System

Abstract

A system is provided.SOLUTION: A system comprising: means for acquiring basic information from a user; means for transmitting the basic information to a server; means for interacting with the user and recording an interaction content; means for transmitting the interaction content to the server; means for the server accumulating the basic information and the interaction content; means for the server analyzing the basic information and the interaction content and generating a future image; and means for presenting the generated future image to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Many young people are concerned about their future careers and lifestyles, but there is a lack of effective systems to support them. There is a need to reduce anxiety about the future and unplanned behavior, and to help them set specific goals and develop action plans. To solve this problem, a system is needed that utilizes data obtained from each user's basic information and dialogue to generate a vision of their future and provide specific advice. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for acquiring basic information from a user, a means for transmitting the basic information to a server, a means for engaging in a dialogue with the user and recording the dialogue, a means for transmitting the dialogue to the server, a means for the server to store the basic information and the dialogue, a means for the server to analyze the basic information and the dialogue and generate a future vision, and a means for presenting the generated future vision to the user. Through dialogue sessions held several times a year, users can receive specific advice on future occupations, places of residence, lifestyles, fields of study, personality traits, and so on. By providing specific future visions and action plans tailored to the needs of each individual user, the system reduces anxiety about the future and shows them a path toward achieving their goals.

[0006] "Users" refer to individuals who use the system, particularly young people seeking advice regarding their future careers and lifestyles.

[0007] "Basic information" refers to the initial information entered about the user, including name, age, gender, school, hobbies, interests, strengths and weaknesses, etc.

[0008] "Server" refers to a computer system that stores and analyzes basic information and conversation content obtained from users.

[0009] An "interactive session" refers to the process in which a user interacts with an AI interactive bot in natural language.

[0010] "Dialogue content" refers to the record of the conversation between the user and the AI ​​dialogue bot during the dialogue session.

[0011] "Generating" refers to the server creating future visions and advice based on the data it collects.

[0012] "Future vision" refers to predictions generated by the server by analyzing the user's data, such as the user's future occupation, place of residence, lifestyle, areas of study, and personality traits that the user should be careful of.

[0013] "Presenting" refers to displaying or providing the future vision or advice generated by the server to the user. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention acquires basic information from users, analyzes that information, generates a vision of the user's future, and provides specific advice. This system is implemented using a user terminal, a server, and an AI conversation bot.

[0036] User input of basic information

[0037] When a user first accesses the system, he or she logs in and enters basic information, including name, age, gender, school, hobbies, interests, strengths and weaknesses.

[0038] The device acquires this basic information and sends it to the server, which then stores it in a database.

[0039] Initiating interactive sessions and collecting user information

[0040] The user clicks the "Start Dialogue" button to start a dialogue session. The device launches an AI dialogue bot session, and the user and the AI ​​dialogue bot converse in natural language. The dialogue is recorded in real time.

[0041] The terminal sends the conversation content (conversation log) to the server, which stores this information in a database.

[0042] Analyzing data and generating future vision

[0043] The server integrates and analyzes the user's basic information and conversation history, using machine learning algorithms to consider a wide range of data points, including career aptitude, residential choice, lifestyle, areas of study, and personality traits.

[0044] Based on the analysis results, the server generates a future vision and specific advice for the user, which concretely shows the user's current situation and future prospects.

[0045] Providing advice

[0046] The server transmits the generated future image and advice to the terminal, which then presents it to the user.

[0047] As a concrete example, consider the case where a junior high school student user accesses the system. When logging in for the first time, the user enters the following: name "Ms. A," age "14," gender "female," school "junior high school," hobbies "reading, basketball," interests "medical field, literature," strengths "English," and weaknesses "math."

[0048] Next, let's say you start a dialogue session and ask the AI ​​bot for advice about your future career. During the conversation, it becomes clear that you're interested in the medical field but don't know what to study specifically. This information is sent to the server and stored.

[0049] The server analyzes this information and generates specific advice, such as, "To enter the medical field, it is important to learn the basics of biology and chemistry, so it would be a good idea to choose a nearby high school that offers a good range of these subjects." This advice is then presented to the user via their device, helping them to concretely envision their future.

[0050] As a result, the present invention can alleviate users' anxieties about the future and show them a concrete path to achieving their goals.

[0051] The processing flow will be explained below.

[0052] Step 1:

[0053] A user logs in to an application. The application provides a login screen, and the user enters their account information to pass authentication.

[0054] Step 2:

[0055] When a user first uses the device, they enter basic information, including their name, age, gender, school, hobbies, interests, strengths, and weaknesses. Once the information is entered, the device stores this information locally.

[0056] Step 3:

[0057] The device sends basic information stored locally to the server, which receives this information and stores it in a database.

[0058] Step 4:

[0059] The user starts a conversation session by clicking the "Start conversation" button. The device launches an AI conversation bot session, and the user and the AI ​​conversation bot converse in natural language.

[0060] Step 5:

[0061] The user engages in a conversation with an AI conversation bot. The AI ​​conversation bot responds to the user's questions and records the content in real time. The device temporarily saves the collected conversation content (conversation log).

[0062] Step 6:

[0063] The device sends the collected conversation content to a server, which receives this information and stores it in a database.

[0064] Step 7:

[0065] The server aggregates the basic information and conversation content obtained from the user and performs data analysis, using machine learning algorithms to extract user trends and interests.

[0066] Step 8:

[0067] Based on the analysis results, the server generates a future profile of the user, including their occupation, place of residence, lifestyle, field of study, and personality traits they should pay attention to.

[0068] Step 9:

[0069] The server sends the generated future vision and specific advice to the terminal, which receives this information and presents it to the user.

[0070] Step 10:

[0071] The user can view the proposed future vision and advice, plan the next dialogue session, and set corresponding study plans and lifestyles as needed.

[0072] Example 1

[0073] 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."

[0074] In modern society, it is important for individuals to clearly visualize their future, but this requires the collection and analysis of a large amount of information. However, in order for individuals to concretely visualize their future, they must effectively collect a wide variety of information and conduct specialized analysis. This process takes a great deal of time and effort, making it difficult for average users. For this reason, it is necessary to provide a system that allows users to have a clear vision of their future and receive specific advice based on that vision.

[0075] 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.

[0076] In this invention, the server includes means for acquiring basic information from a user, means for transmitting the basic information to the server via a communication terminal, means for the user and the AI ​​conversational bot to dialogue and record the dialogue content, means for transmitting the dialogue content to the server via the communication terminal, means for the server to store the basic information and the dialogue content in a database, means for the server to analyze the basic information and the dialogue content using a machine learning algorithm and generate a future image, and means for presenting the generated future image and advice to the user's communication terminal. This allows users to receive real-time analysis based on their basic information and the dialogue content, and easily obtain specific future images and advice.

[0077] A "user" is an individual who uses the system to input their basic information and receives future vision and advice through dialogue with an AI conversational bot.

[0078] "Basic information" refers to information about the user, such as name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses.

[0079] A "communication terminal" is an electronic device used by a user to input basic information, interact with an AI conversational bot, and exchange information with a server.

[0080] A "server" is a central processing unit that receives basic information and conversational content from users, stores this information, analyzes it, and provides the results to users.

[0081] An "AI conversation bot" is a system that uses artificial intelligence technology to converse with users in natural language and record the content of that conversation.

[0082] A "database" is a data storage system within a server that stores basic information about users and the contents of their interactions.

[0083] A "machine learning algorithm" is an artificial intelligence technique used to analyze a user's basic information and dialogue content to generate a future image.

[0084] The "future image" is a specific vision of the user's future that is generated by the server based on the user's basic information and the content of the dialogue.

[0085] "Advice" is a specific course of action or recommendation provided to the user based on the future image generated by the server.

[0086] This invention is a system that acquires basic information from users, analyzes that information, creates a future image for the user, and provides specific advice. This system is implemented using a user terminal, a server, and an AI conversation bot.

[0087] User input of basic information

[0088] When a user first accesses the system, they log in and enter basic information such as their name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses, etc. This information is provided, for example, through an input form on a web browser.

[0089] The device acquires basic information entered by the user and converts it into a specified data format (string, integer, list, etc.). This device includes PCs, smartphones, tablets, etc.

[0090] The device sends the converted basic information to the server using the HTTPS protocol, and the transmitted data is encrypted using SSL / TLS.

[0091] The basic information received by the server is decoded by the security module and passed to the database module for storage. The database is a relational database such as MySQL or PostgreSQL.

[0092] Initiating interactive sessions and collecting user information

[0093] A user clicks the "Start conversation" button to start a conversation with the AI ​​bot. The conversation interface can be a web browser-based chat window or a mobile app.

[0094] The device launches an AI conversation bot session, and the user and the AI ​​conversation bot converse in natural language. The AI ​​conversation bot uses natural language processing technologies such as Google Dialogflow and IBM Watson Assistant.

[0095] Users ask questions or ask inquiries in everyday language, and the content is recorded and converted into text in real time.

[0096] The device records and converts the conversation into text in real time and periodically sends it to the server, using real-time communication technologies such as WebSocket.

[0097] The server stores the conversation content it receives in a database and uses it for later analysis.

[0098] Analyzing data and generating future vision

[0099] The server uses a scheduled task manager to launch tasks to analyze the data in real time, using Python machine learning libraries (such as Scikit-learn and TensorFlow).

[0100] The server aggregates the user's basic information and past interactions and analyzes them with machine learning algorithms, taking into account a wide range of data points, including career aptitude, residential choice, lifestyle, areas of study, and personality.

[0101] Based on the analysis results, the server generates a future vision and specific advice for the user, using a natural language generation model (e.g., GPT-3).

[0102] Providing advice

[0103] The server sends the generated future image and advice to the user's communication device via a REST API, with the data sent in JSON format.

[0104] The advice received by the device is displayed in the user interface, and graphs and charts can be used to make it easier to understand visually.

[0105] The user can review the advice provided and decide on the next course of action.

[0106] Specific examples

[0107] Consider the case where a junior high school student user first accesses the system. This user enters his / her name "Ms. A," his / her age "14 years old," his / her gender "female," his / her school "junior high school," his / her hobbies "reading, basketball," his / her interests "medical field, literature," his / her strengths "English," and his / her weaknesses "math."

[0108] Next, the user consults the AI ​​conversation bot about "worried about their future career." During the conversation, it becomes clear that the user is interested in the medical field but doesn't know what to study specifically.

[0109] The server analyzes this information and generates specific advice, such as, "To enter the medical field, it is important to learn the basics of biology and chemistry, so it would be a good idea to choose a nearby high school that offers a good range of these subjects."

[0110] An example of a prompt for a generative AI model is: "A, a 14-year-old middle school student, says she wants to go into the medical field but doesn't know what to study. What advice should you give her?"

[0111] In this way, the present invention can reduce the user's anxiety about their future and clarify the path to achieving specific goals.

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Step 1:

[0114] When a user accesses the system for the first time, they enter the necessary authentication information on the login screen. If the login is successful, they proceed to the basic information input screen. The user enters basic information such as their name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses. The basic information entered is displayed on the terminal as a confirmation screen, and after the user confirms it, it is converted into a data format and a data set for transmission is generated. The input data is in the form of a string, integer, list, etc.

[0115] Input: Name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses

[0116] Output: Dataset with basic information transformed

[0117] Step 2:

[0118] The terminal sends the converted basic information to the server using the HTTPS protocol. The transmitted data is encrypted using SSL / TLS. The data received by the server is decoded by the security module and passed to the database module for storage. The database stores the information of each individual user after properly indexing it. The database used is, for example, MySQL or PostgreSQL.

[0119] Input: Dataset with basic information transformed

[0120] Output: Basic information stored in the database

[0121] Step 3:

[0122] A session with the AI ​​conversational bot begins when the user clicks the "Start conversation" button. The conversation interface is a web browser-based chat window or a mobile app. The device launches the AI ​​conversational bot session, and the AI ​​conversational bot converses with the user in natural language. The conversation is recorded and converted into text in real time.

[0123] Input: Request to start a conversation

[0124] Output: Start of a conversation session with the AI ​​conversation bot

[0125] Step 4:

[0126] The user asks questions and provides advice to the AI ​​conversational bot in everyday language. For example, they might ask, "I'm worried about my future career," or "I'm interested in the medical field, but I don't know what to study." The device converts the conversation into text in real time and records it. The conversation is sent to the server every five seconds. Real-time communication technologies such as WebSocket are used.

[0127] Input: User interaction

[0128] Output: Real-time transcript of conversation

[0129] Step 5:

[0130] The server stores the received conversation content in a database. The conversation content is saved in text format and used for later analysis. The database saves the conversation content with a timestamp, recording when and what was discussed.

[0131] Input: Real-time transcription of conversation records

[0132] Output: Interaction records stored in a database

[0133] Step 6:

[0134] The server uses a scheduled task manager to launch tasks that analyze data in real time. Python machine learning libraries (such as Scikit-learn and TensorFlow) are used for the analysis. The server combines the user's past basic information and conversations and analyzes them using machine learning algorithms. This includes career aptitude, choice of residence, lifestyle, areas of study, and personality.

[0135] Input: Basic information and conversation records

[0136] Output: Analysis results

[0137] Step 7:

[0138] The server generates a future vision and specific advice for the user based on the analysis results. This generation uses a natural language generation model (e.g., GPT-3). The generated future vision and advice are saved in text format.

[0139] Input: Analysis results

[0140] Output: Generated future vision and advice

[0141] Step 8:

[0142] The server sends the generated future image and advice to the user's device. The data is sent via a REST API in JSON format. The device displays the advice received in a user interface. Graphs and charts can also be used for visual clarity.

[0143] Input: Generated future vision and advice

[0144] Output: Advice displayed in the user interface

[0145] Specific examples

[0146] When a junior high school student user first accesses the system, they enter their name "Ms. A," age "14," gender "female," school "junior high school," hobbies "reading, basketball," interests "medical field, literature," strengths "English," and weaknesses "math." Next, the user consults the AI ​​conversation bot, saying, "I'm worried about my future career." During the conversation, it becomes clear that the user is interested in the medical field but doesn't know what to study specifically. The server analyzes this information and generates specific advice: "To enter the medical field, it is important to learn the basics of biology and chemistry, and it would be a good idea to choose a nearby high school that offers a good range of these subjects." The generated advice is sent to the user's device and displayed.

[0147] An example of a prompt for a generative AI model is: "A, a 14-year-old middle school student, says she wants to go into the medical field but doesn't know what to study. What advice should you give her?"

[0148] (Application example 1)

[0149] 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."

[0150] Most current personalized shopping assistant systems present products based on a user's purchasing history and basic preferences, making it difficult to predict a user's future purchasing trends and recommend optimal products based on those predictions. Furthermore, systems that provide specific advice to users are limited. Therefore, new methods are needed to suggest optimal products for users in a timely manner and improve their purchasing experience.

[0151] 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.

[0152] In this invention, the server includes a means for acquiring basic information from the user, a means for conducting a dialogue and recording the dialogue content, a means for analyzing the basic information and the dialogue content to generate a future image, and a means for generating product recommendations, which makes it possible to predict the user's future preferences and purchasing tendencies and to suggest optimal products based on those predictions.

[0153] "Basic information" refers to personal information provided by the user, such as name, age, gender, educational institution, hobbies, interests, strengths and weaknesses.

[0154] A "server" is a computer system that stores and analyzes basic information and conversation content sent by users.

[0155] "Dialogue content" is a record of the conversation between the user and the system.

[0156] "Future Vision" is a prediction of the user's future occupation, place of residence, lifestyle, areas of study, personality traits, etc., generated by analyzing the user's basic information and conversation content.

[0157] "Product recommendation" is the proposal of products that are considered optimal for the user based on the generated future image.

[0158] "Means of dialogue" refers to systems such as AI dialogue bots used to communicate with users in natural language.

[0159] The present invention provides a system that predicts a user's future purchasing trends and recommends optimal products. The system includes a method for acquiring and analyzing basic information and conversation content, and generating product recommendations based on the information.

[0160] System Configuration

[0161] The system includes:

[0162] 1. User device (smartphone, tablet, etc.)

[0163] 2. Server

[0164] 3. AI conversation bot

[0165] Enter basic information

[0166] When a user first accesses the system, they log in and enter basic information, including their name, age, gender, educational institution, hobbies, interests, strengths and weaknesses. The device acquires this information and sends it to the server, which then stores it in a database.

[0167] Initiating a dialogue session and gathering information

[0168] The user clicks the "Start Dialogue" button to begin a dialogue session. The device launches an AI dialogue bot session, and the user and the AI ​​dialogue bot converse in natural language. The content of the dialogue is recorded in real time. The device sends the content of the dialogue (conversation log) to the server, which stores this information in a database.

[0169] Analyzing data and generating future vision

[0170] The server analyzes the user's basic information and conversations from the past, using a machine learning algorithm to consider a wide range of data points, such as career aptitude, residential choice, lifestyle, areas of study, and personality traits that need attention. Based on the analysis results, the server generates a future profile for the user.

