runs the generative AI model, analyzes user questions, and provides answers.

[1791] 3. Database: Stores user authentication information, usage history, feedback, and goal setting information.

[1792] Specific functions of the system

[1793] Authentication Process

[1794] Terminal: When a user accesses the system, a login screen is displayed. The user enters their user ID and password and presses the login button.

[1795] Server: Receives the authentication information sent from the device and compares it with the information in the database. If authentication is successful, it starts a user session and sends an instruction to the device to display the next screen.

[1796] Questions and Answers

[1797] Terminal: The user accesses the dialogue interface and enters a question. When the user has finished entering the question, he or she clicks the submit button.

[1798] Server: Receives the user's question and sends it to the generative AI model, which analyzes the question and generates an appropriate answer.

[1799] Server: Sends the generated answer to the user's device.

[1800] Terminal: Display the answer to the user.

[1801] Feedback collection

[1802] Terminal: A feedback interface is displayed where users can enter their thoughts on the answers or ask follow-up questions.

[1803] Server: Receives user-entered feedback and stores it as training data for the generative AI model.

[1804] Goal setting and progress management

[1805] Terminal: Displays the goal setting screen and provides an interface for the user to input the goals they wish to set.

[1806] Server: Stores the entered goals in a database and sets reminders and alerts.

[1807] Terminal: User periodically enters and updates progress.

[1808] Server: Checks progress and sends reminders and alerts accordingly.

[1809] Specific examples

[1810] Authentication Examples

[1811] Device: The user launches the Buddy AI app and the login screen appears.

[1812] User: Enter the user ID "user@example.com" and password "password123" and click the Login button.

[1813] Server: Checks the entered authentication information against the database, and displays the main interface if authentication is successful.

[1814] Specific examples of questions and answers

[1815] Terminal: The user types in "Please tell me about risk management for a new project" and submits.

[1816] Server: Sends questions to the generative AI model and generates answers for risk management.

[1817] Terminal: The answer "The basic outline of risk management is as follows..." is displayed.

[1818] Examples of feedback collection

[1819] Device: User types "This answer was very helpful" and submits.

[1820] Server: Saves the feedback as training data.

[1821] Specific examples of goal setting and progress management

[1822] Terminal: The user types in "Complete the project risk analysis within one month" and submits.

[1823] Server: Save your goals and set weekly progress reminders.

[1824] Users: Enter and update their progress regularly.

[1825] Server: Checks progress and sends necessary reminders and alerts.

[1826] As described above, this system provides an environment in which users can easily acquire new skills and knowledge by implementing the processes of authentication, question and answer, feedback collection, goal setting, and progress management.

[1827] The processing flow will be explained below.

[1828] Step 1: "Enter your credentials"

[1829] Terminal: Displays the login screen and provides an interface for the user to enter their user ID and password.

[1830] User: Enter your user ID and password and click the Login button.

[1831] Step 2: "Submit authentication information"

[1832] Terminal: Sends the authentication information (user ID and password) entered by the user to the server.

[1833] Step 3: "Verify Authentication"

[1834] Server: Compares the received authentication information with the information in the database and generates an authentication result.

[1835] Server: If authentication is successful, it starts a user session and sends instructions to the terminal to display the next screen. If authentication fails, it returns an error message.

[1836] Step 4: View authentication results

[1837] Terminal: If authentication is successful, display the main interface. If authentication fails, display an error message to the user.

[1838] Step 5: "Enter your question"

[1839] Terminal: displays a dialogue interface for users to enter questions.

[1840] User: Type in the question they need help with and click the submit button.

[1841] Step 6: Submit your question

[1842] Terminal: Sends the questions entered by the user to the server.

[1843] Step 7: Parse the Question

[1844] Server: Sends the received question to the generative AI model and analyzes the question.

[1845] Step 8: Generate answers

[1846] Server: The generative AI model analyzes the intent of the question and generates an appropriate answer, taking into account the user's past usage history and characteristics as necessary.

[1847] Server: Sends the generated answer to the device.

[1848] Step 9: "View Answers"

[1849] Terminal: Displays the answer received from the server to the user.

[1850] Step 10: "Enter your feedback"

[1851] Terminal: Displays an interface for users to enter feedback on their answers.

[1852] User: Enter their thoughts on the answer or any follow-up questions and click the submit button.

[1853] Step 11: "Submit Feedback"

[1854] Terminal: Sends the feedback entered by the user to the server.

[1855] Step 12: Analyze feedback

[1856] Server: Analyzes the received feedback and stores it as training data for the generative AI model.

[1857] Step 13: Enter your goal settings

[1858] Terminal: Displays an interface for users to set goals.

[1859] User: Enter the goal they want to achieve and click the submit button.

[1860] Step 14: "Save Goal"

[1861] Terminal: Sends the target information entered by the user to the server.

[1862] Server: Stores goal information in a database and sets reminders and alerts for goal achievement.

[1863] Step 15: Enter your progress

[1864] Terminal: Displays an interface for the user to enter progress.

[1865] Users: Enter progress and update regularly.

[1866] Step 16: "Progress Tracking and Notifications"

[1867] Server: Checks the progress in the database, generates reminders and alerts accordingly, and notifies the user.

[1868] On the device: Displays reminders and alerts received from the server to the user.

[1869] Through these steps, the system helps users acquire new skills and knowledge.

[1870] Example 1

[1871] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1872] In today's information environment, it is important for users to be able to quickly respond to a variety of questions and instantly obtain the information they need. However, conventional systems were unable to provide appropriate answers to users' questions and had difficulty effectively collecting and utilizing user feedback. Furthermore, they lacked functionality for user goal setting and progress management, making it difficult for users to effectively manage their own learning and work progress.

[1873] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1874] In this invention, the server includes means for analyzing questions from users and using a generative AI model to generate answers, means for providing the generated answers to the users, means for collecting user feedback and saving it as learning data for the generative AI model, means for setting user goals and managing progress, means for sending reminders and alerts to the user according to progress, means for verifying authentication information and starting a user session if authentication is successful, and means for sending the authentication result to the user terminal. This allows users to receive prompt and appropriate answers to their questions and further enables them to improve their learning and work efficiency through feedback and progress management.