[0171] Generate product recommendations

[0172] Based on the generated future image, the server generates optimal product recommendations for the user, including products that take into account the user's future preferences and purchasing trends.

[0173] Providing advice

[0174] The server generates product recommendations and sends them to the terminal, which then presents them to the user, allowing the user to purchase the most suitable products for their future.

[0175] Specific examples

[0176] As a concrete example, consider the case where a user accesses the system. When logging in for the first time, the user enters his name "Taro," age "30," gender "male," educational institution "university," hobbies "running, reading," interests "health, technology," strengths "planning, analysis," and weaknesses "socializing." Next, the user starts an interactive session and asks the AI ​​conversation bot, "I'm looking for a health-related product. I want something that will help me with my running, which I've recently started." This information is sent to the server and saved.

[0177] The server analyzes this information and suggests items to the user, such as the latest fitness tracking devices and running shoes, that will be useful for running. These suggestions are then presented to the user via their device.

[0178] Example prompt sentence:

[0179] "I'm looking for health and wellness products that will help me with my yoga practice, which I've recently started."

[0180] This makes it possible for the system of the present invention to predict the user's future purchasing trends and to suggest attractive and highly relevant products.

[0181] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0182] Step 1:

[0183] Enter basic information

[0184] When a user first accesses the system, they log in and enter basic information such as their name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, etc. This information is sent via the terminal to the server, which then stores the received basic information in a database.

[0185] Input data: User's basic information (name, age, gender, educational institution, hobbies, interests, strengths and weaknesses)

[0186] Output data: Basic information stored on the server

[0187] Step 2:

[0188] Starting an interactive session

[0189] The user clicks the "Start Dialogue" button to start a dialogue session. This action causes the device to launch an AI dialogue bot session. The user and the AI ​​dialogue bot converse in natural language, and the content of the dialogue is recorded in real time. The recorded content of the dialogue is sent from the device to the server, which stores this information in a database.

[0190] Input data: User clicks and interactions

[0191] Output data: Dialogue content saved on the server

[0192] Step 3:

[0193] Analyzing the data

[0194] The server then integrates the accumulated basic information and conversational content and analyzes it using machine learning algorithms. This analysis takes into account data points such as career aptitude, residential location, lifestyle, areas of study, and personality traits that need attention. The analysis results in a future image of the user.

[0195] Input data: Basic information and dialogue content stored on the server

[0196] Output data: Generated future image

[0197] Step 4:

[0198] Generate product recommendations

[0199] The server generates optimal product recommendations for the user based on the generated future image, using a generative AI model to select products that take into account the user's future preferences and purchasing trends.

[0200] Input data: Generated future image

[0201] Output data: Generated product recommendations

[0202] Step 5:

[0203] Providing advice

[0204] The server sends the generated product recommendations to the terminal, which then presents them to the user, allowing the user to purchase the most suitable products for their future.

[0205] Input data: Generated product recommendations

[0206] Output data: Product recommendations displayed on the device

[0207] 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.

[0208] This invention is a system that acquires basic information from a user, engages in dialogue with the user, analyzes the content of the dialogue and emotions, generates a vision of the user's future, and provides specific advice. This system is implemented using a user terminal, a server, an AI dialogue bot, and an emotion engine.

[0209] User input of basic information

[0210] When a user first accesses the system, he or she logs in and enters basic information, including name, age, gender, school, hobbies, interests, strengths and weaknesses.

[0211] The device acquires this basic information and sends it to the server, which then stores it in a database.

[0212] Initiating interactive sessions and collecting user information

[0213] The user clicks the "Start Dialogue" button to start a dialogue session. The device launches an AI dialogue bot session, and the user and the AI ​​dialogue bot converse in natural language. The dialogue is recorded in real time.

[0214] Analysis by emotion engine

[0215] During an interaction session, the device analyzes the user's emotions using an emotion engine, which analyzes the user's voice tone, facial expressions, text input, etc. to detect the user's emotional state.

[0216] The terminal transmits the collected dialogue content and emotion analysis results to a server, which stores this information in a database.

[0217] Analyzing data and generating future vision

[0218] The server integrates and analyzes the basic information, dialogue content, and sentiment analysis results obtained from the user, using machine learning algorithms to extract the user's tendencies, interests, and emotional state.

[0219] Based on the analysis results, the server generates a future image of the user, including occupation, place of residence, lifestyle, areas of study, personality traits to be aware of, etc. The generated future image and specific advice are customized based on the emotion analysis results obtained from the emotion engine.

[0220] Providing advice

[0221] The server transmits the generated future image and advice to the terminal, which then presents it to the user.

[0222] As a concrete example, consider the case where a junior high school student user accesses the system and enters basic information when logging in for the first time. The basic information includes name "Mr. B," age "14 years old," gender "male," school "junior high school," hobbies "music, basketball," interests "computer science," strengths "programming," and weaknesses "history."

[0223] A conversation session begins, and as the conversation progresses with the AI ​​conversation bot, the user expresses concern about their future career path. During the conversation, the emotion engine analyzes the user's tone of voice and facial expressions to detect anxiety or stress. This information is sent to the server and stored.

[0224] The server analyzes this information and, based on the user's interest in computer science and their strengths in programming, predicts a future career of "software engineer." Taking into account the anxiety expressed in the emotion analysis results, the server offers advice on setting aside time for relaxation and hobbies, as well as specific study methods for improving programming skills.

[0225] The generated future image and advice are presented to the user via the terminal, helping the user to concretely envision their future. In this way, the present invention provides a concrete future image and action plan that meets the individual needs and feelings of the user.

[0226] The processing flow will be explained below.

[0227] Step 1:

[0228] A user logs in to an application. The application provides a login screen, and the user enters their account information to pass authentication.

[0229] Step 2:

[0230] When a user first uses the device, they enter basic information, including their name, age, gender, school, hobbies, interests, strengths, and weaknesses. Once the information is entered, the device stores this information locally.

[0231] Step 3:

[0232] The device sends basic information stored locally to the server, which receives this information and stores it in a database.

[0233] Step 4:

[0234] The user starts a conversation session by clicking the "Start conversation" button. The device launches an AI conversation bot session, and the user and the AI ​​conversation bot converse in natural language.

[0235] Step 5:

[0236] During an interaction session, the terminal analyzes the user's emotions using an emotion engine, which analyzes voice tone, facial expressions, text input, etc. to detect the user's emotional state.

[0237] Step 6:

[0238] The user engages in a conversation with an AI conversation bot. The AI ​​conversation bot responds to the user's questions and records the content in real time. The device temporarily saves the collected conversation content (conversation log).

[0239] Step 7:

[0240] The device sends the content of the conversation and the results of emotion analysis to the server, which receives this information and stores it in a database.

[0241] Step 8:

[0242] The server integrates and analyzes the basic information, conversation content, and emotion analysis results obtained from the user, using machine learning algorithms to extract the user's tendencies, interests, and emotional state.

[0243] Step 9:

[0244] Based on the analysis results, the server generates a future image of the user, including occupation, place of residence, lifestyle, fields of study, personality traits to be aware of, etc. The generated future image and specific advice are customized based on the results of the sentiment analysis.

[0245] Step 10:

[0246] The server sends the generated future image and advice to the terminal, which receives this information and presents it to the user.

[0247] As a concrete example, consider the case where a junior high school student user accesses the system and enters basic information when logging in for the first time. The basic information includes name "Mr. B," age "14 years old," gender "male," school "junior high school," hobbies "music, basketball," interests "computer science," strengths "programming," and weaknesses "history."

[0248] A conversation session begins, and as the conversation progresses with the AI ​​conversation bot, the user expresses concern about their future career path. During the conversation, the emotion engine analyzes the user's tone of voice and facial expressions to detect anxiety or stress. This information is sent to the server and stored.

[0249] The server analyzes this information and, based on the user's interest in computer science and their strengths in programming, predicts a future career of "software engineer." Taking into account the anxiety expressed in the emotion analysis results, the server offers advice on setting aside time for relaxation and hobbies, as well as specific study methods for improving programming skills.

[0250] The generated future image and advice are presented to the user via the terminal, and the user can receive help in concretely drawing out their future image.

[0251] Example 2

[0252] 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."

[0253] Conventional dialogue systems generate future visions based only on the user's basic information and the content of the dialogue, making it difficult to provide advice that fully reflects the user's emotions and psychological state. Furthermore, due to the lack of emotion analysis functionality, there was an issue of being unable to properly evaluate emotions such as anxiety and stress that the user is experiencing.

[0254] 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.

[0255] In this invention, the server includes means for acquiring basic information from a user, means for transmitting the basic information to the server, means for engaging in a dialogue with the user and recording the dialogue content and emotion analysis results, means for transmitting the dialogue content and emotion analysis results to the server, means for the server to store the basic information, dialogue content, and emotion analysis results, means for the server to analyze the basic information, dialogue content, and emotion analysis results and generate a future image, and means for presenting the generated future image and specific advice to the user. This makes it possible to generate more specific and personalized future images and advice that reflect the user's emotions and psychological state.

[0256] "User" refers to an individual who uses the system to enter basic information and engage in interactive sessions.

[0257] "Basic Information" refers to personal information entered by a User into the System, including name, age, gender, educational institution, recreational activities, interests, strengths and weaknesses.

[0258] "Server" refers to a central processing unit that accumulates and analyzes basic information, dialogue content, and emotion analysis results sent by users, and generates future visions and advice.

[0259] An "interactive session" refers to the process in which a user converses with an AI interactive bot in natural language on the system, and the content of the conversation is recorded and analyzed.

[0260] "Emotion analysis" refers to the process of analyzing a user's voice tone, facial expressions, and text input to detect their emotional state in real time.

[0261] "Future image" refers to the future image of the user, including their occupation, place of residence, lifestyle, areas of study, personality traits, etc., generated by the server based on the analysis results.

[0262] "Advice" refers to recommendations such as specific action plans and study methods based on the generated future vision and the results of emotional analysis.

[0263] "Machine learning algorithm" refers to an algorithm that analyzes patterns and trends and generates future visions based on basic information, dialogue content, and emotion analysis results obtained by the server from users.

[0264] This invention is a system that acquires basic information from a user, generates a future image of the user using data collected through dialogue with an AI dialogue bot and emotion analysis results, and provides specific advice. This system is implemented using a user terminal, a server, an AI dialogue bot, and an emotion engine.

[0265] Hardware and Software

[0266] This system uses the following hardware and software:

[0267] User terminal: A personal computer, smartphone, tablet, etc., which functions as a basic information input and interaction interface.

[0268] Server: A central processing unit that stores data, analyzes it, and generates future images.

[0269] AI dialogue bot: A dialogue system using a natural language processing engine.

[0270] Emotion Engine: A software engine that analyzes voice tone, facial expressions, and text input to detect emotional states.

[0271] Program processing explanation

[0272] Obtaining basic information

[0273] When a user first accesses the system, they log in and enter basic information such as their name, age, gender, educational institution, recreational activities, interests, strengths and weaknesses, etc. The terminal acquires this basic information and sends it to the server, which then stores it in a database.

[0274] Starting an interactive session

[0275] When the user clicks the "Start Dialogue" button, the device launches an AI conversation bot session. The user and the AI ​​conversation bot converse in natural language, and the conversation is recorded in real time.

[0276] Analysis by emotion engine

[0277] During a conversation session, the device uses an emotion engine to analyze the user's voice tone, facial expressions, and text input to detect their emotional state. Based on the analysis, the device classifies their mood and psychological state. The device then sends the collected conversation content and emotion analysis results to a server, which stores this information in a database.

[0278] Data integration and analysis

[0279] The server combines the basic information, dialogue content, and sentiment analysis results obtained from the user and analyzes them using a machine learning algorithm. This analysis extracts the user's tendencies, interests, and emotional state, and generates a future image based on this. The generated future image includes occupation, place of residence, lifestyle, areas of study, personality traits, etc. The generated future image and specific advice are customized based on the sentiment analysis results.

[0280] Providing advice

[0281] The server transmits the generated future image and advice to the terminal, which then presents it to the user.

[0282] Specific examples

[0283] Consider a case where a junior high school student uses the system. The user logs in to the system for the first time and enters basic information, including name "Mr. B," age "14," gender "male," educational institution "junior high school," recreational activities "music, basketball," interests "computer science," areas of expertise "programming," and areas of expertise "history." The user begins a dialogue session and tells the AI ​​dialogue bot, "I'm worried about my future career path." During the dialogue, the emotion engine analyzes the voice tone and facial expressions to detect anxiety. This data is sent to and stored on the server. The server analyzes this information and generates a future image, such as "software engineer," and customizes specific advice on relaxation and study methods based on the emotion analysis results. The generated future image and advice are then displayed on the device.

[0284] Generative AI Models and Prompts

[0285] Example of an input prompt for a generative AI model:

[0286] Basic information obtained from the user: name "Mr. B", age "14 years old", gender "male", educational institution "junior high school", recreational activities "music, basketball", interests "computer science", areas of expertise "programming", areas of weakness "history".

[0287] Dialogue content: "I'm worried about my future career path"

[0288] Emotion analysis results: "I feel anxious and stressed"

[0289] Based on this information, I would like you to generate a vision for Mr. B's future and specific advice.

[0290] In this way, the system of the present invention makes it possible to provide a specific vision of the future and a plan of action that meets the individual needs and feelings of the user.

[0291] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0292] Step 1: Enter basic information

[0293] When a user accesses the system for the first time, they log in. As input, they enter their name, age, gender, educational institution, recreational activities, interests, strengths and weaknesses. The terminal acquires this basic information and sends it to the server. As output, the basic information is correctly sent to the server. Specifically, this involves entering data into an input form and pressing the "Submit" button.

[0294] Step 2: Save your basic information

[0295] The server receives basic information and stores it in a database. As input, it requires basic information sent from the device. As output, it stores the basic information in a database. During this process, the server uses a database access library to persist the data. Specific operations include connecting to the database and executing SQL queries.

[0296] Step 3: Starting an interactive session

[0297] The user clicks the "Start Dialogue" button. The user's click is required as input. The device launches an AI dialogue bot session. As output, the dialogue bot is launched and ready for dialogue. Specific actions include launching the dialogue bot and updating the user interface.

[0298] Step 4: Continuing the conversation

[0299] The user and the AI ​​conversation bot converse in natural language. The user's voice or text input is required as input. The device records the conversation in real time. The conversation is output as text. Specific operations include converting voice data to text and saving the text data.

[0300] Step 5: Perform sentiment analysis

[0301] During an interaction session, the device uses an emotion engine to analyze the user's voice tone, facial expressions, and text input to detect their emotional state. The inputs required are the user's voice data, facial expression data, and text data. The output is an emotion analysis result. Specific operations include the use of voice analysis software, facial recognition software, and text analysis algorithms.

[0302] Step 6: Sending dialogue content and sentiment analysis results

[0303] The terminal sends the collected dialogue content and emotion analysis results to the server. The dialogue content and emotion analysis results are required as input. The dialogue content and emotion analysis results are sent to the server as output. Specific operations include creating a data package and sending it over the network.

[0304] Step 7: Data accumulation

[0305] The server accumulates the dialogue content and emotion analysis results in a database. As input, the dialogue content and emotion analysis results sent from the device are required. As output, the dialogue content and emotion analysis results are saved in the database. Specific operations include connecting to the database and executing SQL queries.

[0306] Step 8: Data synthesis and analysis

[0307] The server integrates basic information, dialogue content, and sentiment analysis results and analyzes them using machine learning algorithms. Basic information, dialogue content, and sentiment analysis results are required as input. As output, the user's tendencies, interests, and emotional state are extracted, and a future image is generated. Specific operations include data preprocessing, feature extraction, and the application of machine learning models.

[0308] Step 9: Generate vision and advice

[0309] The server generates a future image based on the analysis results and customizes specific advice based on the sentiment analysis results. The data analysis results are required as input. The future image and specific advice are generated as output. Specific operations include using a generative AI model and generating prompt sentences.

[0310] Step 10: Providing advice

[0311] The server sends the generated future image and advice to the terminal. The generated future image and advice are required as input. The future image and advice are presented to the user as output. Specific operations include network transmission and updating of the user interface.

[0312] (Application example 2)

[0313] 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."

[0314] Conventional user assistance systems present a vision of the future by analyzing a user's basic information and dialogue. However, these systems do not take into account the user's transaction history or purchasing patterns, and therefore have the problem of being unable to provide specific advice, particularly in financial matters. As a result, they are unable to predict the user's future financial situation and provide specific support based on that prediction. To solve these problems, the present invention aims to provide a system that predicts the user's future financial situation based on transaction history and purchasing patterns and provides individually customized financial advice.

[0315] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring basic information from the user; means for transmitting the basic information to the server; means for engaging in a dialogue with the user and recording the dialogue; means for transmitting the dialogue to the server; means for the server to store the basic information and the dialogue; means for the server to analyze the basic information and the dialogue and generate a future image; means for presenting the generated future image to the user; means for acquiring a transaction history and a purchasing pattern and transmitting them to the server; means for predicting a future financial situation based on the transaction history and purchasing pattern; and means for generating financial advice based on the prediction results and presenting it to the user. This makes it possible to specifically predict a user's future financial situation and provide individually customized financial advice.

[0316] "Basic Information" refers to personal information or profile information about a user, including name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, income, daily expenses, etc.

[0317] A "server" is a computer system that stores, analyzes, and manages information sent by users.

[0318] "Dialogue content" refers to a record of the conversation between the user and the AI ​​dialogue bot.

[0319] "Transaction history" is a record of a user's past financial transactions and purchasing activities.

[0320] "Purchase patterns" are data that indicate the trends and characteristics of a user's purchasing behavior.

[0321] "Future image" is a prediction about the user's future lifestyle, occupation, etc., generated by the server by analyzing basic information and the content of the conversation.

[0322] "Financial situation" refers to the user's predicted future financial situation and asset status.

[0323] "Financial Advice" means specific financial assistance or advice based on a user's current financial data and projected future financial situation.

[0324] "Customization" refers to optimizing services and advice to suit the user's individual needs and circumstances.

[0325] This invention is a system that provides individually customized financial advice by acquiring basic information from users, conducting dialogue and emotion analysis, and predicting future financial situations based on transaction history and purchasing patterns. The system consists of a user terminal, a server, an AI dialogue bot, an emotion engine, and a database.

[0326] Hardware and Software Configuration

[0327] Hardware: The user device is a smartphone.

[0328] software:

[0329] AI conversation bot: Uses large-scale language models such as GPT-3.5 and later.

[0330] Emotion engine: Uses IBM Watson Tone Analyzer.

[0331] Database: Use Amazon RDS.

[0332] Frontend: Built with React Native.

[0333] Backend: Built with Node.js and Express.

[0334] Entering and submitting user information

[0335] When a user logs in for the first time, they enter basic information such as their name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, income, and daily expenses. This basic information is sent from the user's device to the server and stored in a database.

[0336] Starting and Recording an Interactive Session

[0337] To start a conversation, the user taps the "Start conversation" button. The user's device launches an AI conversation bot session and engages in a conversation in natural language. The conversation is recorded in real time and sent to the server.

[0338] Emotion analysis

[0339] During the interaction session, the user's device analyzes the user's emotions using an emotion engine. The emotion engine analyzes the voice tone and text input to detect the user's emotional state (e.g., stress, anxiety). The analysis results are also sent to the server and stored in a database.

[0340] Transaction data collection and analysis

[0341] The system captures the user's transaction history and purchasing patterns across electronic payment services and sends them to a server, which uses this data to analyze the user's financial habits.

[0342] Generate future financial forecasts and advice

[0343] The server integrates and analyzes basic information, conversation content, sentiment analysis results, and transaction history. This analysis uses machine learning algorithms to predict the user's future financial situation. Based on this prediction, the server generates individually customized financial advice and sends it to the user's device.

[0344] Specific examples

[0345] For example, when a 25-year-old user uses the "Future Wallet" app, they enter their income and daily expenses when they first log in. If they ask the AI ​​conversation bot, "I've been worried about rent and food lately," the emotion engine will detect the anxiety and analyze it against past transaction data. The system will then suggest specific ways to cut down on unnecessary spending and provide advice on achieving future savings goals.

[0346] Prompt Sentence Examples

[0347] text

[0348] Based on the past six months of transaction data and the user's conversations, predict his or her future financial situation and provide the best savings plan and investment strategy. The user is 25 years old and spends the same amount on rent and food every month, but based on recent conversations, he is worried about his messy spending.

[0349] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0350] Step 1:

[0351] When a user logs in for the first time, they enter basic information such as name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, income, and daily expenses. The device collects this information and sends it to the server. The server stores the received basic information in a database. In this step, the input is provided by the user and the output is the basic information stored in the database.

[0352] Step 2:

[0353] The user clicks the "Start Dialogue" button to start a dialogue session. The device launches an AI dialogue bot session and begins the dialogue. The user and the AI ​​dialogue bot converse in natural language, and the content of the dialogue is recorded in real time. The content of the dialogue is collected as data and sent from the device to the server. The server stores this data in a database. The input is the dialogue content, and the output is the dialogue content saved in the database.

[0354] Step 3:

[0355] During an interactive session, the device uses an emotion engine to analyze the user's emotions. The emotion engine analyzes voice tone and text input to detect the user's emotional state (e.g., stress, anxiety) in real time. The results of this analysis are collected as data and sent from the device to a server. The server stores this emotion data in a database. The input is the user's voice and text data, and the output is analyzed emotion data.

[0356] Step 4:

[0357] The terminal acquires the user's transaction history and purchasing patterns for electronic payment services and sends them to the server. The server stores the received transaction data in a database. The input is the transaction history and purchasing patterns, and the output is the transaction data stored in the database.

[0358] Step 5:

[0359] The server integrates the basic information, dialogue content, emotional data, and transaction data stored in the database and performs analysis using machine learning algorithms. The server predicts the user's future financial situation and generates specific financial advice such as savings goals and investment strategies. This prediction is made using a generative AI model. The input is the integrated data set, and the output is the predicted future financial situation and generated financial advice.

[0360] Step 6:

[0361] The server sends the generated financial advice to the user terminal, which presents it to the user for viewing. The input is the financial advice sent by the server, and the output is the advice presented to the user.

[0362] The above is a specific flow of processing in the system that realizes the application example, and its explanation.

[0363] 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.

[0364] 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.

[0365] 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.

[0366] [Second embodiment]

[0367] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0368] 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.

[0369] 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).

[0370] 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.

[0371] 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.

[0372] 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).

[0373] 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.

[0374] 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.

[0375] 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.

[0376] 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.

[0377] 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.

[0378] 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."

[0379] This invention acquires basic information from users, analyzes that information, generates a vision of the user's future, and provides specific advice. This system is implemented using a user terminal, a server, and an AI conversation bot.

[0380] User input of basic information

[0381] When a user first accesses the system, he or she logs in and enters basic information, including name, age, gender, school, hobbies, interests, strengths and weaknesses.

[0382] The device acquires this basic information and sends it to the server, which then stores it in a database.

[0383] Initiating interactive sessions and collecting user information

[0384] The user clicks the "Start Dialogue" button to start a dialogue session. The device launches an AI dialogue bot session, and the user and the AI ​​dialogue bot converse in natural language. The dialogue is recorded in real time.

[0385] The terminal sends the conversation content (conversation log) to the server, which stores this information in a database.

[0386] Analyzing data and generating future vision

[0387] The server integrates and analyzes the user's basic information and conversation history, using machine learning algorithms to consider a wide range of data points, including career aptitude, residential choice, lifestyle, areas of study, and personality traits.

[0388] Based on the analysis results, the server generates a future vision and specific advice for the user, which concretely shows the user's current situation and future prospects.

[0389] Providing advice

[0390] The server transmits the generated future image and advice to the terminal, which then presents it to the user.

[0391] As a concrete example, consider the case where a junior high school student user accesses the system. When logging in for the first time, the user enters the following: name "Ms. A," age "14," gender "female," school "junior high school," hobbies "reading, basketball," interests "medical field, literature," strengths "English," and weaknesses "math."

[0392] Next, let's say you start a dialogue session and ask the AI ​​bot for advice about your future career. During the conversation, it becomes clear that you're interested in the medical field but don't know what to study specifically. This information is sent to the server and stored.

[0393] The server analyzes this information and generates specific advice, such as, "To enter the medical field, it is important to learn the basics of biology and chemistry, so it would be a good idea to choose a nearby high school that offers a good range of these subjects." This advice is then presented to the user via their device, helping them to concretely envision their future.

[0394] As a result, the present invention can alleviate users' anxieties about the future and show them a concrete path to achieving their goals.

[0395] The processing flow will be explained below.

[0396] Step 1:

[0397] A user logs in to an application. The application provides a login screen, and the user enters their account information to pass authentication.

[0398] Step 2:

[0399] When a user first uses the device, they enter basic information, including their name, age, gender, school, hobbies, interests, strengths, and weaknesses. Once the information is entered, the device stores this information locally.

[0400] Step 3:

[0401] The device sends basic information stored locally to the server, which receives this information and stores it in a database.

[0402] Step 4:

[0403] The user starts a conversation session by clicking the "Start conversation" button. The device launches an AI conversation bot session, and the user and the AI ​​conversation bot converse in natural language.

[0404] Step 5:

[0405] The user engages in a conversation with an AI conversation bot. The AI ​​conversation bot responds to the user's questions and records the content in real time. The device temporarily saves the collected conversation content (conversation log).

[0406] Step 6:

[0407] The device sends the collected conversation content to a server, which receives this information and stores it in a database.

[0408] Step 7:

[0409] The server aggregates the basic information and conversation content obtained from the user and performs data analysis, using machine learning algorithms to extract user trends and interests.

[0410] Step 8:

[0411] Based on the analysis results, the server generates a future profile of the user, including their occupation, place of residence, lifestyle, field of study, and personality traits they should pay attention to.

[0412] Step 9:

[0413] The server sends the generated future vision and specific advice to the terminal, which receives this information and presents it to the user.

[0414] Step 10:

[0415] The user can view the proposed future vision and advice, plan the next dialogue session, and set corresponding study plans and lifestyles as needed.

[0416] Example 1

[0417] 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."

[0418] In modern society, it is important for individuals to clearly visualize their future, but this requires the collection and analysis of a large amount of information. However, in order for individuals to concretely visualize their future, they must effectively collect a wide variety of information and conduct specialized analysis. This process takes a great deal of time and effort, making it difficult for average users. For this reason, it is necessary to provide a system that allows users to have a clear vision of their future and receive specific advice based on that vision.

[0419] 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.

[0420] In this invention, the server includes means for acquiring basic information from a user, means for transmitting the basic information to the server via a communication terminal, means for the user and the AI ​​conversational bot to dialogue and record the dialogue content, means for transmitting the dialogue content to the server via the communication terminal, means for the server to store the basic information and the dialogue content in a database, means for the server to analyze the basic information and the dialogue content using a machine learning algorithm and generate a future image, and means for presenting the generated future image and advice to the user's communication terminal. This allows users to receive real-time analysis based on their basic information and the dialogue content, and easily obtain specific future images and advice.

[0421] A "user" is an individual who uses the system to input their basic information and receives future vision and advice through dialogue with an AI conversational bot.

[0422] "Basic information" refers to information about the user, such as name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses.

[0423] A "communication terminal" is an electronic device used by a user to input basic information, interact with an AI conversational bot, and exchange information with a server.

[0424] A "server" is a central processing unit that receives basic information and conversational content from users, stores this information, analyzes it, and provides the results to users.

[0425] An "AI conversation bot" is a system that uses artificial intelligence technology to converse with users in natural language and record the content of that conversation.

[0426] A "database" is a data storage system within a server that stores basic information about users and the contents of their interactions.

[0427] A "machine learning algorithm" is an artificial intelligence technique used to analyze a user's basic information and dialogue content to generate a future image.

[0428] The "future image" is a specific vision of the user's future that is generated by the server based on the user's basic information and the content of the dialogue.

[0429] "Advice" is a specific course of action or recommendation provided to the user based on the future image generated by the server.

[0430] This invention is a system that acquires basic information from users, analyzes that information, creates a future image for the user, and provides specific advice. This system is implemented using a user terminal, a server, and an AI conversation bot.

[0431] User input of basic information

[0432] When a user first accesses the system, they log in and enter basic information such as their name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses, etc. This information is provided, for example, through an input form on a web browser.

[0433] The device acquires basic information entered by the user and converts it into a specified data format (string, integer, list, etc.). This device includes PCs, smartphones, tablets, etc.

[0434] The device sends the converted basic information to the server using the HTTPS protocol, and the transmitted data is encrypted using SSL / TLS.

[0435] The basic information received by the server is decoded by the security module and passed to the database module for storage. The database is a relational database such as MySQL or PostgreSQL.

[0436] Initiating interactive sessions and collecting user information

[0437] A user clicks the "Start conversation" button to start a conversation with the AI ​​bot. The conversation interface can be a web browser-based chat window or a mobile app.

[0438] The device launches an AI conversation bot session, and the user and the AI ​​conversation bot converse in natural language. The AI ​​conversation bot uses natural language processing technologies such as Google Dialogflow and IBM Watson Assistant.

[0439] Users ask questions or ask inquiries in everyday language, and the content is recorded and converted into text in real time.

[0440] The device records and converts the conversation into text in real time and periodically sends it to the server, using real-time communication technologies such as WebSocket.

[0441] The server stores the conversation content it receives in a database and uses it for later analysis.

[0442] Analyzing data and generating future vision

[0443] The server uses a scheduled task manager to launch tasks to analyze the data in real time, using Python machine learning libraries (such as Scikit-learn and TensorFlow).

[0444] The server aggregates the user's basic information and past interactions and analyzes them with machine learning algorithms, taking into account a wide range of data points, including career aptitude, residential choice, lifestyle, areas of study, and personality.

[0445] Based on the analysis results, the server generates a future vision and specific advice for the user, using a natural language generation model (e.g., GPT-3).

[0446] Providing advice

[0447] The server sends the generated future image and advice to the user's communication device via a REST API, with the data sent in JSON format.

[0448] The advice received by the device is displayed in the user interface, and graphs and charts can be used to make it easier to understand visually.

[0449] The user can review the advice provided and decide on the next course of action.

[0450] Specific examples

[0451] Consider the case where a junior high school student user first accesses the system. This user enters his / her name "Ms. A," his / her age "14 years old," his / her gender "female," his / her school "junior high school," his / her hobbies "reading, basketball," his / her interests "medical field, literature," his / her strengths "English," and his / her weaknesses "math."

[0452] Next, the user consults the AI ​​conversation bot about "worried about their future career." During the conversation, it becomes clear that the user is interested in the medical field but doesn't know what to study specifically.

[0453] The server analyzes this information and generates specific advice, such as, "To enter the medical field, it is important to learn the basics of biology and chemistry, so it would be a good idea to choose a nearby high school that offers a good range of these subjects."

[0454] An example of a prompt for a generative AI model is: "A, a 14-year-old middle school student, says she wants to go into the medical field but doesn't know what to study. What advice should you give her?"

[0455] In this way, the present invention can reduce the user's anxiety about their future and clarify the path to achieving specific goals.

[0456] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0457] Step 1:

[0458] When a user accesses the system for the first time, they enter the necessary authentication information on the login screen. If the login is successful, they proceed to the basic information input screen. The user enters basic information such as their name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses. The basic information entered is displayed on the terminal as a confirmation screen, and after the user confirms it, it is converted into a data format and a data set for transmission is generated. The input data is in the form of a string, integer, list, etc.

[0459] Input: Name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses

[0460] Output: Dataset with basic information transformed

[0461] Step 2:

[0462] The terminal sends the converted basic information to the server using the HTTPS protocol. The transmitted data is encrypted using SSL / TLS. The data received by the server is decoded by the security module and passed to the database module for storage. The database stores the information of each individual user after properly indexing it. The database used is, for example, MySQL or PostgreSQL.

[0463] Input: Dataset with basic information transformed

[0464] Output: Basic information stored in the database

[0465] Step 3:

[0466] A session with the AI ​​conversational bot begins when the user clicks the "Start conversation" button. The conversation interface is a web browser-based chat window or a mobile app. The device launches the AI ​​conversational bot session, and the AI ​​conversational bot converses with the user in natural language. The conversation is recorded and converted into text in real time.

[0467] Input: Request to start a conversation

[0468] Output: Start of a conversation session with the AI ​​conversation bot

[0469] Step 4:

[0470] The user asks questions and provides advice to the AI ​​conversational bot in everyday language. For example, they might ask, "I'm worried about my future career," or "I'm interested in the medical field, but I don't know what to study." The device converts the conversation into text in real time and records it. The conversation is sent to the server every five seconds. Real-time communication technologies such as WebSocket are used.

[0471] Input: User interaction

[0472] Output: Real-time transcript of conversation

[0473] Step 5:

[0474] The server stores the received conversation content in a database. The conversation content is saved in text format and used for later analysis. The database saves the conversation content with a timestamp, recording when and what was discussed.

[0475] Input: Real-time transcription of conversation records

[0476] Output: Interaction records stored in a database

[0477] Step 6:

[0478] The server uses a scheduled task manager to launch tasks that analyze data in real time. Python machine learning libraries (such as Scikit-learn and TensorFlow) are used for the analysis. The server combines the user's past basic information and conversations and analyzes them using machine learning algorithms. This includes career aptitude, choice of residence, lifestyle, areas of study, and personality.