[1875] A "generative AI model" is an artificial intelligence technology that analyzes questions from users and generates appropriate answers.

[1876] A "means" is a specific method or device for achieving a specific function or action.

[1877] "User" refers to a person who uses the system to enter questions or set goals.

[1878] "Feedback" refers to responses provided by users, such as their impressions of the answers and services, or any follow-up questions.

[1879] "Training data" is data that a generative AI model uses to improve and refine its accuracy.

[1880] "Goal setting" is the process by which a user inputs the goals they want to achieve and manages their progress.

[1881] "Progress management" refers to checking and managing the degree of achievement and progress toward set goals.

[1882] "Reminders" is a feature that sends notifications at specific times or situations to help users remember their set goals and tasks.

[1883] An "alert" is a notification sent to alert or warn the user.

[1884] "Authentication information" refers to information such as user ID and password that a user enters when accessing a system.

[1885] A "user session" refers to the series of operations a user performs from the time they log in to the time they log out of the system.

[1886] A "database" is a system for structuring, storing, and managing data such as authentication information and usage history.

[1887] The present invention is a system that provides an environment where users can easily ask any question at any time. This system utilizes a generative AI model and encompasses processes such as user authentication, interactive assistance, response provision, feedback collection, goal setting and progress management. A specific embodiment of this system is described below.

[1888] System configuration

[1889] The system includes the following elements:

[1890] 1. User device: The device accessed and operated by the user (e.g., smartphone, tablet, PC, etc.).

[1891] 2. Server: The central system that runs the generative AI model, analyzes user questions, and provides answers.

[1892] 3. Database: A system that stores user authentication information, usage history, feedback, and goal setting information.

[1893] Hardware and Software

[1894] User device: Provides the front-end interface that users access. For example, it can be a smartphone application running on iOS or Android, or a web application running on a web browser.

[1895] Server: Cloud-based computing resources for running generative AI models (e.g., GPT-4), such as virtual machines or container services provided by Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure.

[1896] Database: A database system for storing user information and usage history. Examples include Amazon RDS, Google Cloud SQL, and Microsoft SQL Server.

[1897] Specific steps in the process and data handling

[1898] Authentication Process

[1899] When a user accesses the system from a user terminal, a login screen is displayed. The user enters their user ID and password and presses the login button. The server receives the authentication information sent from the terminal and compares it with the information in the database. If authentication is successful, it starts a user session and sends an instruction to the terminal to display the next screen.

[1900] Handling Questions and Answers

[1901] The user accesses the dialogue interface and inputs a question. For example, the user inputs and submits "Please tell me about risk management for a new project." The server receives the user's question and sends it to the generative AI model. The generative AI model analyzes the question and generates an appropriate answer. The server sends the generated answer to the user's device, and the device displays the answer to the user.

[1902] Feedback collection

[1903] A feedback interface is displayed in which the user can enter their thoughts about the answer or any additional questions. For example, they can enter "This answer was very helpful" and submit. The server receives the user's feedback and stores it as training data for the generative AI model.

[1904] Goal setting and progress management

[1905] It displays a goal setting screen and provides an interface for users to input the goals they want to set. For example, a user can input "Complete project risk analysis within one month" and submit it. The server saves the input goals in a database and sets reminders and alerts. Users can periodically input and update their progress, and the server will check the progress and send reminders and alerts as appropriate.

[1906] This allows users to efficiently acquire their own skills and knowledge. The system provides interactive support to users, helping them manage their learning and work progress through feedback and goal setting, thereby enabling users to effectively manage themselves.

[1907] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1908] Authentication Process

[1909] Step 1:

[1910] A user accesses the system using a user terminal, which displays the login screen.

[1911] Input: Enter your user ID and password.

[1912] Output: The input information is displayed on the login screen.

[1913] Step 2:

[1914] The user presses the login button.

[1915] Input: User ID and password.

[1916] Output: The input information is sent to the server.

[1917] Step 3:

[1918] The server receives the authentication information sent from the terminal and compares it with the authentication information stored in the database.

[1919] Input: User ID and password.

[1920] Data processing: Retrieve user information from the database and compare it with the received authentication information.

[1921] Output: Authentication result (success or failure).

[1922] Step 4:

[1923] The server sends the authentication result to the terminal.

[1924] Input: Authentication result.

[1925] Output: The authentication result is sent to the terminal.

[1926] Step 5:

[1927] The device will display the following screen based on the authentication result: the main interface if successful, or an error message if unsuccessful.

[1928] Input: Authentication result.

[1929] Output: The main interface or an error message is displayed.

[1930] Question and Answer Process

[1931] Step 1:

[1932] The user accesses the dialogue interface and enters a question.

[1933] Input: Question (e.g. "Please tell me about risk management for a new project").

[1934] Output: The entered question is displayed on the screen.

[1935] Step 2:

[1936] The user presses the send button.

[1937] Input: Question.

[1938] Output: The question is sent to the server.

[1939] Step 3:

[1940] The server receives the user's query.

[1941] Input: Question.

[1942] Output: The question is processed as a prompt that is sent to the generative AI model.

[1943] Step 4:

[1944] A generative AI model analyzes the question and generates an appropriate answer.

[1945] Input: The question as a prompt.

[1946] Data computation: Analyzing questions and generating answers based on relevant information.

[1947] Output: The generated answer.

[1948] Step 5:

[1949] The server receives the generated response and transmits it to the user terminal.

[1950] Input: The generated answer.

[1951] Output: The answer is sent to the user's device.

[1952] Step 6:

[1953] The device displays the answer to the user.

[1954] Input:Answer.

[1955] Output: The answer is displayed on the screen.

[1956] Feedback Collection Process

[1957] Step 1:

[1958] Users can enter their thoughts and follow-up questions into the feedback interface.

[1959] Input: Feedback (e.g., "This answer was very helpful").

[1960] Output: Feedback is displayed on the screen.

[1961] Step 2:

[1962] The user presses the send button.

[1963] Input: Feedback content.

[1964] Output: Feedback is sent to the server.

[1965] Step 3:

[1966] The server receives the feedback and stores it as training data for the generative AI model.

[1967] Input: Feedback content.

[1968] Data processing: The feedback content is organized and saved in a database as learning data.

[1969] Output: The feedback is stored in a database.

[1970] Goal setting and progress management process

[1971] Step 1:

[1972] The user accesses the goal setting screen and enters the goal.

[1973] Input: Objective (e.g., "Complete project risk analysis within one month").

[1974] Output: The entered goal is displayed on the screen.

[1975] Step 2:

[1976] The user presses the send button.

[1977] Input: Goal content.

[1978] Output: The goal is sent to the server.

[1979] Step 3:

[1980] The server receives the target content and stores it in a database.

[1981] Input: Goal content.

[1982] Data processing: Organize the target content and save it in a database.

[1983] Output: The goal is saved in the database.

[1984] Step 4:

[1985] The server sets reminders and alerts and sends them to the user accordingly.

[1986] Inputs: Progress data, alert conditions.

[1987] Output: Reminders and alerts are set and sent to the user's device.

[1988] Step 5:

[1989] Users periodically enter and update their progress.

[1990] Input: Progress data (e.g., "Risk analysis 50% complete").

[1991] Output: Progress is updated to the database.

[1992] Step 6:

[1993] The server receives progress data and sends reminders and alerts as needed.

[1994] Input: Progress data.

[1995] Output: Reminders and alerts are sent to the user's device as appropriate.

[1996] The above are the specific processing steps and details of the program for this system.

[1997] (Application example 1)

[1998] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1999] In traditional shopping experiences, customers have to put in a lot of effort to obtain product information and find the perfect product. It's also difficult for customers to provide feedback after a purchase, set goals, or track progress. It's also difficult for customers to receive personalized recommendations based on their past purchasing history and preferences. There's a need for a system that can improve this situation and provide a more efficient and personalized shopping experience.

[2000] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[2001] In this invention, the server includes: means for analyzing customer inquiries using a generative AI model and generating responses; means for providing the generated responses to the customer; means for collecting customer opinions and saving them as training data for the generative AI model; means for setting customer goals and managing progress; means for sending reminders and alerts to the customer based on their progress; means for the customer to input information about products available and for the generative AI model to recommend optimal products; and means for providing a personalized shopping experience based on the customer's past purchase history and preferences. This not only enables customers to efficiently obtain product information and find optimal products, but also facilitates post-purchase feedback, goal setting, and progress management. Furthermore, customers can receive personalized suggestions based on their past purchase history and preferences.

[2002] A "generative AI model" is a program that uses artificial intelligence algorithms to analyze input data and generate answers or suggestions in natural language.

[2003] "Customer" refers to a user who uses the system to obtain information about products or purchase products.

[2004] An "inquiry" refers to a question or request that a customer makes to the system.

[2005] A "response" is an answer or suggestion that a generative AI model generates in response to a customer inquiry.

[2006] "Opinions" refers to the feedback or thoughts that customers provide about a response.

[2007] "Training data" refers to the dataset used by a generative AI model to learn, and may include information such as opinions.

[2008] "Goal setting" refers to the act of registering specific goals and tasks that a customer wants to achieve in the system.

[2009] "Progress management" refers to the process by which a system tracks and manages the degree of achievement and progress of set goals.

[2010] "Reminder" is a function that allows the system to notify and alert you about set goals.

[2011] An "alert" is a notification that the system sends to the customer based on a specific condition or progress.

[2012] "Products handled" refers to all products offered in physical stores and online shops.

[2013] "Recommendation" refers to the act of a generative AI model suggesting the most appropriate product or service based on customer inquiries and past data.

[2014] "Purchase history" refers to a record of a customer's past purchases of products or services.

[2015] A "personalized shopping experience" refers to the entire purchasing process being individually optimized based on the customer's past purchasing history and preferences.

[2016] To implement this invention, it is necessary to build a system that uses a server, a terminal, and a generative AI model.

[2017] First, users access the system through a device such as a smartphone or smart glasses. A dedicated application called "Smart Shopping Assistant" is installed on the device. Using this application, users can log in to the system and use various functions.

[2018] The server uses a generative AI model to analyze user queries and generate a response. For example, when a user uses a smartphone to ask, "Please tell me about the ingredients in this new shampoo," the server sends this query to the generative AI model. The generative AI model uses its internal algorithm to analyze the query and generate an appropriate response. The generated response is then sent from the server to the device and displayed to the user. This response process uses advanced generative AI models such as OpenAI's GPT-3.5.

[2019] Users can also provide feedback after their purchase. For example, if a user sends feedback such as "This product was very useful," the server stores this information and uses it as training data for the generative AI model, which continuously improves the model's accuracy.

[2020] Furthermore, users can set purchasing goals and manage their progress. For example, if a user sets a goal of "completing a grocery list within a month," the server stores this information and sends periodic reminders and alerts to help users achieve their goals.

[2021] The system can also provide a personalized shopping experience based on a customer's past purchase history and preferences. For example, if a customer has previously purchased a particular brand of shampoo, the generative AI model can use that information to recommend new products from that brand, helping customers find the perfect product efficiently.

[2022] The hardware used includes cloud servers (e.g., AWS or Google Cloud), users' smartphones, and smart glasses. The software used includes OpenAI's GPT-3.5, server-side programming using Flask, and a database management system using PostgreSQL.

[2023] Below are some examples of prompt sentences.

[2024] Prompt text during the authentication process:

[2025] Please log in with your user ID "user@example.com" and password "password123".

[2026] Prompt sentences in queries and responses:

[2027] Q: What are the ingredients in your new shampoo?

[2028] Feedback gathering prompt:

[2029] This product was very helpful.

[2030] Goal setting and progress tracking prompts:

[2031] Complete your grocery list within one month.