[0479] Input: Basic information and conversation records

[0480] Output: Analysis results

[0481] Step 7:

[0482] The server generates a future vision and specific advice for the user based on the analysis results. This generation uses a natural language generation model (e.g., GPT-3). The generated future vision and advice are saved in text format.

[0483] Input: Analysis results

[0484] Output: Generated future vision and advice

[0485] Step 8:

[0486] The server sends the generated future image and advice to the user's device. The data is sent via a REST API in JSON format. The device displays the advice received in a user interface. Graphs and charts can also be used for visual clarity.

[0487] Input: Generated future vision and advice

[0488] Output: Advice displayed in the user interface

[0489] Specific examples

[0490] When a junior high school student user first accesses the system, they enter their name "Ms. A," age "14," gender "female," school "junior high school," hobbies "reading, basketball," interests "medical field, literature," strengths "English," and weaknesses "math." Next, the user consults the AI ​​conversation bot, saying, "I'm worried about my future career." During the conversation, it becomes clear that the user is interested in the medical field but doesn't know what to study specifically. The server analyzes this information and generates specific advice: "To enter the medical field, it is important to learn the basics of biology and chemistry, and it would be a good idea to choose a nearby high school that offers a good range of these subjects." The generated advice is sent to the user's device and displayed.

[0491] An example of a prompt for a generative AI model is: "A, a 14-year-old middle school student, says she wants to go into the medical field but doesn't know what to study. What advice should you give her?"

[0492] (Application example 1)

[0493] 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."

[0494] Most current personalized shopping assistant systems present products based on a user's purchasing history and basic preferences, making it difficult to predict a user's future purchasing trends and recommend optimal products based on those predictions. Furthermore, systems that provide specific advice to users are limited. Therefore, new methods are needed to suggest optimal products for users in a timely manner and improve their purchasing experience.

[0495] 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.

[0496] In this invention, the server includes a means for acquiring basic information from the user, a means for conducting a dialogue and recording the dialogue content, a means for analyzing the basic information and the dialogue content to generate a future image, and a means for generating product recommendations, which makes it possible to predict the user's future preferences and purchasing tendencies and to suggest optimal products based on those predictions.

[0497] "Basic information" refers to personal information provided by the user, such as name, age, gender, educational institution, hobbies, interests, strengths and weaknesses.

[0498] A "server" is a computer system that stores and analyzes basic information and conversation content sent by users.

[0499] "Dialogue content" is a record of the conversation between the user and the system.

[0500] "Future Vision" is a prediction of the user's future occupation, place of residence, lifestyle, areas of study, personality traits, etc., generated by analyzing the user's basic information and conversation content.

[0501] "Product recommendation" is the proposal of products that are considered optimal for the user based on the generated future image.

[0502] "Means of dialogue" refers to systems such as AI dialogue bots used to communicate with users in natural language.

[0503] The present invention provides a system that predicts a user's future purchasing trends and recommends optimal products. The system includes a method for acquiring and analyzing basic information and conversation content, and generating product recommendations based on the information.

[0504] System Configuration

[0505] The system includes:

[0506] 1. User device (smartphone, tablet, etc.)

[0507] 2. Server

[0508] 3. AI conversation bot

[0509] Enter basic information

[0510] When a user first accesses the system, they log in and enter basic information, including their name, age, gender, educational institution, hobbies, interests, strengths and weaknesses. The device acquires this information and sends it to the server, which then stores it in a database.

[0511] Initiating a dialogue session and gathering information

[0512] The user clicks the "Start Dialogue" button to begin a dialogue session. The device launches an AI dialogue bot session, and the user and the AI ​​dialogue bot converse in natural language. The content of the dialogue is recorded in real time. The device sends the content of the dialogue (conversation log) to the server, which stores this information in a database.

[0513] Analyzing data and generating future vision

[0514] The server analyzes the user's basic information and conversations from the past, using a machine learning algorithm to consider a wide range of data points, such as career aptitude, residential choice, lifestyle, areas of study, and personality traits that need attention. Based on the analysis results, the server generates a future profile for the user.

[0515] Generate product recommendations

[0516] Based on the generated future image, the server generates optimal product recommendations for the user, including products that take into account the user's future preferences and purchasing trends.

[0517] Providing advice

[0518] The server generates product recommendations and sends them to the terminal, which then presents them to the user, allowing the user to purchase the most suitable products for their future.

[0519] Specific examples

[0520] As a concrete example, consider the case where a user accesses the system. When logging in for the first time, the user enters his name "Taro," age "30," gender "male," educational institution "university," hobbies "running, reading," interests "health, technology," strengths "planning, analysis," and weaknesses "socializing." Next, the user starts an interactive session and asks the AI ​​conversation bot, "I'm looking for a health-related product. I want something that will help me with my running, which I've recently started." This information is sent to the server and saved.

[0521] The server analyzes this information and suggests items to the user, such as the latest fitness tracking devices and running shoes, that will be useful for running. These suggestions are then presented to the user via their device.

[0522] Example prompt sentence:

[0523] "I'm looking for health and wellness products that will help me with my yoga practice, which I've recently started."

[0524] This makes it possible for the system of the present invention to predict the user's future purchasing trends and to suggest attractive and highly relevant products.

[0525] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0526] Step 1:

[0527] Enter basic information

[0528] When a user first accesses the system, they log in and enter basic information such as their name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, etc. This information is sent via the terminal to the server, which then stores the received basic information in a database.

[0529] Input data: User's basic information (name, age, gender, educational institution, hobbies, interests, strengths and weaknesses)

[0530] Output data: Basic information stored on the server

[0531] Step 2:

[0532] Starting an interactive session

[0533] The user clicks the "Start Dialogue" button to start a dialogue session. This action causes the device to launch an AI dialogue bot session. The user and the AI ​​dialogue bot converse in natural language, and the content of the dialogue is recorded in real time. The recorded content of the dialogue is sent from the device to the server, which stores this information in a database.

[0534] Input data: User clicks and interactions

[0535] Output data: Dialogue content saved on the server

[0536] Step 3:

[0537] Analyzing the data

[0538] The server then integrates the accumulated basic information and conversational content and analyzes it using machine learning algorithms. This analysis takes into account data points such as career aptitude, residential location, lifestyle, areas of study, and personality traits that need attention. The analysis results in a future image of the user.

[0539] Input data: Basic information and dialogue content stored on the server

[0540] Output data: Generated future image

[0541] Step 4:

[0542] Generate product recommendations

[0543] The server generates optimal product recommendations for the user based on the generated future image, using a generative AI model to select products that take into account the user's future preferences and purchasing trends.

[0544] Input data: Generated future image

[0545] Output data: Generated product recommendations

[0546] Step 5:

[0547] Providing advice

[0548] The server sends the generated product recommendations to the terminal, which then presents them to the user, allowing the user to purchase the most suitable products for their future.

[0549] Input data: Generated product recommendations

[0550] Output data: Product recommendations displayed on the device

[0551] 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.

[0552] This invention is a system that acquires basic information from a user, engages in dialogue with the user, analyzes the content of the dialogue and emotions, generates a vision of the user's future, and provides specific advice. This system is implemented using a user terminal, a server, an AI dialogue bot, and an emotion engine.

[0553] User input of basic information

[0554] When a user first accesses the system, he or she logs in and enters basic information, including name, age, gender, school, hobbies, interests, strengths and weaknesses.

[0555] The device acquires this basic information and sends it to the server, which then stores it in a database.

[0556] Initiating interactive sessions and collecting user information

[0557] The user clicks the "Start Dialogue" button to start a dialogue session. The device launches an AI dialogue bot session, and the user and the AI ​​dialogue bot converse in natural language. The dialogue is recorded in real time.

[0558] Analysis by emotion engine

[0559] During an interaction session, the device analyzes the user's emotions using an emotion engine, which analyzes the user's voice tone, facial expressions, text input, etc. to detect the user's emotional state.

[0560] The terminal transmits the collected dialogue content and emotion analysis results to a server, which stores this information in a database.

[0561] Analyzing data and generating future vision

[0562] The server integrates and analyzes the basic information, dialogue content, and sentiment analysis results obtained from the user, using machine learning algorithms to extract the user's tendencies, interests, and emotional state.

[0563] Based on the analysis results, the server generates a future image of the user, including occupation, place of residence, lifestyle, areas of study, personality traits to be aware of, etc. The generated future image and specific advice are customized based on the emotion analysis results obtained from the emotion engine.

[0564] Providing advice

[0565] The server transmits the generated future image and advice to the terminal, which then presents it to the user.

[0566] As a concrete example, consider the case where a junior high school student user accesses the system and enters basic information when logging in for the first time. The basic information includes name "Mr. B," age "14 years old," gender "male," school "junior high school," hobbies "music, basketball," interests "computer science," strengths "programming," and weaknesses "history."

[0567] A conversation session begins, and as the conversation progresses with the AI ​​conversation bot, the user expresses concern about their future career path. During the conversation, the emotion engine analyzes the user's tone of voice and facial expressions to detect anxiety or stress. This information is sent to the server and stored.

[0568] The server analyzes this information and, based on the user's interest in computer science and their strengths in programming, predicts a future career of "software engineer." Taking into account the anxiety expressed in the emotion analysis results, the server offers advice on setting aside time for relaxation and hobbies, as well as specific study methods for improving programming skills.

[0569] The generated future image and advice are presented to the user via the terminal, helping the user to concretely envision their future. In this way, the present invention provides a concrete future image and action plan that meets the individual needs and feelings of the user.

[0570] The processing flow will be explained below.

[0571] Step 1:

[0572] A user logs in to an application. The application provides a login screen, and the user enters their account information to pass authentication.

[0573] Step 2:

[0574] When a user first uses the device, they enter basic information, including their name, age, gender, school, hobbies, interests, strengths, and weaknesses. Once the information is entered, the device stores this information locally.

[0575] Step 3:

[0576] The device sends basic information stored locally to the server, which receives this information and stores it in a database.

[0577] Step 4:

[0578] The user starts a conversation session by clicking the "Start conversation" button. The device launches an AI conversation bot session, and the user and the AI ​​conversation bot converse in natural language.

[0579] Step 5:

[0580] During an interaction session, the terminal analyzes the user's emotions using an emotion engine, which analyzes voice tone, facial expressions, text input, etc. to detect the user's emotional state.

[0581] Step 6:

[0582] The user engages in a conversation with an AI conversation bot. The AI ​​conversation bot responds to the user's questions and records the content in real time. The device temporarily saves the collected conversation content (conversation log).

[0583] Step 7:

[0584] The device sends the content of the conversation and the results of emotion analysis to the server, which receives this information and stores it in a database.

[0585] Step 8:

[0586] The server integrates and analyzes the basic information, conversation content, and emotion analysis results obtained from the user, using machine learning algorithms to extract the user's tendencies, interests, and emotional state.

[0587] Step 9:

[0588] Based on the analysis results, the server generates a future image of the user, including occupation, place of residence, lifestyle, fields of study, personality traits to be aware of, etc. The generated future image and specific advice are customized based on the results of the sentiment analysis.

[0589] Step 10:

[0590] The server sends the generated future image and advice to the terminal, which receives this information and presents it to the user.

[0591] As a concrete example, consider the case where a junior high school student user accesses the system and enters basic information when logging in for the first time. The basic information includes name "Mr. B," age "14 years old," gender "male," school "junior high school," hobbies "music, basketball," interests "computer science," strengths "programming," and weaknesses "history."

[0592] A conversation session begins, and as the conversation progresses with the AI ​​conversation bot, the user expresses concern about their future career path. During the conversation, the emotion engine analyzes the user's tone of voice and facial expressions to detect anxiety or stress. This information is sent to the server and stored.

[0593] The server analyzes this information and, based on the user's interest in computer science and their strengths in programming, predicts a future career of "software engineer." Taking into account the anxiety expressed in the emotion analysis results, the server offers advice on setting aside time for relaxation and hobbies, as well as specific study methods for improving programming skills.

[0594] The generated future image and advice are presented to the user via the terminal, and the user can receive help in concretely drawing out their future image.

[0595] Example 2

[0596] 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."

[0597] Conventional dialogue systems generate future visions based only on the user's basic information and the content of the dialogue, making it difficult to provide advice that fully reflects the user's emotions and psychological state. Furthermore, due to the lack of emotion analysis functionality, there was an issue of being unable to properly evaluate emotions such as anxiety and stress that the user is experiencing.

[0598] 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.

[0599] In this invention, the server includes means for acquiring basic information from a user, means for transmitting the basic information to the server, means for engaging in a dialogue with the user and recording the dialogue content and emotion analysis results, means for transmitting the dialogue content and emotion analysis results to the server, means for the server to store the basic information, dialogue content, and emotion analysis results, means for the server to analyze the basic information, dialogue content, and emotion analysis results and generate a future image, and means for presenting the generated future image and specific advice to the user. This makes it possible to generate more specific and personalized future images and advice that reflect the user's emotions and psychological state.

[0600] "User" refers to an individual who uses the system to enter basic information and engage in interactive sessions.

[0601] "Basic Information" refers to personal information entered by a User into the System, including name, age, gender, educational institution, recreational activities, interests, strengths and weaknesses.

[0602] "Server" refers to a central processing unit that accumulates and analyzes basic information, dialogue content, and emotion analysis results sent by users, and generates future visions and advice.

[0603] An "interactive session" refers to the process in which a user converses with an AI interactive bot in natural language on the system, and the content of the conversation is recorded and analyzed.

[0604] "Emotion analysis" refers to the process of analyzing a user's voice tone, facial expressions, and text input to detect their emotional state in real time.

[0605] "Future image" refers to the future image of the user, including their occupation, place of residence, lifestyle, areas of study, personality traits, etc., generated by the server based on the analysis results.

[0606] "Advice" refers to recommendations such as specific action plans and study methods based on the generated future vision and the results of emotional analysis.

[0607] "Machine learning algorithm" refers to an algorithm that analyzes patterns and trends and generates future visions based on basic information, dialogue content, and emotion analysis results obtained by the server from users.

[0608] This invention is a system that acquires basic information from a user, generates a future image of the user using data collected through dialogue with an AI dialogue bot and emotion analysis results, and provides specific advice. This system is implemented using a user terminal, a server, an AI dialogue bot, and an emotion engine.

[0609] Hardware and Software

[0610] This system uses the following hardware and software:

[0611] User terminal: A personal computer, smartphone, tablet, etc., which functions as a basic information input and interaction interface.

[0612] Server: A central processing unit that stores data, analyzes it, and generates future images.

[0613] AI dialogue bot: A dialogue system using a natural language processing engine.

[0614] Emotion Engine: A software engine that analyzes voice tone, facial expressions, and text input to detect emotional states.

[0615] Program processing explanation

[0616] Obtaining basic information

[0617] When a user first accesses the system, they log in and enter basic information such as their name, age, gender, educational institution, recreational activities, interests, strengths and weaknesses, etc. The terminal acquires this basic information and sends it to the server, which then stores it in a database.

[0618] Starting an interactive session

[0619] When the user clicks the "Start Dialogue" button, the device launches an AI conversation bot session. The user and the AI ​​conversation bot converse in natural language, and the conversation is recorded in real time.

[0620] Analysis by emotion engine

[0621] During a conversation session, the device uses an emotion engine to analyze the user's voice tone, facial expressions, and text input to detect their emotional state. Based on the analysis, the device classifies their mood and psychological state. The device then sends the collected conversation content and emotion analysis results to a server, which stores this information in a database.

[0622] Data integration and analysis

[0623] The server combines the basic information, dialogue content, and sentiment analysis results obtained from the user and analyzes them using a machine learning algorithm. This analysis extracts the user's tendencies, interests, and emotional state, and generates a future image based on this. The generated future image includes occupation, place of residence, lifestyle, areas of study, personality traits, etc. The generated future image and specific advice are customized based on the sentiment analysis results.

[0624] Providing advice

[0625] The server transmits the generated future image and advice to the terminal, which then presents it to the user.

[0626] Specific examples

[0627] Consider a case where a junior high school student uses the system. The user logs in to the system for the first time and enters basic information, including name "Mr. B," age "14," gender "male," educational institution "junior high school," recreational activities "music, basketball," interests "computer science," areas of expertise "programming," and areas of expertise "history." The user begins a dialogue session and tells the AI ​​dialogue bot, "I'm worried about my future career path." During the dialogue, the emotion engine analyzes the voice tone and facial expressions to detect anxiety. This data is sent to and stored on the server. The server analyzes this information and generates a future image, such as "software engineer," and customizes specific advice on relaxation and study methods based on the emotion analysis results. The generated future image and advice are then displayed on the device.

[0628] Generative AI Models and Prompts

[0629] Example of an input prompt for a generative AI model:

[0630] Basic information obtained from the user: name "Mr. B", age "14 years old", gender "male", educational institution "junior high school", recreational activities "music, basketball", interests "computer science", areas of expertise "programming", areas of weakness "history".