[2032] In this way, a system can be realized that not only allows customers to efficiently obtain product information and find the most suitable product, but also makes it easy to provide feedback after purchase, and enables goal setting and progress management.

[2033] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2034] Step 1: Authentication Process

[2035] When a user accesses the system, the terminal displays a login screen. The user enters their user ID and password and presses the login button.

[2036] Input: User ID and password

[2037] Data processing: The user ID and password are sent to the server and compared with the information in the database.

[2038] Output: Authentication success or failure result

[2039] Specific operation: The server compares the authentication information stored in the database with the information entered by the user, and if authentication is successful, starts a user session and sends the following screen to the terminal.

[2040] Step 2: Questions and Answers

[2041] The user inputs a question into the dialogue interface of the terminal and sends it, and the terminal sends the question to the server.

[2042] Input: User question text

[2043] Data processing: The server sends the question text to the generative AI model, which then analyzes the question and generates an appropriate answer.

[2044] Output: Answer text

[2045] Specific operation: The server sends the generated answer to the terminal, and the terminal displays the answer to the user.

[2046] Step 3: Gather feedback

[2047] The user inputs feedback on the response into the dialogue interface and sends it, and the terminal sends the feedback to the server.

[2048] Input: User feedback text

[2049] Data processing: The server stores the feedback as training data for the generative AI model.

[2050] Output: Feedback saving completion message

[2051] What it does: The server stores the feedback data in a database and uses it to improve the generative AI model.

[2052] Step 4: Set goals and track progress

[2053] The user inputs and submits a goal on the goal setting screen, and the device sends the goal to the server.

[2054] Input: User goal text

[2055] Data processing: The server stores the goals in a database and sets reminders and alerts.

[2056] Output: Goal setting confirmation message and reminder or alert setting status

[2057] Specific behavior: The server generates periodic reminders and alerts based on the set goals and notifies the user.

[2058] Step 5: Personalized recommendations

[2059] The user inputs information about the products they sell into the terminal and sends it to the server.

[2060] Input: Product information

[2061] Data processing: The server uses a generative AI model to perform the optimal product recommendation process, providing personalized recommendations based on the user's past purchase history and preferences.

[2062] Output: Recommended products list

[2063] Specific operation: The server provides the generative AI model with information about the products on offer and the user's past data, and then sends the recommendations generated by the model to the terminal, which then displays the recommendation list to the user.

[2064] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[2065] The present invention is a system that combines a generative AI model and an emotion engine to analyze questions from users and provide appropriate answers and support based on the user's emotions. A specific embodiment of this system is described below.

[2066] System Overview

[2067] The system includes the following elements:

[2068] 1. User device: The device accessed and operated by the user (e.g., smartphone, tablet, PC, etc.).

[2069] 2. Server: The central system that runs the generative AI model and emotion engine, analyzes user questions and emotions, and provides answers.

[2070] 3. Database: Stores user authentication information, usage history, feedback, goal setting information, and emotional data.

[2071] Specific functions of the system

[2072] Authentication Process

[2073] Terminal: When a user accesses the system, a login screen is displayed. The user enters their user ID and password and presses the login button.

[2074] Server: Receives the authentication information sent from the device and compares it with the information in the database. If authentication is successful, it starts a user session and sends an instruction to the device to display the next screen.

[2075] Question and sentiment analysis

[2076] Terminal: The user accesses a dialogue interface and can enter questions and options for expressing emotions (e.g., emoticons and emotion labels).

[2077] User: Enter the question and feelings for which they need assistance and click the send button.

[2078] Server: Receives the user's question and sends it to the emotion engine to analyze the user's emotion. The analysis result and the question are sent to the generative AI model to generate an appropriate answer.

[2079] Server: Adjusts the tone and content of the response based on the user's sentiment and sends the final response to the device.

[2080] Providing answers and collecting feedback

[2081] Terminal: Displays the answers received from the server to the user, and also displays an interface for the user to enter feedback on the answers.

[2082] User: Enter their thoughts on the answer, any follow-up questions, and their emotional state (e.g., satisfied, dissatisfied, etc.), then click the submit button.

[2083] Server: Receives feedback and emotion data and stores it as training data for the generative AI model.

[2084] Goal setting and progress management

[2085] Terminal: Displays the goal setting screen and provides an interface for the user to input the goals they wish to set.

[2086] User: Enter the goal and emotional state they want to achieve and click the send button.

[2087] Server: Stores the entered goal and emotional state information in a database and sets reminders and alerts for goal achievement. These notifications are adjusted based on the user's emotional state.

[2088] Specific examples

[2089] Authentication Examples

[2090] Device: The user launches a system app and is presented with a login screen.

[2091] User: Enter the user ID "user@example.com" and password "password123" and click the Login button.

[2092] Server: Checks the entered authentication information against the database, and displays the main interface if authentication is successful.

[2093] Specific examples of question and sentiment analysis

[2094] Terminal: The user types "Please tell me about risk management for a new project," selects the emotion emoticon "anxiety," and sends.

[2095] Server: Analyzing the question and sentiment, the generative AI model generates an answer such as, "The basic outline of risk management is as follows..." The answer has a warm tone to allay the user's anxiety.

[2096] Terminal: The answer is displayed to the user.

[2097] Examples of feedback collection

[2098] Device: User types "This answer was very helpful," selects "Satisfied" as the emotional state, and submits.

[2099] Server: Stores feedback and emotional states as training data.

[2100] Specific examples of goal setting and progress management

[2101] Terminal: User types "Complete project risk analysis within one month," selects the emotion emoticon "Motivated," and submits.

[2102] Server: Store your goals and emotional state and set tone-sensitive reminders like, "You're on track!"

[2103] Users: Enter and update their progress regularly.

[2104] Server: Check in on progress and send positive reminders, such as "Great progress!"

[2105] The system helps users acquire new skills and knowledge by implementing the following processes: authentication, question and emotion analysis, answer provision and feedback collection, goal setting and progress management. By combining it with an emotion engine, it can respond to users' emotions and provide more personalized support.

[2106] The processing flow will be explained below.