[0631] Dialogue content: "I'm worried about my future career path"

[0632] Emotion analysis results: "I feel anxious and stressed"

[0633] Based on this information, I would like you to generate a vision for Mr. B's future and specific advice.

[0634] In this way, the system of the present invention makes it possible to provide a specific vision of the future and a plan of action that meets the individual needs and feelings of the user.

[0635] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0636] Step 1: Enter basic information

[0637] When a user accesses the system for the first time, they log in. As input, they enter their name, age, gender, educational institution, recreational activities, interests, strengths and weaknesses. The terminal acquires this basic information and sends it to the server. As output, the basic information is correctly sent to the server. Specifically, this involves entering data into an input form and pressing the "Submit" button.

[0638] Step 2: Save your basic information

[0639] The server receives basic information and stores it in a database. As input, it requires basic information sent from the device. As output, it stores the basic information in a database. During this process, the server uses a database access library to persist the data. Specific operations include connecting to the database and executing SQL queries.

[0640] Step 3: Starting an interactive session

[0641] The user clicks the "Start Dialogue" button. The user's click is required as input. The device launches an AI dialogue bot session. As output, the dialogue bot is launched and ready for dialogue. Specific actions include launching the dialogue bot and updating the user interface.

[0642] Step 4: Continuing the conversation

[0643] The user and the AI ​​conversation bot converse in natural language. The user's voice or text input is required as input. The device records the conversation in real time. The conversation is output as text. Specific operations include converting voice data to text and saving the text data.

[0644] Step 5: Perform sentiment analysis

[0645] During an interaction session, the device uses an emotion engine to analyze the user's voice tone, facial expressions, and text input to detect their emotional state. The inputs required are the user's voice data, facial expression data, and text data. The output is an emotion analysis result. Specific operations include the use of voice analysis software, facial recognition software, and text analysis algorithms.

[0646] Step 6: Sending dialogue content and sentiment analysis results

[0647] The terminal sends the collected dialogue content and emotion analysis results to the server. The dialogue content and emotion analysis results are required as input. The dialogue content and emotion analysis results are sent to the server as output. Specific operations include creating a data package and sending it over the network.

[0648] Step 7: Data accumulation

[0649] The server accumulates the dialogue content and emotion analysis results in a database. As input, the dialogue content and emotion analysis results sent from the device are required. As output, the dialogue content and emotion analysis results are saved in the database. Specific operations include connecting to the database and executing SQL queries.

[0650] Step 8: Data synthesis and analysis

[0651] The server integrates basic information, dialogue content, and sentiment analysis results and analyzes them using machine learning algorithms. Basic information, dialogue content, and sentiment analysis results are required as input. As output, the user's tendencies, interests, and emotional state are extracted, and a future image is generated. Specific operations include data preprocessing, feature extraction, and the application of machine learning models.

[0652] Step 9: Generate vision and advice

[0653] The server generates a future image based on the analysis results and customizes specific advice based on the sentiment analysis results. The data analysis results are required as input. The future image and specific advice are generated as output. Specific operations include using a generative AI model and generating prompt sentences.

[0654] Step 10: Providing advice

[0655] The server sends the generated future image and advice to the terminal. The generated future image and advice are required as input. The future image and advice are presented to the user as output. Specific operations include network transmission and updating of the user interface.

[0656] (Application example 2)

[0657] 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."

[0658] Conventional user assistance systems present a vision of the future by analyzing a user's basic information and dialogue. However, these systems do not take into account the user's transaction history or purchasing patterns, and therefore have the problem of being unable to provide specific advice, particularly in financial matters. As a result, they are unable to predict the user's future financial situation and provide specific support based on that prediction. To solve these problems, the present invention aims to provide a system that predicts the user's future financial situation based on transaction history and purchasing patterns and provides individually customized financial advice.

[0659] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring basic information from the user; means for transmitting the basic information to the server; means for engaging in a dialogue with the user and recording the dialogue; means for transmitting the dialogue to the server; means for the server to store the basic information and the dialogue; means for the server to analyze the basic information and the dialogue and generate a future image; means for presenting the generated future image to the user; means for acquiring a transaction history and a purchasing pattern and transmitting them to the server; means for predicting a future financial situation based on the transaction history and purchasing pattern; and means for generating financial advice based on the prediction results and presenting it to the user. This makes it possible to specifically predict a user's future financial situation and provide individually customized financial advice.

[0660] "Basic Information" refers to personal information or profile information about a user, including name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, income, daily expenses, etc.

[0661] A "server" is a computer system that stores, analyzes, and manages information sent by users.

[0662] "Dialogue content" refers to a record of the conversation between the user and the AI ​​dialogue bot.

[0663] "Transaction history" is a record of a user's past financial transactions and purchasing activities.

[0664] "Purchase patterns" are data that indicate the trends and characteristics of a user's purchasing behavior.

[0665] "Future image" is a prediction about the user's future lifestyle, occupation, etc., generated by the server by analyzing basic information and the content of the conversation.

[0666] "Financial situation" refers to the user's predicted future financial situation and asset status.

[0667] "Financial Advice" means specific financial assistance or advice based on a user's current financial data and projected future financial situation.

[0668] "Customization" refers to optimizing services and advice to suit the user's individual needs and circumstances.

[0669] This invention is a system that provides individually customized financial advice by acquiring basic information from users, conducting dialogue and emotion analysis, and predicting future financial situations based on transaction history and purchasing patterns. The system consists of a user terminal, a server, an AI dialogue bot, an emotion engine, and a database.

[0670] Hardware and Software Configuration

[0671] Hardware: The user device is a smartphone.

[0672] software:

[0673] AI conversation bot: Uses large-scale language models such as GPT-3.5 and later.

[0674] Emotion engine: Uses IBM Watson Tone Analyzer.

[0675] Database: Use Amazon RDS.

[0676] Frontend: Built with React Native.

[0677] Backend: Built with Node.js and Express.

[0678] Entering and submitting user information

[0679] When a user logs in for the first time, they enter basic information such as their name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, income, and daily expenses. This basic information is sent from the user's device to the server and stored in a database.

[0680] Starting and Recording an Interactive Session

[0681] To start a conversation, the user taps the "Start conversation" button. The user's device launches an AI conversation bot session and engages in a conversation in natural language. The conversation is recorded in real time and sent to the server.

[0682] Emotion analysis

[0683] During the interaction session, the user's device analyzes the user's emotions using an emotion engine. The emotion engine analyzes the voice tone and text input to detect the user's emotional state (e.g., stress, anxiety). The analysis results are also sent to the server and stored in a database.

[0684] Transaction data collection and analysis

[0685] The system captures the user's transaction history and purchasing patterns across electronic payment services and sends them to a server, which uses this data to analyze the user's financial habits.

[0686] Generate future financial forecasts and advice

[0687] The server integrates and analyzes basic information, conversation content, sentiment analysis results, and transaction history. This analysis uses machine learning algorithms to predict the user's future financial situation. Based on this prediction, the server generates individually customized financial advice and sends it to the user's device.

[0688] Specific examples

[0689] For example, when a 25-year-old user uses the "Future Wallet" app, they enter their income and daily expenses when they first log in. If they ask the AI ​​conversation bot, "I've been worried about rent and food lately," the emotion engine will detect the anxiety and analyze it against past transaction data. The system will then suggest specific ways to cut down on unnecessary spending and provide advice on achieving future savings goals.

[0690] Prompt Sentence Examples

[0691] text

[0692] Based on the past six months of transaction data and the user's conversations, predict his or her future financial situation and provide the best savings plan and investment strategy. The user is 25 years old and spends the same amount on rent and food every month, but based on recent conversations, he is worried about his messy spending.

[0693] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0694] Step 1:

[0695] When a user logs in for the first time, they enter basic information such as name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, income, and daily expenses. The device collects this information and sends it to the server. The server stores the received basic information in a database. In this step, the input is provided by the user and the output is the basic information stored in the database.

[0696] Step 2:

[0697] The user clicks the "Start Dialogue" button to start a dialogue session. The device launches an AI dialogue bot session and begins the dialogue. The user and the AI ​​dialogue bot converse in natural language, and the content of the dialogue is recorded in real time. The content of the dialogue is collected as data and sent from the device to the server. The server stores this data in a database. The input is the dialogue content, and the output is the dialogue content saved in the database.

[0698] Step 3:

[0699] During an interactive session, the device uses an emotion engine to analyze the user's emotions. The emotion engine analyzes voice tone and text input to detect the user's emotional state (e.g., stress, anxiety) in real time. The results of this analysis are collected as data and sent from the device to a server. The server stores this emotion data in a database. The input is the user's voice and text data, and the output is analyzed emotion data.

[0700] Step 4:

[0701] The terminal acquires the user's transaction history and purchasing patterns for electronic payment services and sends them to the server. The server stores the received transaction data in a database. The input is the transaction history and purchasing patterns, and the output is the transaction data stored in the database.

[0702] Step 5:

[0703] The server integrates the basic information, dialogue content, emotional data, and transaction data stored in the database and performs analysis using machine learning algorithms. The server predicts the user's future financial situation and generates specific financial advice such as savings goals and investment strategies. This prediction is made using a generative AI model. The input is the integrated data set, and the output is the predicted future financial situation and generated financial advice.

[0704] Step 6:

[0705] The server sends the generated financial advice to the user terminal, which presents it to the user for viewing. The input is the financial advice sent by the server, and the output is the advice presented to the user.

[0706] The above is a specific flow of processing in the system that realizes the application example, and its explanation.

[0707] 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.

[0708] 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.

[0709] 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.

[0710] [Third embodiment]

[0711] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0712] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0713] 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).

[0714] 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.

[0715] 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.

[0716] 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).

[0717] 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.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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."

[0723] This invention acquires basic information from users, analyzes that information, generates a vision of the user's future, and provides specific advice. This system is implemented using a user terminal, a server, and an AI conversation bot.

[0724] User input of basic information

[0725] When a user first accesses the system, he or she logs in and enters basic information, including name, age, gender, school, hobbies, interests, strengths and weaknesses.

[0726] The device acquires this basic information and sends it to the server, which then stores it in a database.

[0727] Initiating interactive sessions and collecting user information

[0728] The user clicks the "Start Dialogue" button to start a dialogue session. The device launches an AI dialogue bot session, and the user and the AI ​​dialogue bot converse in natural language. The dialogue is recorded in real time.

[0729] The terminal sends the conversation content (conversation log) to the server, which stores this information in a database.

[0730] Analyzing data and generating future vision

[0731] The server integrates and analyzes the user's basic information and conversation history, using machine learning algorithms to consider a wide range of data points, including career aptitude, residential choice, lifestyle, areas of study, and personality traits.

[0732] Based on the analysis results, the server generates a future vision and specific advice for the user, which concretely shows the user's current situation and future prospects.

[0733] Providing advice

[0734] The server transmits the generated future image and advice to the terminal, which then presents it to the user.

[0735] As a concrete example, consider the case where a junior high school student user accesses the system. When logging in for the first time, the user enters the following: name "Ms. A," age "14," gender "female," school "junior high school," hobbies "reading, basketball," interests "medical field, literature," strengths "English," and weaknesses "math."

[0736] Next, let's say you start a dialogue session and ask the AI ​​bot for advice about your future career. During the conversation, it becomes clear that you're interested in the medical field but don't know what to study specifically. This information is sent to the server and stored.

[0737] The server analyzes this information and generates specific advice, such as, "To enter the medical field, it is important to learn the basics of biology and chemistry, so it would be a good idea to choose a nearby high school that offers a good range of these subjects." This advice is then presented to the user via their device, helping them to concretely envision their future.

[0738] As a result, the present invention can alleviate users' anxieties about the future and show them a concrete path to achieving their goals.

[0739] The processing flow will be explained below.

[0740] Step 1:

[0741] A user logs in to an application. The application provides a login screen, and the user enters their account information to pass authentication.

[0742] Step 2:

[0743] When a user first uses the device, they enter basic information, including their name, age, gender, school, hobbies, interests, strengths, and weaknesses. Once the information is entered, the device stores this information locally.

[0744] Step 3:

[0745] The device sends basic information stored locally to the server, which receives this information and stores it in a database.

[0746] Step 4:

[0747] The user starts a conversation session by clicking the "Start conversation" button. The device launches an AI conversation bot session, and the user and the AI ​​conversation bot converse in natural language.

[0748] Step 5:

[0749] The user engages in a conversation with an AI conversation bot. The AI ​​conversation bot responds to the user's questions and records the content in real time. The device temporarily saves the collected conversation content (conversation log).

[0750] Step 6:

[0751] The device sends the collected conversation content to a server, which receives this information and stores it in a database.

[0752] Step 7:

[0753] The server aggregates the basic information and conversation content obtained from the user and performs data analysis, using machine learning algorithms to extract user trends and interests.

[0754] Step 8:

[0755] Based on the analysis results, the server generates a future profile of the user, including their occupation, place of residence, lifestyle, field of study, and personality traits they should pay attention to.

[0756] Step 9:

[0757] The server sends the generated future vision and specific advice to the terminal, which receives this information and presents it to the user.

[0758] Step 10:

[0759] The user can view the proposed future vision and advice, plan the next dialogue session, and set corresponding study plans and lifestyles as needed.

[0760] Example 1

[0761] 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."

[0762] In modern society, it is important for individuals to clearly visualize their future, but this requires the collection and analysis of a large amount of information. However, in order for individuals to concretely visualize their future, they must effectively collect a wide variety of information and conduct specialized analysis. This process takes a great deal of time and effort, making it difficult for average users. For this reason, it is necessary to provide a system that allows users to have a clear vision of their future and receive specific advice based on that vision.

[0763] 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.

[0764] In this invention, the server includes means for acquiring basic information from a user, means for transmitting the basic information to the server via a communication terminal, means for the user and the AI ​​conversational bot to dialogue and record the dialogue content, means for transmitting the dialogue content to the server via the communication terminal, means for the server to store the basic information and the dialogue content in a database, means for the server to analyze the basic information and the dialogue content using a machine learning algorithm and generate a future image, and means for presenting the generated future image and advice to the user's communication terminal. This allows users to receive real-time analysis based on their basic information and the dialogue content, and easily obtain specific future images and advice.

[0765] A "user" is an individual who uses the system to input their basic information and receives future vision and advice through dialogue with an AI conversational bot.

[0766] "Basic information" refers to information about the user, such as name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses.

[0767] A "communication terminal" is an electronic device used by a user to input basic information, interact with an AI conversational bot, and exchange information with a server.

[0768] A "server" is a central processing unit that receives basic information and conversational content from users, stores this information, analyzes it, and provides the results to users.

[0769] An "AI conversation bot" is a system that uses artificial intelligence technology to converse with users in natural language and record the content of that conversation.

[0770] A "database" is a data storage system within a server that stores basic information about users and the contents of their interactions.

[0771] A "machine learning algorithm" is an artificial intelligence technique used to analyze a user's basic information and dialogue content to generate a future image.

[0772] The "future image" is a specific vision of the user's future that is generated by the server based on the user's basic information and the content of the dialogue.

[0773] "Advice" is a specific course of action or recommendation provided to the user based on the future image generated by the server.

[0774] This invention is a system that acquires basic information from users, analyzes that information, creates a future image for the user, and provides specific advice. This system is implemented using a user terminal, a server, and an AI conversation bot.

[0775] User input of basic information

[0776] When a user first accesses the system, they log in and enter basic information such as their name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses, etc. This information is provided, for example, through an input form on a web browser.

[0777] The device acquires basic information entered by the user and converts it into a specified data format (string, integer, list, etc.). This device includes PCs, smartphones, tablets, etc.

[0778] The device sends the converted basic information to the server using the HTTPS protocol, and the transmitted data is encrypted using SSL / TLS.

[0779] The basic information received by the server is decoded by the security module and passed to the database module for storage. The database is a relational database such as MySQL or PostgreSQL.

[0780] Initiating interactive sessions and collecting user information

[0781] A user clicks the "Start conversation" button to start a conversation with the AI ​​bot. The conversation interface can be a web browser-based chat window or a mobile app.

[0782] The device launches an AI conversation bot session, and the user and the AI ​​conversation bot converse in natural language. The AI ​​conversation bot uses natural language processing technologies such as Google Dialogflow and IBM Watson Assistant.

[0783] Users ask questions or ask inquiries in everyday language, and the content is recorded and converted into text in real time.

[0784] The device records and converts the conversation into text in real time and periodically sends it to the server, using real-time communication technologies such as WebSocket.

[0785] The server stores the conversation content it receives in a database and uses it for later analysis.

[0786] Analyzing data and generating future vision

[0787] The server uses a scheduled task manager to launch tasks to analyze the data in real time, using Python machine learning libraries (such as Scikit-learn and TensorFlow).

[0788] The server aggregates the user's basic information and past interactions and analyzes them with machine learning algorithms, taking into account a wide range of data points, including career aptitude, residential choice, lifestyle, areas of study, and personality.

[0789] Based on the analysis results, the server generates a future vision and specific advice for the user, using a natural language generation model (e.g., GPT-3).

[0790] Providing advice

[0791] The server sends the generated future image and advice to the user's communication device via a REST API, with the data sent in JSON format.

[0792] The advice received by the device is displayed in the user interface, and graphs and charts can be used to make it easier to understand visually.

[0793] The user can review the advice provided and decide on the next course of action.

[0794] Specific examples

[0795] Consider the case where a junior high school student user first accesses the system. This user enters his / her name "Ms. A," his / her age "14 years old," his / her gender "female," his / her school "junior high school," his / her hobbies "reading, basketball," his / her interests "medical field, literature," his / her strengths "English," and his / her weaknesses "math."

[0796] Next, the user consults the AI ​​conversation bot about "worried about their future career." During the conversation, it becomes clear that the user is interested in the medical field but doesn't know what to study specifically.

[0797] The server analyzes this information and generates specific advice, such as, "To enter the medical field, it is important to learn the basics of biology and chemistry, so it would be a good idea to choose a nearby high school that offers a good range of these subjects."

[0798] An example of a prompt for a generative AI model is: "A, a 14-year-old middle school student, says she wants to go into the medical field but doesn't know what to study. What advice should you give her?"

[0799] In this way, the present invention can reduce the user's anxiety about their future and clarify the path to achieving specific goals.

[0800] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0801] Step 1:

[0802] When a user accesses the system for the first time, they enter the necessary authentication information on the login screen. If the login is successful, they proceed to the basic information input screen. The user enters basic information such as their name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses. The basic information entered is displayed on the terminal as a confirmation screen, and after the user confirms it, it is converted into a data format and a data set for transmission is generated. The input data is in the form of a string, integer, list, etc.

[0803] Input: Name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses

[0804] Output: Dataset with basic information transformed

[0805] Step 2:

[0806] The terminal sends the converted basic information to the server using the HTTPS protocol. The transmitted data is encrypted using SSL / TLS. The data received by the server is decoded by the security module and passed to the database module for storage. The database stores the information of each individual user after properly indexing it. The database used is, for example, MySQL or PostgreSQL.

[0807] Input: Dataset with basic information transformed

[0808] Output: Basic information stored in the database

[0809] Step 3:

[0810] A session with the AI ​​conversational bot begins when the user clicks the "Start conversation" button. The conversation interface is a web browser-based chat window or a mobile app. The device launches the AI ​​conversational bot session, and the AI ​​conversational bot converses with the user in natural language. The conversation is recorded and converted into text in real time.

[0811] Input: Request to start a conversation

[0812] Output: Start of a conversation session with the AI ​​conversation bot

[0813] Step 4:

[0814] The user asks questions and provides advice to the AI ​​conversational bot in everyday language. For example, they might ask, "I'm worried about my future career," or "I'm interested in the medical field, but I don't know what to study." The device converts the conversation into text in real time and records it. The conversation is sent to the server every five seconds. Real-time communication technologies such as WebSocket are used.

[0815] Input: User interaction

[0816] Output: Real-time transcript of conversation

[0817] Step 5:

[0818] The server stores the received conversation content in a database. The conversation content is saved in text format and used for later analysis. The database saves the conversation content with a timestamp, recording when and what was discussed.

[0819] Input: Real-time transcription of conversation records

[0820] Output: Interaction records stored in a database

[0821] Step 6:

[0822] The server uses a scheduled task manager to launch tasks that analyze data in real time. Python machine learning libraries (such as Scikit-learn and TensorFlow) are used for the analysis. The server combines the user's past basic information and conversations and analyzes them using machine learning algorithms. This includes career aptitude, choice of residence, lifestyle, areas of study, and personality.

[0823] Input: Basic information and conversation records

[0824] Output: Analysis results

[0825] Step 7:

[0826] The server generates a future vision and specific advice for the user based on the analysis results. This generation uses a natural language generation model (e.g., GPT-3). The generated future vision and advice are saved in text format.

[0827] Input: Analysis results

[0828] Output: Generated future vision and advice

[0829] Step 8:

[0830] The server sends the generated future image and advice to the user's device. The data is sent via a REST API in JSON format. The device displays the advice received in a user interface. Graphs and charts can also be used for visual clarity.

[0831] Input: Generated future vision and advice

[0832] Output: Advice displayed in the user interface

[0833] Specific examples

[0834] When a junior high school student user first accesses the system, they enter their name "Ms. A," age "14," gender "female," school "junior high school," hobbies "reading, basketball," interests "medical field, literature," strengths "English," and weaknesses "math." Next, the user consults the AI ​​conversation bot, saying, "I'm worried about my future career." During the conversation, it becomes clear that the user is interested in the medical field but doesn't know what to study specifically. The server analyzes this information and generates specific advice: "To enter the medical field, it is important to learn the basics of biology and chemistry, and it would be a good idea to choose a nearby high school that offers a good range of these subjects." The generated advice is sent to the user's device and displayed.

[0835] An example of a prompt for a generative AI model is: "A, a 14-year-old middle school student, says she wants to go into the medical field but doesn't know what to study. What advice should you give her?"

[0836] (Application example 1)

[0837] 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."

[0838] Most current personalized shopping assistant systems present products based on a user's purchasing history and basic preferences, making it difficult to predict a user's future purchasing trends and recommend optimal products based on those predictions. Furthermore, systems that provide specific advice to users are limited. Therefore, new methods are needed to suggest optimal products for users in a timely manner and improve their purchasing experience.

[0839] 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.

[0840] In this invention, the server includes a means for acquiring basic information from the user, a means for conducting a dialogue and recording the dialogue content, a means for analyzing the basic information and the dialogue content to generate a future image, and a means for generating product recommendations, which makes it possible to predict the user's future preferences and purchasing tendencies and to suggest optimal products based on those predictions.

[0841] "Basic information" refers to personal information provided by the user, such as name, age, gender, educational institution, hobbies, interests, strengths and weaknesses.

[0842] A "server" is a computer system that stores and analyzes basic information and conversation content sent by users.

[0843] "Dialogue content" is a record of the conversation between the user and the system.

[0844] "Future Vision" is a prediction of the user's future occupation, place of residence, lifestyle, areas of study, personality traits, etc., generated by analyzing the user's basic information and conversation content.

[0845] "Product recommendation" is the proposal of products that are considered optimal for the user based on the generated future image.

[0846] "Means of dialogue" refers to systems such as AI dialogue bots used to communicate with users in natural language.

[0847] The present invention provides a system that predicts a user's future purchasing trends and recommends optimal products. The system includes a method for acquiring and analyzing basic information and conversation content, and generating product recommendations based on the information.

[0848] System Configuration

[0849] The system includes:

[0850] 1. User device (smartphone, tablet, etc.)

[0851] 2. Server

[0852] 3. AI conversation bot

[0853] Enter basic information

[0854] When a user first accesses the system, they log in and enter basic information, including their name, age, gender, educational institution, hobbies, interests, strengths and weaknesses. The device acquires this information and sends it to the server, which then stores it in a database.

[0855] Initiating a dialogue session and gathering information

[0856] The user clicks the "Start Dialogue" button to begin a dialogue session. The device launches an AI dialogue bot session, and the user and the AI ​​dialogue bot converse in natural language. The content of the dialogue is recorded in real time. The device sends the content of the dialogue (conversation log) to the server, which stores this information in a database.

[0857] Analyzing data and generating future vision

[0858] The server analyzes the user's basic information and conversations from the past, using a machine learning algorithm to consider a wide range of data points, such as career aptitude, residential choice, lifestyle, areas of study, and personality traits that need attention. Based on the analysis results, the server generates a future profile for the user.

[0859] Generate product recommendations

[0860] Based on the generated future image, the server generates optimal product recommendations for the user, including products that take into account the user's future preferences and purchasing trends.

[0861] Providing advice

[0862] The server generates product recommendations and sends them to the terminal, which then presents them to the user, allowing the user to purchase the most suitable products for their future.

[0863] Specific examples

[0864] As a concrete example, consider the case where a user accesses the system. When logging in for the first time, the user enters his name "Taro," age "30," gender "male," educational institution "university," hobbies "running, reading," interests "health, technology," strengths "planning, analysis," and weaknesses "socializing." Next, the user starts an interactive session and asks the AI ​​conversation bot, "I'm looking for a health-related product. I want something that will help me with my running, which I've recently started." This information is sent to the server and saved.

[0865] The server analyzes this information and suggests items to the user, such as the latest fitness tracking devices and running shoes, that will be useful for running. These suggestions are then presented to the user via their device.

[0866] Example prompt sentence:

[0867] "I'm looking for health and wellness products that will help me with my yoga practice, which I've recently started."

[0868] This makes it possible for the system of the present invention to predict the user's future purchasing trends and to suggest attractive and highly relevant products.

[0869] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0870] Step 1:

[0871] Enter basic information

[0872] When a user first accesses the system, they log in and enter basic information such as their name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, etc. This information is sent via the terminal to the server, which then stores the received basic information in a database.

[0873] Input data: User's basic information (name, age, gender, educational institution, hobbies, interests, strengths and weaknesses)

[0874] Output data: Basic information stored on the server

[0875] Step 2:

[0876] Starting an interactive session

[0877] The user clicks the "Start Dialogue" button to start a dialogue session. This action causes the device to launch an AI dialogue bot session. The user and the AI ​​dialogue bot converse in natural language, and the content of the dialogue is recorded in real time. The recorded content of the dialogue is sent from the device to the server, which stores this information in a database.

[0878] Input data: User clicks and interactions

[0879] Output data: Dialogue content saved on the server

[0880] Step 3:

[0881] Analyzing the data

[0882] The server then integrates the accumulated basic information and conversational content and analyzes it using machine learning algorithms. This analysis takes into account data points such as career aptitude, residential location, lifestyle, areas of study, and personality traits that need attention. The analysis results in a future image of the user.

[0883] Input data: Basic information and dialogue content stored on the server

[0884] Output data: Generated future image

[0885] Step 4:

[0886] Generate product recommendations

[0887] The server generates optimal product recommendations for the user based on the generated future image, using a generative AI model to select products that take into account the user's future preferences and purchasing trends.

[0888] Input data: Generated future image

[0889] Output data: Generated product recommendations

[0890] Step 5:

[0891] Providing advice

[0892] The server sends the generated product recommendations to the terminal, which then presents them to the user, allowing the user to purchase the most suitable products for their future.

[0893] Input data: Generated product recommendations

[0894] Output data: Product recommendations displayed on the device

[0895] 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.

[0896] This invention is a system that acquires basic information from a user, engages in dialogue with the user, analyzes the content of the dialogue and emotions, generates a vision of the user's future, and provides specific advice. This system is implemented using a user terminal, a server, an AI dialogue bot, and an emotion engine.

[0897] User input of basic information

[0898] When a user first accesses the system, he or she logs in and enters basic information, including name, age, gender, school, hobbies, interests, strengths and weaknesses.

[0899] The device acquires this basic information and sends it to the server, which then stores it in a database.

[0900] Initiating interactive sessions and collecting user information

[0901] The user clicks the "Start Dialogue" button to start a dialogue session. The device launches an AI dialogue bot session, and the user and the AI ​​dialogue bot converse in natural language. The dialogue is recorded in real time.

[0902] Analysis by emotion engine

[0903] During an interaction session, the device analyzes the user's emotions using an emotion engine, which analyzes the user's voice tone, facial expressions, text input, etc. to detect the user's emotional state.

[0904] The terminal transmits the collected dialogue content and emotion analysis results to a server, which stores this information in a database.

[0905] Analyzing data and generating future vision

[0906] The server integrates and analyzes the basic information, dialogue content, and sentiment analysis results obtained from the user, using machine learning algorithms to extract the user's tendencies, interests, and emotional state.

[0907] Based on the analysis results, the server generates a future image of the user, including occupation, place of residence, lifestyle, areas of study, personality traits to be aware of, etc. The generated future image and specific advice are customized based on the emotion analysis results obtained from the emotion engine.

[0908] Providing advice

[0909] The server transmits the generated future image and advice to the terminal, which then presents it to the user.

[0910] As a concrete example, consider the case where a junior high school student user accesses the system and enters basic information when logging in for the first time. The basic information includes name "Mr. B," age "14 years old," gender "male," school "junior high school," hobbies "music, basketball," interests "computer science," strengths "programming," and weaknesses "history."

[0911] A conversation session begins, and as the conversation progresses with the AI ​​conversation bot, the user expresses concern about their future career path. During the conversation, the emotion engine analyzes the user's tone of voice and facial expressions to detect anxiety or stress. This information is sent to the server and stored.

[0912] The server analyzes this information and, based on the user's interest in computer science and their strengths in programming, predicts a future career of "software engineer." Taking into account the anxiety expressed in the emotion analysis results, the server offers advice on setting aside time for relaxation and hobbies, as well as specific study methods for improving programming skills.

[0913] The generated future image and advice are presented to the user via the terminal, helping the user to concretely envision their future. In this way, the present invention provides a concrete future image and action plan that meets the individual needs and feelings of the user.

[0914] The processing flow will be explained below.

[0915] Step 1:

[0916] A user logs in to an application. The application provides a login screen, and the user enters their account information to pass authentication.

[0917] Step 2:

[0918] When a user first uses the device, they enter basic information, including their name, age, gender, school, hobbies, interests, strengths, and weaknesses. Once the information is entered, the device stores this information locally.

[0919] Step 3:

[0920] The device sends basic information stored locally to the server, which receives this information and stores it in a database.

[0921] Step 4:

[0922] The user starts a conversation session by clicking the "Start conversation" button. The device launches an AI conversation bot session, and the user and the AI ​​conversation bot converse in natural language.

[0923] Step 5:

[0924] During an interaction session, the terminal analyzes the user's emotions using an emotion engine, which analyzes voice tone, facial expressions, text input, etc. to detect the user's emotional state.

[0925] Step 6:

[0926] The user engages in a conversation with an AI conversation bot. The AI ​​conversation bot responds to the user's questions and records the content in real time. The device temporarily saves the collected conversation content (conversation log).

[0927] Step 7:

[0928] The device sends the content of the conversation and the results of emotion analysis to the server, which receives this information and stores it in a database.

[0929] Step 8:

[0930] The server integrates and analyzes the basic information, conversation content, and emotion analysis results obtained from the user, using machine learning algorithms to extract the user's tendencies, interests, and emotional state.

[0931] Step 9:

[0932] Based on the analysis results, the server generates a future image of the user, including occupation, place of residence, lifestyle, fields of study, personality traits to be aware of, etc. The generated future image and specific advice are customized based on the results of the sentiment analysis.

[0933] Step 10:

[0934] The server sends the generated future image and advice to the terminal, which receives this information and presents it to the user.

[0935] As a concrete example, consider the case where a junior high school student user accesses the system and enters basic information when logging in for the first time. The basic information includes name "Mr. B," age "14 years old," gender "male," school "junior high school," hobbies "music, basketball," interests "computer science," strengths "programming," and weaknesses "history."

[0936] A conversation session begins, and as the conversation progresses with the AI ​​conversation bot, the user expresses concern about their future career path. During the conversation, the emotion engine analyzes the user's tone of voice and facial expressions to detect anxiety or stress. This information is sent to the server and stored.

[0937] The server analyzes this information and, based on the user's interest in computer science and their strengths in programming, predicts a future career of "software engineer." Taking into account the anxiety expressed in the emotion analysis results, the server offers advice on setting aside time for relaxation and hobbies, as well as specific study methods for improving programming skills.

[0938] The generated future image and advice are presented to the user via the terminal, and the user can receive help in concretely drawing out their future image.

[0939] Example 2

[0940] 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."

[0941] Conventional dialogue systems generate future visions based only on the user's basic information and the content of the dialogue, making it difficult to provide advice that fully reflects the user's emotions and psychological state. Furthermore, due to the lack of emotion analysis functionality, there was an issue of being unable to properly evaluate emotions such as anxiety and stress that the user is experiencing.

[0942] 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.

[0943] In this invention, the server includes means for acquiring basic information from a user, means for transmitting the basic information to the server, means for engaging in a dialogue with the user and recording the dialogue content and emotion analysis results, means for transmitting the dialogue content and emotion analysis results to the server, means for the server to store the basic information, dialogue content, and emotion analysis results, means for the server to analyze the basic information, dialogue content, and emotion analysis results and generate a future image, and means for presenting the generated future image and specific advice to the user. This makes it possible to generate more specific and personalized future images and advice that reflect the user's emotions and psychological state.

[0944] "User" refers to an individual who uses the system to enter basic information and engage in interactive sessions.

[0945] "Basic Information" refers to personal information entered by a User into the System, including name, age, gender, educational institution, recreational activities, interests, strengths and weaknesses.

[0946] "Server" refers to a central processing unit that accumulates and analyzes basic information, dialogue content, and emotion analysis results sent by users, and generates future visions and advice.