[2107] Step 1: "Enter your credentials"

[2108] Terminal: Displays the login screen and provides an interface for the user to enter their user ID and password.

[2109] User: Enter your user ID and password and click the Login button.

[2110] Step 2: "Submit authentication information"

[2111] Terminal: Sends the authentication information (user ID and password) entered by the user to the server.

[2112] Step 3: "Verify Authentication"

[2113] Server: Compares the received authentication information with the information in the database and generates an authentication result.

[2114] Server: If authentication is successful, it starts a user session and sends instructions to the terminal to display the next screen. If authentication fails, it returns an error message.

[2115] Step 4: View authentication results

[2116] Terminal: If authentication is successful, display the main interface. If authentication fails, display an error message to the user.

[2117] Step 5: "Enter a question and emotion"

[2118] Terminal: Presents a dialogue interface for the user to enter a question and provides the option to select an emotion emoticon.

[2119] User: Enter a question along with an emotion emoticon or emotion label and click the submit button.

[2120] Step 6: "Submit your questions and feelings"

[2121] Terminal: Sends the questions and emotion data entered by the user to the server.

[2122] Step 7: "Analyze emotions"

[2123] Server: Uses an emotion engine to analyze the received emotion data and identify the user's emotional state.

[2124] Server: Sends the analysis results to the generative AI model.

[2125] Step 8: Parse the question and generate an answer

[2126] Server: The generative AI model generates an answer based on the received question and the results of sentiment analysis.

[2127] Server: Adjust the tone of your response to match the user's emotional state.

[2128] Server: Sends the final answer to the device.

[2129] Step 9: "View Answers"

[2130] Terminal: Displays the final answer received from the server to the user.

[2131] Step 10: "Enter your feedback"

[2132] Terminal: Displays an interface for users to enter feedback on their answers, with the option to also enter their emotional state.

[2133] User: Enter your thoughts, follow-up questions, or emotional state and click the send button.

[2134] Step 11: Send feedback and emotions

[2135] Terminal: Sends the user-entered feedback and emotional state to the server.

[2136] Step 12: Analyze feedback and sentiment

[2137] Server: Analyzes the received feedback and emotional state and stores it as training data for the generative AI model.

[2138] Step 13: Enter your goal settings

[2139] Terminal: Displays the goal setting screen and provides an interface for the user to set goals.

[2140] User: Enter the goal and emotional state they want to achieve and click the send button.

[2141] Step 14: "Preserve your goals and feelings"

[2142] Terminal: Sends the goal information and emotional state entered by the user to the server.

[2143] Server: Stores goal information and emotional state in a database and sets reminders and alerts for goal achievement.

[2144] Step 15: Enter your progress

[2145] Terminal: Displays an interface for the user to enter progress.

[2146] Users: Enter progress and update regularly.

[2147] Step 16: Progress Tracking and Emotion-Based Notifications

[2148] Server: Checks progress and emotional state in a database and generates reminders and alerts.

[2149] Server: Adjusts the content of notifications based on the emotional state and sends them to the user.

[2150] On the device: Displaying received reminders and alerts to the user.

[2151] Example 2

[2152] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2153] While systems using conventional generative AI models can provide answers based on user questions, it is difficult to provide personalized answers that take user emotions into account. It is also necessary to efficiently collect user feedback and use it as training data for generative AI models. Furthermore, it is also necessary to support user goal setting and progress management, and to appropriately send emotional reminders and alerts. It is necessary to solve these problems and improve the user experience.

[2154] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2155] In this invention, the server includes a means for analyzing user questions and emotional information using a generative AI model to generate answers, a means for providing the generated answers to the user and adjusting the tone and content based on the user's emotions, and a means for collecting user feedback and emotional data and saving it as training data for the generative AI model. This enables the provision of personalized answers to user questions that take emotions into account. The server also includes a means for setting user goals and managing progress, and a means for sending reminders and alerts to the user based on their progress and emotional state, thereby helping the user achieve their goals and maintaining their motivation at the appropriate time.

[2156] A "generative AI model" is an artificial intelligence algorithm that generates appropriate answers based on user questions and prompts.

[2157] "Emotional information" refers to the emotional expressions (e.g., emoticons or emotion labels) that users input into the system, and is data that represents the user's emotional state.

[2158] "Feedback" refers to the act and content of a user providing evaluation or additional feedback on an answer.

[2159] "Training data" is a dataset, such as collected feedback or sentiment data, that is used to improve the performance of a generative AI model.

[2160] "Goal setting" refers to the process of setting goals that users want to achieve and the content of those goals.

[2161] "Progress management" is the process of reviewing, tracking, and managing the progress of a user toward the goals they have set.

[2162] A "reminder" is a notification sent from the server to encourage the user to achieve their set goals or progress.

[2163] "Alerts" are important notifications or warnings that occur based on the user's progress or emotional state.

[2164] An "emotion engine" is a technology that analyzes a user's emotions and adjusts the tone and content of the generative AI model's responses based on the results.

[2165] "Tone" refers to the emotional nuance and style of expression of the responses generated.

[2166] MODE FOR CARRYING OUT THE INVENTION

[2167] System Overview

[2168] The present invention is a system that combines a generative AI model and an emotion engine to analyze user questions and provide appropriate answers and support based on the user's emotions. This system is designed to improve the user experience and provide personalized support. A specific embodiment of this system is described below.

[2169] Hardware and software used

[2170] User device: The device accessed and operated by a user (e.g., smartphone, tablet, PC).

[2171] Server: The central system that runs the generative AI model and emotion engine.

[2172] Database: Stores user authentication information, usage history, feedback, goal setting information, and emotional data.

[2173] Generative AI model: An artificial intelligence algorithm that generates answers based on user questions and prompts.

[2174] Emotion engine: A technology that analyzes user emotions and adjusts the tone and content of the generative AI model's responses.

[2175] Example

[2176] 1. User Authentication

[2177] Terminal: When a user accesses the system, a login screen is displayed. The user enters their user ID and password and presses the login button.