[0947] An "interactive session" refers to the process in which a user converses with an AI interactive bot in natural language on the system, and the content of the conversation is recorded and analyzed.

[0948] "Emotion analysis" refers to the process of analyzing a user's voice tone, facial expressions, and text input to detect their emotional state in real time.

[0949] "Future image" refers to the future image of the user, including their occupation, place of residence, lifestyle, areas of study, personality traits, etc., generated by the server based on the analysis results.

[0950] "Advice" refers to recommendations such as specific action plans and study methods based on the generated future vision and the results of emotional analysis.

[0951] "Machine learning algorithm" refers to an algorithm that analyzes patterns and trends and generates future visions based on basic information, dialogue content, and emotion analysis results obtained by the server from users.

[0952] This invention is a system that acquires basic information from a user, generates a future image of the user using data collected through dialogue with an AI dialogue bot and emotion analysis results, and provides specific advice. This system is implemented using a user terminal, a server, an AI dialogue bot, and an emotion engine.

[0953] Hardware and Software

[0954] This system uses the following hardware and software:

[0955] User terminal: A personal computer, smartphone, tablet, etc., which functions as a basic information input and interaction interface.

[0956] Server: A central processing unit that stores data, analyzes it, and generates future images.

[0957] AI dialogue bot: A dialogue system using a natural language processing engine.

[0958] Emotion Engine: A software engine that analyzes voice tone, facial expressions, and text input to detect emotional states.

[0959] Program processing explanation

[0960] Obtaining basic information

[0961] When a user first accesses the system, they log in and enter basic information such as their name, age, gender, educational institution, recreational activities, interests, strengths and weaknesses, etc. The terminal acquires this basic information and sends it to the server, which then stores it in a database.

[0962] Starting an interactive session

[0963] When the user clicks the "Start Dialogue" button, the device launches an AI conversation bot session. The user and the AI ​​conversation bot converse in natural language, and the conversation is recorded in real time.

[0964] Analysis by emotion engine

[0965] During a conversation session, the device uses an emotion engine to analyze the user's voice tone, facial expressions, and text input to detect their emotional state. Based on the analysis, the device classifies their mood and psychological state. The device then sends the collected conversation content and emotion analysis results to a server, which stores this information in a database.

[0966] Data integration and analysis

[0967] The server combines the basic information, dialogue content, and sentiment analysis results obtained from the user and analyzes them using a machine learning algorithm. This analysis extracts the user's tendencies, interests, and emotional state, and generates a future image based on this. The generated future image includes occupation, place of residence, lifestyle, areas of study, personality traits, etc. The generated future image and specific advice are customized based on the sentiment analysis results.

[0968] Providing advice

[0969] The server transmits the generated future image and advice to the terminal, which then presents it to the user.

[0970] Specific examples

[0971] Consider a case where a junior high school student uses the system. The user logs in to the system for the first time and enters basic information, including name "Mr. B," age "14," gender "male," educational institution "junior high school," recreational activities "music, basketball," interests "computer science," areas of expertise "programming," and areas of expertise "history." The user begins a dialogue session and tells the AI ​​dialogue bot, "I'm worried about my future career path." During the dialogue, the emotion engine analyzes the voice tone and facial expressions to detect anxiety. This data is sent to and stored on the server. The server analyzes this information and generates a future image, such as "software engineer," and customizes specific advice on relaxation and study methods based on the emotion analysis results. The generated future image and advice are then displayed on the device.

[0972] Generative AI Models and Prompts

[0973] Example of an input prompt for a generative AI model:

[0974] Basic information obtained from the user: name "Mr. B", age "14 years old", gender "male", educational institution "junior high school", recreational activities "music, basketball", interests "computer science", areas of expertise "programming", areas of weakness "history".

[0975] Dialogue content: "I'm worried about my future career path"

[0976] Emotion analysis results: "I feel anxious and stressed"

[0977] Based on this information, I would like you to generate a vision for Mr. B's future and specific advice.

[0978] In this way, the system of the present invention makes it possible to provide a specific vision of the future and a plan of action that meets the individual needs and feelings of the user.

[0979] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0980] Step 1: Enter basic information

[0981] When a user accesses the system for the first time, they log in. As input, they enter their name, age, gender, educational institution, recreational activities, interests, strengths and weaknesses. The terminal acquires this basic information and sends it to the server. As output, the basic information is correctly sent to the server. Specifically, this involves entering data into an input form and pressing the "Submit" button.

[0982] Step 2: Save your basic information

[0983] The server receives basic information and stores it in a database. As input, it requires basic information sent from the device. As output, it stores the basic information in a database. During this process, the server uses a database access library to persist the data. Specific operations include connecting to the database and executing SQL queries.

[0984] Step 3: Starting an interactive session

[0985] The user clicks the "Start Dialogue" button. The user's click is required as input. The device launches an AI dialogue bot session. As output, the dialogue bot is launched and ready for dialogue. Specific actions include launching the dialogue bot and updating the user interface.

[0986] Step 4: Continuing the conversation

[0987] The user and the AI ​​conversation bot converse in natural language. The user's voice or text input is required as input. The device records the conversation in real time. The conversation is output as text. Specific operations include converting voice data to text and saving the text data.

[0988] Step 5: Perform sentiment analysis

[0989] During an interaction session, the device uses an emotion engine to analyze the user's voice tone, facial expressions, and text input to detect their emotional state. The inputs required are the user's voice data, facial expression data, and text data. The output is an emotion analysis result. Specific operations include the use of voice analysis software, facial recognition software, and text analysis algorithms.

[0990] Step 6: Sending dialogue content and sentiment analysis results

[0991] The terminal sends the collected dialogue content and emotion analysis results to the server. The dialogue content and emotion analysis results are required as input. The dialogue content and emotion analysis results are sent to the server as output. Specific operations include creating a data package and sending it over the network.

[0992] Step 7: Data accumulation

[0993] The server accumulates the dialogue content and emotion analysis results in a database. As input, the dialogue content and emotion analysis results sent from the device are required. As output, the dialogue content and emotion analysis results are saved in the database. Specific operations include connecting to the database and executing SQL queries.

[0994] Step 8: Data synthesis and analysis

[0995] The server integrates basic information, dialogue content, and sentiment analysis results and analyzes them using machine learning algorithms. Basic information, dialogue content, and sentiment analysis results are required as input. As output, the user's tendencies, interests, and emotional state are extracted, and a future image is generated. Specific operations include data preprocessing, feature extraction, and the application of machine learning models.

[0996] Step 9: Generate vision and advice

[0997] The server generates a future image based on the analysis results and customizes specific advice based on the sentiment analysis results. The data analysis results are required as input. The future image and specific advice are generated as output. Specific operations include using a generative AI model and generating prompt sentences.

[0998] Step 10: Providing advice

[0999] The server sends the generated future image and advice to the terminal. The generated future image and advice are required as input. The future image and advice are presented to the user as output. Specific operations include network transmission and updating of the user interface.

[1000] (Application example 2)

[1001] 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."

[1002] Conventional user assistance systems present a vision of the future by analyzing a user's basic information and dialogue. However, these systems do not take into account the user's transaction history or purchasing patterns, and therefore have the problem of being unable to provide specific advice, particularly in financial matters. As a result, they are unable to predict the user's future financial situation and provide specific support based on that prediction. To solve these problems, the present invention aims to provide a system that predicts the user's future financial situation based on transaction history and purchasing patterns and provides individually customized financial advice.

[1003] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring basic information from the user; means for transmitting the basic information to the server; means for engaging in a dialogue with the user and recording the dialogue; means for transmitting the dialogue to the server; means for the server to store the basic information and the dialogue; means for the server to analyze the basic information and the dialogue and generate a future image; means for presenting the generated future image to the user; means for acquiring a transaction history and a purchasing pattern and transmitting them to the server; means for predicting a future financial situation based on the transaction history and purchasing pattern; and means for generating financial advice based on the prediction results and presenting it to the user. This makes it possible to specifically predict a user's future financial situation and provide individually customized financial advice.

[1004] "Basic Information" refers to personal information or profile information about a user, including name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, income, daily expenses, etc.

[1005] A "server" is a computer system that stores, analyzes, and manages information sent by users.

[1006] "Dialogue content" refers to a record of the conversation between the user and the AI ​​dialogue bot.

[1007] "Transaction history" is a record of a user's past financial transactions and purchasing activities.

[1008] "Purchase patterns" are data that indicate the trends and characteristics of a user's purchasing behavior.

[1009] "Future image" is a prediction about the user's future lifestyle, occupation, etc., generated by the server by analyzing basic information and the content of the conversation.

[1010] "Financial situation" refers to the user's predicted future financial situation and asset status.

[1011] "Financial Advice" means specific financial assistance or advice based on a user's current financial data and projected future financial situation.

[1012] "Customization" refers to optimizing services and advice to suit the user's individual needs and circumstances.

[1013] This invention is a system that provides individually customized financial advice by acquiring basic information from users, conducting dialogue and emotion analysis, and predicting future financial situations based on transaction history and purchasing patterns. The system consists of a user terminal, a server, an AI dialogue bot, an emotion engine, and a database.

[1014] Hardware and Software Configuration

[1015] Hardware: The user device is a smartphone.

[1016] software:

[1017] AI conversation bot: Uses large-scale language models such as GPT-3.5 and later.

[1018] Emotion engine: Uses IBM Watson Tone Analyzer.

[1019] Database: Use Amazon RDS.

[1020] Frontend: Built with React Native.

[1021] Backend: Built with Node.js and Express.

[1022] Entering and submitting user information

[1023] When a user logs in for the first time, they enter basic information such as their name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, income, and daily expenses. This basic information is sent from the user's device to the server and stored in a database.

[1024] Starting and Recording an Interactive Session

[1025] To start a conversation, the user taps the "Start conversation" button. The user's device launches an AI conversation bot session and engages in a conversation in natural language. The conversation is recorded in real time and sent to the server.

[1026] Emotion analysis

[1027] During the interaction session, the user's device analyzes the user's emotions using an emotion engine. The emotion engine analyzes the voice tone and text input to detect the user's emotional state (e.g., stress, anxiety). The analysis results are also sent to the server and stored in a database.

[1028] Transaction data collection and analysis

[1029] The system captures the user's transaction history and purchasing patterns across electronic payment services and sends them to a server, which uses this data to analyze the user's financial habits.

[1030] Generate future financial forecasts and advice

[1031] The server integrates and analyzes basic information, conversation content, sentiment analysis results, and transaction history. This analysis uses machine learning algorithms to predict the user's future financial situation. Based on this prediction, the server generates individually customized financial advice and sends it to the user's device.

[1032] Specific examples

[1033] For example, when a 25-year-old user uses the "Future Wallet" app, they enter their income and daily expenses when they first log in. If they ask the AI ​​conversation bot, "I've been worried about rent and food lately," the emotion engine will detect the anxiety and analyze it against past transaction data. The system will then suggest specific ways to cut down on unnecessary spending and provide advice on achieving future savings goals.

[1034] Prompt Sentence Examples

[1035] text

[1036] Based on the past six months of transaction data and the user's conversations, predict his or her future financial situation and provide the best savings plan and investment strategy. The user is 25 years old and spends the same amount on rent and food every month, but based on recent conversations, he is worried about his messy spending.

[1037] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1038] Step 1:

[1039] When a user logs in for the first time, they enter basic information such as name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, income, and daily expenses. The device collects this information and sends it to the server. The server stores the received basic information in a database. In this step, the input is provided by the user and the output is the basic information stored in the database.

[1040] Step 2:

[1041] The user clicks the "Start Dialogue" button to start a dialogue session. The device launches an AI dialogue bot session and begins the dialogue. The user and the AI ​​dialogue bot converse in natural language, and the content of the dialogue is recorded in real time. The content of the dialogue is collected as data and sent from the device to the server. The server stores this data in a database. The input is the dialogue content, and the output is the dialogue content saved in the database.

[1042] Step 3:

[1043] During an interactive session, the device uses an emotion engine to analyze the user's emotions. The emotion engine analyzes voice tone and text input to detect the user's emotional state (e.g., stress, anxiety) in real time. The results of this analysis are collected as data and sent from the device to a server. The server stores this emotion data in a database. The input is the user's voice and text data, and the output is analyzed emotion data.

[1044] Step 4:

[1045] The terminal acquires the user's transaction history and purchasing patterns for electronic payment services and sends them to the server. The server stores the received transaction data in a database. The input is the transaction history and purchasing patterns, and the output is the transaction data stored in the database.

[1046] Step 5:

[1047] The server integrates the basic information, dialogue content, emotional data, and transaction data stored in the database and performs analysis using machine learning algorithms. The server predicts the user's future financial situation and generates specific financial advice such as savings goals and investment strategies. This prediction is made using a generative AI model. The input is the integrated data set, and the output is the predicted future financial situation and generated financial advice.

[1048] Step 6:

[1049] The server sends the generated financial advice to the user terminal, which presents it to the user for viewing. The input is the financial advice sent by the server, and the output is the advice presented to the user.

[1050] The above is a specific flow of processing in the system that realizes the application example, and its explanation.

[1051] 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.

[1052] 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.

[1053] 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.

[1054] [Fourth embodiment]

[1055] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1056] 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.

[1057] 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).

[1058] 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.

[1059] 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.

[1060] 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).

[1061] 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.

[1062] 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.

[1063] 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.

[1064] 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.

[1065] 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.

[1066] 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.

[1067] 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."

[1068] This invention acquires basic information from users, analyzes that information, generates a vision of the user's future, and provides specific advice. This system is implemented using a user terminal, a server, and an AI conversation bot.

[1069] User input of basic information

[1070] When a user first accesses the system, he or she logs in and enters basic information, including name, age, gender, school, hobbies, interests, strengths and weaknesses.

[1071] The device acquires this basic information and sends it to the server, which then stores it in a database.

[1072] Initiating interactive sessions and collecting user information

[1073] The user clicks the "Start Dialogue" button to start a dialogue session. The device launches an AI dialogue bot session, and the user and the AI ​​dialogue bot converse in natural language. The dialogue is recorded in real time.

[1074] The terminal sends the conversation content (conversation log) to the server, which stores this information in a database.

[1075] Analyzing data and generating future vision

[1076] The server integrates and analyzes the user's basic information and conversation history, using machine learning algorithms to consider a wide range of data points, including career aptitude, residential choice, lifestyle, areas of study, and personality traits.

[1077] Based on the analysis results, the server generates a future vision and specific advice for the user, which concretely shows the user's current situation and future prospects.

[1078] Providing advice

[1079] The server transmits the generated future image and advice to the terminal, which then presents it to the user.

[1080] As a concrete example, consider the case where a junior high school student user accesses the system. When logging in for the first time, the user enters the following: name "Ms. A," age "14," gender "female," school "junior high school," hobbies "reading, basketball," interests "medical field, literature," strengths "English," and weaknesses "math."

[1081] Next, let's say you start a dialogue session and ask the AI ​​bot for advice about your future career. During the conversation, it becomes clear that you're interested in the medical field but don't know what to study specifically. This information is sent to the server and stored.

[1082] The server analyzes this information and generates specific advice, such as, "To enter the medical field, it is important to learn the basics of biology and chemistry, so it would be a good idea to choose a nearby high school that offers a good range of these subjects." This advice is then presented to the user via their device, helping them to concretely envision their future.

[1083] As a result, the present invention can alleviate users' anxieties about the future and show them a concrete path to achieving their goals.

[1084] The processing flow will be explained below.

[1085] Step 1:

[1086] A user logs in to an application. The application provides a login screen, and the user enters their account information to pass authentication.

[1087] Step 2:

[1088] When a user first uses the device, they enter basic information, including their name, age, gender, school, hobbies, interests, strengths, and weaknesses. Once the information is entered, the device stores this information locally.

[1089] Step 3:

[1090] The device sends basic information stored locally to the server, which receives this information and stores it in a database.

[1091] Step 4:

[1092] The user starts a conversation session by clicking the "Start conversation" button. The device launches an AI conversation bot session, and the user and the AI ​​conversation bot converse in natural language.

[1093] Step 5:

[1094] The user engages in a conversation with an AI conversation bot. The AI ​​conversation bot responds to the user's questions and records the content in real time. The device temporarily saves the collected conversation content (conversation log).

[1095] Step 6:

[1096] The device sends the collected conversation content to a server, which receives this information and stores it in a database.

[1097] Step 7:

[1098] The server aggregates the basic information and conversation content obtained from the user and performs data analysis, using machine learning algorithms to extract user trends and interests.

[1099] Step 8:

[1100] Based on the analysis results, the server generates a future profile of the user, including their occupation, place of residence, lifestyle, field of study, and personality traits they should pay attention to.

[1101] Step 9:

[1102] The server sends the generated future vision and specific advice to the terminal, which receives this information and presents it to the user.

[1103] Step 10:

[1104] The user can view the proposed future vision and advice, plan the next dialogue session, and set corresponding study plans and lifestyles as needed.