[2178] Server: Receives the authentication information sent from the terminal and compares it with the information in the database. If authentication is successful, it sends an instruction to the terminal to display the next screen.

[2179] Example: A user launches a system app, enters the user ID "user@example.com" and password "password123", and clicks the login button. The server checks the authentication information against the database, and if authentication is successful, displays the main interface.

[2180] 2. Question and Sentiment Analysis

[2181] Terminal: The user accesses a dialogue interface and can enter questions and options for expressing emotions (e.g., emoticons and emotion labels).

[2182] User: Enter the question and feelings for which they need assistance and click the send button.

[2183] Server: Receives the user's question and sends it to the emotion engine to analyze the user's emotion. The analysis result and the question are sent to the generative AI model to generate an appropriate answer.

[2184] Server: Adjusts the tone and content of the response based on the user's sentiment and sends the final response to the device.

[2185] Example: A user types, "Please tell me about risk management for a new project," selects the emotion emoticon "anxiety," and submits the question. The server analyzes the question and emotion, and the generative AI model generates an answer: "The basic outline of risk management is as follows..." The answer has a warm tone to alleviate the user's anxiety.

[2186] 3. Providing answers and collecting feedback

[2187] Terminal: Displays the answers received from the server to the user, and also displays an interface for the user to enter feedback on the answers.

[2188] User: Enter their thoughts on the answer, any follow-up questions, or their emotional state (e.g., satisfied, dissatisfied), and click the submit button.

[2189] Server: Receives feedback and emotion data and stores it as training data for the generative AI model.

[2190] Example: A user types "This answer was very helpful," selects "Satisfied" as the emotional state, and submits it. The server saves the feedback and emotional state as training data.

[2191] 4. Goal setting and progress management

[2192] Terminal: Displays the goal setting screen and provides an interface for the user to input the goals they wish to set.

[2193] User: Enter the goal and emotional state they want to achieve and click the send button.

[2194] Server: Stores the entered goal and emotional state information in a database and sets reminders and alerts for goal achievement. These notifications are adjusted based on the user's emotional state.

[2195] Example: A user enters "Complete a risk analysis for the project within one month," selects the emotion emoticon "Motivated," and submits it. The server saves the goal and emotional state, and sets tone-based reminders such as "You're making good progress!" The user periodically enters and updates their progress. The server monitors their progress and sends positive reminders such as "Great progress!"

[2196] The system helps users acquire new skills and knowledge by implementing the following processes: authentication, question and emotion analysis, answer provision and feedback collection, goal setting and progress management. By combining it with an emotion engine, it can respond to users' emotions and provide more personalized support.

[2197] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2198] Step 1:

[2199] Displaying the login screen

[2200] Terminal: When a user accesses the system, a login screen is displayed, providing fields for entering a user ID and password, and a login button.

[2201] Input: User ID, Password

[2202] Output: Sending login information

[2203] Step 2:

[2204] Sending authentication information

[2205] User: Enter your user ID (e.g., "user@example.com") and password (e.g., "password123") and click the Login button.

[2206] Terminal: Sends the entered user ID and password to the server.

[2207] Input: The credentials entered by the user

[2208] Output: Sending authentication information to the server

[2209] Step 3:

[2210] Authentication Check

[2211] Server: Receives the user ID and password sent from the terminal and accesses the database to verify this information.

[2212] Server: If authentication is successful, it starts a user session and sends a signal to the terminal to display the following screen. If authentication fails, it sends an error message to the terminal.

[2213] Input: Authentication information sent from the device

[2214] Output: Authentication result (success or failure)

[2215] Step 4:

[2216] Viewing authentication results

[2217] Terminal: Based on the signal received from the server, if authentication is successful, it displays the main interface, otherwise it displays an error message (e.g. "Authentication failed. Please try again").

[2218] Input: Authentication result sent from the server

[2219] Output: Main interface or error message display

[2220] Step 5:

[2221] Enter your question

[2222] User: Enters a question using the dialogue interface (e.g., "Please tell me about risk management for a new project") and selects an emoticon option (e.g., emoticon "😟" for anxiety).

[2223] Terminal: Provides the user with a question and emotional expression options, and sends this data to the server when the user clicks the send button.

[2224] Input: Questions and sentiment information entered by the user

[2225] Output: Sends questions and emotion information to the server

[2226] Step 6:

[2227] Question and sentiment analysis

[2228] Server: The server receives questions and emotion data from the device and sends them to the emotion engine for analysis. The emotion engine analyzes the user's emotion (e.g., anxiety) and returns the analysis results to the generative AI model.

[2229] Server: Generates and sends a prompt to the generative AI model (e.g., "The user is curious and anxious about risk management for a new project. Please generate an appropriate answer to this question.").

[2230] Input: User question and sentiment information

[2231] Output: Sentiment analysis results and generated prompt sentences

[2232] Step 7:

[2233] Generate answers

[2234] Generative AI model: Generates an answer to a user's question based on the prompt sent (e.g., "A basic overview of risk management is as follows...") The answer incorporates sentiment analysis and adds a gentle tone to allay any anxiety.

[2235] Input: Generated prompt text

[2236] Output: The generated answer

[2237] Step 8:

[2238] Providing answers

[2239] Server: Sends the generated answer to the device.

[2240] Terminal: Display the answer to the user.

[2241] Input: Generated Answer

[2242] Output: Show answer

[2243] Step 9:

[2244] Enter your feedback

[2245] User: Enters their thoughts about the provided answer (e.g., "This answer was very helpful") and emotional state (e.g., "Satisfied"), and clicks the submit button.

[2246] Terminal: Sends input feedback and emotional state to the server.

[2247] Input: Feedback and emotional state entered by the user

[2248] Output: Sending feedback and emotional state to the server

[2249] Step 10:

[2250] Save your feedback

[2251] Server: The feedback and emotion data received from the device is stored in a database and used as training data for the generative AI model.

[2252] Input: Feedback and emotion data sent from the device

[2253] Output: Save to database

[2254] Step 11:

[2255] Enter your goal settings

[2256] User: Uses the goal setting screen to enter a new goal (e.g., "Complete project risk analysis within one month") and emotional state (e.g., "Motivated"), then clicks the submit button.

[2257] Terminal: Sends goals and emotional state to the server.

[2258] Input: Goals and emotional states entered by the user

[2259] Output: Sending goals and emotional states to the server

[2260] Step 12:

[2261] Saving goals and emotional states

[2262] Server: Stores the received goal and emotional state information in a database. Sets reminders and alerts for goal achievement and adjusts them based on the user's emotional state.

[2263] Input: User's goal and emotional state

[2264] Output: Save to database and set reminder

[2265] Step 13:

[2266] Progress management

[2267] Users: Periodically enter and update progress (e.g., "50% of risk analysis is complete").

[2268] Server: Stores the received progress in a database and sends context-appropriate positive reminders and supportive messages (e.g., "Great progress!") to the device.

[2269] On the device: Display reminders and support messages to the user.

[2270] Input: progress

[2271] Output: Saving progress information to a database and displaying messages

[2272] The above is the specific flow of the system's program processing, which provides personalized support to users and enables efficient operation of the system.

[2273] (Application example 2)

[2274] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2275] On existing online shopping sites, responses to user questions are often limited to standard phrases or basic guidance, lacking personalized responses that take into account the individual feelings and circumstances of the user. Furthermore, there is no mechanism to adequately alleviate the uncertainty and anxiety users may feel when choosing a product. As a result, users are experiencing a decline in satisfaction and a decrease in their desire to purchase.

[2276] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2277] In this invention, the server includes a means for analyzing a user's emotions and adjusting the content and tone of answers and advice based on the results, a means for recommending products based on the content of the user's question and the results of the emotion analysis, and a means for analyzing the user's question using a generative AI model and generating an answer, thereby enabling personalized responses that take into account not only the content of the user's question but also their emotions.

[2278] A "generative AI model" is a type of artificial intelligence that generates natural language responses based on given text data, and is a model with advanced language processing capabilities.

[2279] "Analyzing emotions" means analyzing the emotional information contained in the text data collected from the user using specific algorithms or software to determine the user's emotional state.

[2280] "Analyzing a question" means using text analysis techniques to analyze the question text provided by the user and understand its content and purpose.

[2281] "Generating an answer" means automatically generating an appropriate response based on the analyzed question data and providing it to the user.

[2282] "Providing to the user" means displaying the generated answers and recommendations on the user's device.

[2283] "Gathering feedback" means collecting data on opinions and feelings about responses provided by users.

[2284] "Storing as training data" means accumulating collected feedback and other data in a database as training data for improving the performance of generative AI models.

[2285] "Setting goals and managing progress" means managing the progress of the goals set by the user and recording appropriate progress.

[2286] "Send reminders and alerts" means sending timely notifications about the progress of goals set by the user and important events.

[2287] "Adjusting your tone" means changing the wording and presentation of your responses depending on the user's emotional state.

[2288] "Making product recommendations" means selecting and recommending appropriate products based on the user's questions and emotional state.

[2289] System Overview

[2290] The system for implementing this invention consists of a terminal used by the user, a server connected to it, and a database. The user accesses an online shopping site application using a smartphone and inputs a question about a product. This question and the user's emotional information are sent to the server, where it is analyzed and answered.

[2291] Technology used

[2292] Hardware: Smartphones, servers

[2293] Software: Generative AI models (e.g., OpenAI's GPT-3.5), emotion engines (e.g., IBM Watson), databases (e.g., MySQL)

[2294] Question and sentiment analysis

[2295] The device sends the user's question and emotional information to the server, which uses an emotion engine to analyze the user's emotions and passes the results to the generative AI model, which then generates the optimal answer based on the user's question and emotions.

[2296] Specific examples

[2297] The user types, "What size is this shirt?" and selects the emotion emoticon "unsure." The server analyzes the question and emotion, and the generative AI model responds, "Please refer to the shirt size chart. If you're unsure, we recommend you choose your usual size."

[2298] Product recommendation function

[2299] The server makes product recommendations based on the user's questions and sentiment analysis results. A generative AI model selects products based on the user's preferences and sentiment, and provides them to the user's device as a list.

[2300] Specific examples

[2301] The user types "What's your recommendation for a birthday present?", selects the emoticon "Joy," and submits the question. The server analyzes the question and emotion, and the generative AI model responds with "Choose a gift that matches the recipient's tastes! Here are some recommended gifts," making product recommendations in a tone that conveys joy.

[2302] Gathering feedback and learning

[2303] The user's device displays a feedback interface, and the user inputs their thoughts on the answers and recommendations. The server receives this feedback and stores it as training data for the generative AI model, which improves the accuracy of future answers.

[2304] Prompt Sentence Examples

[2305] What would you recommend as a birthday gift? Emotion: Joy. Generate an appropriate answer based on this.

[2306] Goal setting and progress management

[2307] The user's device displays a goal setting screen and the user inputs the goal they want to set. The server stores the input goal and emotional state information in a database and sets reminders and alerts for goal achievement. These notifications are adjusted based on the user's emotional state.

[2308] Specific examples

[2309] A user types "Complete a risk analysis of the project within one month," selects the emotion emoticon "Motivated," and submits the goal. The server saves the goal and emotional state and sets a reminder with a tone, such as "You're on track!"

[2310] In this way, the system provides highly personalized support that responds to the user's individual circumstances and emotions.

[2311] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2312] Step 1:

[2313] When a user launches an online shopping site application on their smartphone, the login screen appears. The user enters their user ID and password and clicks the login button.

[2314] Input: User ID "user@example.com" and password "password123"

[2315] Output: Authentication information sent to the server

[2316] Step 2:

[2317] The server receives the authentication information submitted and checks it against its database. If authentication is successful, it starts a user session and displays the following screen:

[2318] Input: Credentials

[2319] Output: User authentication result and session start instruction

[2320] Step 3:

[2321] User types a question about the product, selects an emotion emoticon, and submits it. Example: "Please tell me about risk management for a new project" (emotion emoticon: "anxiety")

[2322] Input: Question text and emotion emoticons

[2323] Output: The user's question and emotion information are sent to the server.

[2324] Step 4:

[2325] The server receives the user's question and emotional information, sends it to the emotion engine for emotional analysis, and passes the analyzed emotional data to the generative AI model, which then generates the optimal answer to the question.

[2326] Input: Question text and sentiment information

[2327] Output: Parsed sentiment data and generated answers

[2328] Step 5:

[2329] The server receives the generated answers, adjusts the tone and content of the answers based on the user's feelings, and sends the final answers to the user's smartphone.

[2330] Input: Generated answers and sentiment data

[2331] Output: Adjusted answer

[2332] Step 6:

[2333] The device displays the tailored answer to the user, who then enters their feedback or emotional state (e.g., "Satisfied") about the answer and clicks the submit button.

[2334] Input: User feedback and emotional state

[2335] Output: Feedback and emotional state are sent to the server

[2336] Step 7:

[2337] The server stores the received feedback and emotional state in a database as training data for the generative AI model.

[2338] Input: Feedback and emotional state

[2339] Output: Save as training data

[2340] Step 8:

[2341] The user uses the goal setting screen to input the goal and emotional state they want to set (e.g., "Complete the project risk analysis within one month," emotional emoticon: "motivated").

[2342] Input: Target text and emotion emoticons

[2343] Output: Goal and emotion information is sent to the server

[2344] Step 9:

[2345] The server stores the received goal and emotional information in a database and sets reminders and alerts for goal achievement, which are adjusted based on the user's emotional state.

[2346] Input: goal and emotion information

[2347] Output: Instructions for setting reminders and alerts

[2348] Step 10:

[2349] The device periodically prompts the user for progress input and updates, and the server monitors the progress and sends appropriate reminders and alerts (e.g., "Great progress!").

[2350] Input: Progress data

[2351] Output: Reminders and alerts

[2352] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2353] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2354] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2355] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2356] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2357] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2358] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2359] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2360] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2361] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2362] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2363] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2364] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2365] 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.

[2366] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2367] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2368] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2369] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2370] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2371] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2372] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2373] The following is further disclosed regarding the above embodiment.

[2374] (Claim 1)

[2375] A generative AI model analyzes user questions and generates answers;

[2376] a means for providing the generated answer to the user;

[2377] A means of collecting user feedback and storing it as training data for the generative AI model;

[2378] A means for users to set goals and track progress;

[2379] A means to send reminders and alerts to users based on their progress;

[2380] A system including:

[2381] (Claim 2)

[2382] a means for authenticating a user;

[2383] means including a database for verifying authentication information;

[2384] The system of claim 1 further comprising:

[2385] (Claim 3)

[2386] 10. The system of claim 1, further comprising means for generating an answer taking into account a user's past usage history and characteristics.

[2387] "Example 1"

[2388] (Claim 1)

[2389] A method using a generative AI model that analyzes user questions and generates answers;

[2390] a means for providing the generated answer to the user;

[2391] A means of collecting user feedback and storing it as training data for the generative AI model;

[2392] A means for users to set goals and track progress;

[2393] A means to send reminders and alerts to users based on their progress;

[2394] a means for verifying the authentication information and initiating a user session if authentication is successful;

[2395] means for transmitting the authentication result to the user terminal;

[2396] A system including:

[2397] (Claim 2)

[2398] a means for authenticating a user;

[2399] means including a database for verifying authentication information;

[2400] The system of claim 1 further comprising:

[2401] (Claim 3)

[2402] 10. The system of claim 1, further comprising means for generating an answer taking into account a user's past usage history and characteristics.

[2403] "Application Example 1"

[2404] (Claim 1)

[2405] A means for analyzing customer inquiries and generating responses using a generative AI model;

[2406] a means for providing the generated response to the customer;

[2407] A means of collecting customer feedback and storing it as training data for generative AI models;

[2408] A means of setting customer goals and tracking progress,

[2409] A way to send reminders and alerts to customers based on progress, and

[2410] A means for customers to input information about products available and for a generative AI model to recommend the most suitable products;

[2411] A means to provide a personalized shopping experience based on a customer's past purchase history and preferences;

[2412] A system including:

[2413] (Claim 2)

[2414] a means for authenticating the customer;

[2415] means including a database for verifying authentication information;

[2416] The system of claim 1 further comprising:

[2417] (Claim 3)

[2418] 10. The system of claim 1, further comprising means for generating a response taking into account a customer's past usage history and characteristics.

[2419] "Example 2: Combining Emotion Engines"

[2420] (Claim 1)

[2421] A generative AI model analyzes questions and emotional information from users and generates answers.

[2422] A way to provide generated answers to users and adjust the tone and content based on sentiment;

[2423] A means of collecting user feedback and sentiment data and storing it as training data for the generative AI model;

[2424] A means for users to set goals and track progress;

[2425] A means of sending reminders and alerts to users based on their progress and emotional state;

[2426] A system including:

[2427] (Claim 2)

[2428] a means for authenticating a user;

[2429] means including a database for verifying authentication information;

[2430] The system of claim 1 further comprising:

[2431] (Claim 3)

[2432] 10. The system of claim 1, further comprising means for generating a response taking into consideration the user's past usage history and characteristics, and adjusting the tone and content of the response based on the results of sentiment analysis.

[2433] "Application example 2 when combining emotion engines"

[2434] (Claim 1)

[2435] A generative AI model analyzes user questions and generates answers;

[2436] a means for providing the generated answer to the user; ...

Claims

1. A generative AI model analyzes user questions and generates answers; a means for providing the generated answer to the user; A means of collecting user feedback and storing it as training data for the generative AI model; A means for users to set goals and track progress; A means to send reminders and alerts to users based on their progress; A system including:

2. a means for authenticating a user; means including a database for verifying authentication information; The system of claim 1 further comprising:

3. The system according to claim 1 , further comprising means for generating an answer taking into consideration a user's past usage history and characteristics.

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