[1105] Example 1

[1106] 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."

[1107] In modern society, it is important for individuals to clearly visualize their future, but this requires the collection and analysis of a large amount of information. However, in order for individuals to concretely visualize their future, they must effectively collect a wide variety of information and conduct specialized analysis. This process takes a great deal of time and effort, making it difficult for average users. For this reason, it is necessary to provide a system that allows users to have a clear vision of their future and receive specific advice based on that vision.

[1108] 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.

[1109] In this invention, the server includes means for acquiring basic information from a user, means for transmitting the basic information to the server via a communication terminal, means for the user and the AI ​​conversational bot to dialogue and record the dialogue content, means for transmitting the dialogue content to the server via the communication terminal, means for the server to store the basic information and the dialogue content in a database, means for the server to analyze the basic information and the dialogue content using a machine learning algorithm and generate a future image, and means for presenting the generated future image and advice to the user's communication terminal. This allows users to receive real-time analysis based on their basic information and the dialogue content, and easily obtain specific future images and advice.

[1110] A "user" is an individual who uses the system to input their basic information and receives future vision and advice through dialogue with an AI conversational bot.

[1111] "Basic information" refers to information about the user, such as name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses.

[1112] A "communication terminal" is an electronic device used by a user to input basic information, interact with an AI conversational bot, and exchange information with a server.

[1113] A "server" is a central processing unit that receives basic information and conversational content from users, stores this information, analyzes it, and provides the results to users.

[1114] An "AI conversation bot" is a system that uses artificial intelligence technology to converse with users in natural language and record the content of that conversation.

[1115] A "database" is a data storage system within a server that stores basic information about users and the contents of their interactions.

[1116] A "machine learning algorithm" is an artificial intelligence technique used to analyze a user's basic information and dialogue content to generate a future image.

[1117] The "future image" is a specific vision of the user's future that is generated by the server based on the user's basic information and the content of the dialogue.

[1118] "Advice" is a specific course of action or recommendation provided to the user based on the future image generated by the server.

[1119] This invention is a system that acquires basic information from users, analyzes that information, creates a future image for the user, and provides specific advice. This system is implemented using a user terminal, a server, and an AI conversation bot.

[1120] User input of basic information

[1121] When a user first accesses the system, they log in and enter basic information such as their name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses, etc. This information is provided, for example, through an input form on a web browser.

[1122] The device acquires basic information entered by the user and converts it into a specified data format (string, integer, list, etc.). This device includes PCs, smartphones, tablets, etc.

[1123] The device sends the converted basic information to the server using the HTTPS protocol, and the transmitted data is encrypted using SSL / TLS.

[1124] The basic information received by the server is decoded by the security module and passed to the database module for storage. The database is a relational database such as MySQL or PostgreSQL.

[1125] Initiating interactive sessions and collecting user information

[1126] A user clicks the "Start conversation" button to start a conversation with the AI ​​bot. The conversation interface can be a web browser-based chat window or a mobile app.

[1127] The device launches an AI conversation bot session, and the user and the AI ​​conversation bot converse in natural language. The AI ​​conversation bot uses natural language processing technologies such as Google Dialogflow and IBM Watson Assistant.

[1128] Users ask questions or ask inquiries in everyday language, and the content is recorded and converted into text in real time.

[1129] The device records and converts the conversation into text in real time and periodically sends it to the server, using real-time communication technologies such as WebSocket.

[1130] The server stores the conversation content it receives in a database and uses it for later analysis.

[1131] Analyzing data and generating future vision

[1132] The server uses a scheduled task manager to launch tasks to analyze the data in real time, using Python machine learning libraries (such as Scikit-learn and TensorFlow).

[1133] The server aggregates the user's basic information and past interactions and analyzes them with machine learning algorithms, taking into account a wide range of data points, including career aptitude, residential choice, lifestyle, areas of study, and personality.

[1134] Based on the analysis results, the server generates a future vision and specific advice for the user, using a natural language generation model (e.g., GPT-3).

[1135] Providing advice

[1136] The server sends the generated future image and advice to the user's communication device via a REST API, with the data sent in JSON format.

[1137] The advice received by the device is displayed in the user interface, and graphs and charts can be used to make it easier to understand visually.

[1138] The user can review the advice provided and decide on the next course of action.

[1139] Specific examples

[1140] Consider the case where a junior high school student user first accesses the system. This user enters his / her name "Ms. A," his / her age "14 years old," his / her gender "female," his / her school "junior high school," his / her hobbies "reading, basketball," his / her interests "medical field, literature," his / her strengths "English," and his / her weaknesses "math."

[1141] Next, the user consults the AI ​​conversation bot about "worried about their future career." During the conversation, it becomes clear that the user is interested in the medical field but doesn't know what to study specifically.

[1142] The server analyzes this information and generates specific advice, such as, "To enter the medical field, it is important to learn the basics of biology and chemistry, so it would be a good idea to choose a nearby high school that offers a good range of these subjects."

[1143] An example of a prompt for a generative AI model is: "A, a 14-year-old middle school student, says she wants to go into the medical field but doesn't know what to study. What advice should you give her?"

[1144] In this way, the present invention can reduce the user's anxiety about their future and clarify the path to achieving specific goals.

[1145] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1146] Step 1:

[1147] When a user accesses the system for the first time, they enter the necessary authentication information on the login screen. If the login is successful, they proceed to the basic information input screen. The user enters basic information such as their name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses. The basic information entered is displayed on the terminal as a confirmation screen, and after the user confirms it, it is converted into a data format and a data set for transmission is generated. The input data is in the form of a string, integer, list, etc.

[1148] Input: Name, age, gender, educational institution, preferences, areas of interest, strengths and weaknesses

[1149] Output: Dataset with basic information transformed

[1150] Step 2:

[1151] The terminal sends the converted basic information to the server using the HTTPS protocol. The transmitted data is encrypted using SSL / TLS. The data received by the server is decoded by the security module and passed to the database module for storage. The database stores the information of each individual user after properly indexing it. The database used is, for example, MySQL or PostgreSQL.

[1152] Input: Dataset with basic information transformed

[1153] Output: Basic information stored in the database

[1154] Step 3:

[1155] A session with the AI ​​conversational bot begins when the user clicks the "Start conversation" button. The conversation interface is a web browser-based chat window or a mobile app. The device launches the AI ​​conversational bot session, and the AI ​​conversational bot converses with the user in natural language. The conversation is recorded and converted into text in real time.

[1156] Input: Request to start a conversation

[1157] Output: Start of a conversation session with the AI ​​conversation bot

[1158] Step 4:

[1159] The user asks questions and provides advice to the AI ​​conversational bot in everyday language. For example, they might ask, "I'm worried about my future career," or "I'm interested in the medical field, but I don't know what to study." The device converts the conversation into text in real time and records it. The conversation is sent to the server every five seconds. Real-time communication technologies such as WebSocket are used.

[1160] Input: User interaction

[1161] Output: Real-time transcript of conversation

[1162] Step 5:

[1163] The server stores the received conversation content in a database. The conversation content is saved in text format and used for later analysis. The database saves the conversation content with a timestamp, recording when and what was discussed.

[1164] Input: Real-time transcription of conversation records

[1165] Output: Interaction records stored in a database

[1166] Step 6:

[1167] The server uses a scheduled task manager to launch tasks that analyze data in real time. Python machine learning libraries (such as Scikit-learn and TensorFlow) are used for the analysis. The server combines the user's past basic information and conversations and analyzes them using machine learning algorithms. This includes career aptitude, choice of residence, lifestyle, areas of study, and personality.

[1168] Input: Basic information and conversation records

[1169] Output: Analysis results

[1170] Step 7:

[1171] The server generates a future vision and specific advice for the user based on the analysis results. This generation uses a natural language generation model (e.g., GPT-3). The generated future vision and advice are saved in text format.

[1172] Input: Analysis results

[1173] Output: Generated future vision and advice

[1174] Step 8:

[1175] The server sends the generated future image and advice to the user's device. The data is sent via a REST API in JSON format. The device displays the advice received in a user interface. Graphs and charts can also be used for visual clarity.

[1176] Input: Generated future vision and advice

[1177] Output: Advice displayed in the user interface

[1178] Specific examples

[1179] When a junior high school student user first accesses the system, they enter their name "Ms. A," age "14," gender "female," school "junior high school," hobbies "reading, basketball," interests "medical field, literature," strengths "English," and weaknesses "math." Next, the user consults the AI ​​conversation bot, saying, "I'm worried about my future career." During the conversation, it becomes clear that the user is interested in the medical field but doesn't know what to study specifically. The server analyzes this information and generates specific advice: "To enter the medical field, it is important to learn the basics of biology and chemistry, and it would be a good idea to choose a nearby high school that offers a good range of these subjects." The generated advice is sent to the user's device and displayed.

[1180] An example of a prompt for a generative AI model is: "A, a 14-year-old middle school student, says she wants to go into the medical field but doesn't know what to study. What advice should you give her?"

[1181] (Application example 1)

[1182] 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."

[1183] Most current personalized shopping assistant systems present products based on a user's purchasing history and basic preferences, making it difficult to predict a user's future purchasing trends and recommend optimal products based on those predictions. Furthermore, systems that provide specific advice to users are limited. Therefore, new methods are needed to suggest optimal products for users in a timely manner and improve their purchasing experience.

[1184] 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.

[1185] In this invention, the server includes a means for acquiring basic information from the user, a means for conducting a dialogue and recording the dialogue content, a means for analyzing the basic information and the dialogue content to generate a future image, and a means for generating product recommendations, which makes it possible to predict the user's future preferences and purchasing tendencies and to suggest optimal products based on those predictions.

[1186] "Basic information" refers to personal information provided by the user, such as name, age, gender, educational institution, hobbies, interests, strengths and weaknesses.

[1187] A "server" is a computer system that stores and analyzes basic information and conversation content sent by users.

[1188] "Dialogue content" is a record of the conversation between the user and the system.

[1189] "Future Vision" is a prediction of the user's future occupation, place of residence, lifestyle, areas of study, personality traits, etc., generated by analyzing the user's basic information and conversation content.

[1190] "Product recommendation" is the proposal of products that are considered optimal for the user based on the generated future image.

[1191] "Means of dialogue" refers to systems such as AI dialogue bots used to communicate with users in natural language.

[1192] The present invention provides a system that predicts a user's future purchasing trends and recommends optimal products. The system includes a method for acquiring and analyzing basic information and conversation content, and generating product recommendations based on the information.

[1193] System Configuration

[1194] The system includes:

[1195] 1. User device (smartphone, tablet, etc.)

[1196] 2. Server

[1197] 3. AI conversation bot

[1198] Enter basic information

[1199] When a user first accesses the system, they log in and enter basic information, including their name, age, gender, educational institution, hobbies, interests, strengths and weaknesses. The device acquires this information and sends it to the server, which then stores it in a database.

[1200] Initiating a dialogue session and gathering information

[1201] The user clicks the "Start Dialogue" button to begin a dialogue session. The device launches an AI dialogue bot session, and the user and the AI ​​dialogue bot converse in natural language. The content of the dialogue is recorded in real time. The device sends the content of the dialogue (conversation log) to the server, which stores this information in a database.

[1202] Analyzing data and generating future vision

[1203] The server analyzes the user's basic information and conversations from the past, using a machine learning algorithm to consider a wide range of data points, such as career aptitude, residential choice, lifestyle, areas of study, and personality traits that need attention. Based on the analysis results, the server generates a future profile for the user.

[1204] Generate product recommendations

[1205] Based on the generated future image, the server generates optimal product recommendations for the user, including products that take into account the user's future preferences and purchasing trends.

[1206] Providing advice

[1207] The server generates product recommendations and sends them to the terminal, which then presents them to the user, allowing the user to purchase the most suitable products for their future.

[1208] Specific examples

[1209] As a concrete example, consider the case where a user accesses the system. When logging in for the first time, the user enters his name "Taro," age "30," gender "male," educational institution "university," hobbies "running, reading," interests "health, technology," strengths "planning, analysis," and weaknesses "socializing." Next, the user starts an interactive session and asks the AI ​​conversation bot, "I'm looking for a health-related product. I want something that will help me with my running, which I've recently started." This information is sent to the server and saved.

[1210] The server analyzes this information and suggests items to the user, such as the latest fitness tracking devices and running shoes, that will be useful for running. These suggestions are then presented to the user via their device.

[1211] Example prompt sentence:

[1212] "I'm looking for health and wellness products that will help me with my yoga practice, which I've recently started."

[1213] This makes it possible for the system of the present invention to predict the user's future purchasing trends and to suggest attractive and highly relevant products.

[1214] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1215] Step 1:

[1216] Enter basic information

[1217] When a user first accesses the system, they log in and enter basic information such as their name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, etc. This information is sent via the terminal to the server, which then stores the received basic information in a database.

[1218] Input data: User's basic information (name, age, gender, educational institution, hobbies, interests, strengths and weaknesses)

[1219] Output data: Basic information stored on the server

[1220] Step 2:

[1221] Starting an interactive session

[1222] The user clicks the "Start Dialogue" button to start a dialogue session. This action causes the device to launch an AI dialogue bot session. The user and the AI ​​dialogue bot converse in natural language, and the content of the dialogue is recorded in real time. The recorded content of the dialogue is sent from the device to the server, which stores this information in a database.

[1223] Input data: User clicks and interactions

[1224] Output data: Dialogue content saved on the server

[1225] Step 3:

[1226] Analyzing the data

[1227] The server then integrates the accumulated basic information and conversational content and analyzes it using machine learning algorithms. This analysis takes into account data points such as career aptitude, residential location, lifestyle, areas of study, and personality traits that need attention. The analysis results in a future image of the user.

[1228] Input data: Basic information and dialogue content stored on the server

[1229] Output data: Generated future image

[1230] Step 4:

[1231] Generate product recommendations

[1232] The server generates optimal product recommendations for the user based on the generated future image, using a generative AI model to select products that take into account the user's future preferences and purchasing trends.

[1233] Input data: Generated future image

[1234] Output data: Generated product recommendations

[1235] Step 5:

[1236] Providing advice

[1237] The server sends the generated product recommendations to the terminal, which then presents them to the user, allowing the user to purchase the most suitable products for their future.

[1238] Input data: Generated product recommendations

[1239] Output data: Product recommendations displayed on the device

[1240] 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.

[1241] This invention is a system that acquires basic information from a user, engages in dialogue with the user, analyzes the content of the dialogue and emotions, generates a vision of the user's future, and provides specific advice. This system is implemented using a user terminal, a server, an AI dialogue bot, and an emotion engine.

[1242] User input of basic information

[1243] When a user first accesses the system, he or she logs in and enters basic information, including name, age, gender, school, hobbies, interests, strengths and weaknesses.

[1244] The device acquires this basic information and sends it to the server, which then stores it in a database.

[1245] Initiating interactive sessions and collecting user information

[1246] The user clicks the "Start Dialogue" button to start a dialogue session. The device launches an AI dialogue bot session, and the user and the AI ​​dialogue bot converse in natural language. The dialogue is recorded in real time.

[1247] Analysis by emotion engine

[1248] During an interaction session, the device analyzes the user's emotions using an emotion engine, which analyzes the user's voice tone, facial expressions, text input, etc. to detect the user's emotional state.

[1249] The terminal transmits the collected dialogue content and emotion analysis results to a server, which stores this information in a database.

[1250] Analyzing data and generating future vision

[1251] The server integrates and analyzes the basic information, dialogue content, and sentiment analysis results obtained from the user, using machine learning algorithms to extract the user's tendencies, interests, and emotional state.

[1252] Based on the analysis results, the server generates a future image of the user, including occupation, place of residence, lifestyle, areas of study, personality traits to be aware of, etc. The generated future image and specific advice are customized based on the emotion analysis results obtained from the emotion engine.

[1253] Providing advice

[1254] The server transmits the generated future image and advice to the terminal, which then presents it to the user.

[1255] As a concrete example, consider the case where a junior high school student user accesses the system and enters basic information when logging in for the first time. The basic information includes name "Mr. B," age "14 years old," gender "male," school "junior high school," hobbies "music, basketball," interests "computer science," strengths "programming," and weaknesses "history."

[1256] A conversation session begins, and as the conversation progresses with the AI ​​conversation bot, the user expresses concern about their future career path. During the conversation, the emotion engine analyzes the user's tone of voice and facial expressions to detect anxiety or stress. This information is sent to the server and stored.

[1257] The server analyzes this information and, based on the user's interest in computer science and their strengths in programming, predicts a future career of "software engineer." Taking into account the anxiety expressed in the emotion analysis results, the server offers advice on setting aside time for relaxation and hobbies, as well as specific study methods for improving programming skills.

[1258] The generated future image and advice are presented to the user via the terminal, helping the user to concretely envision their future. In this way, the present invention provides a concrete future image and action plan that meets the individual needs and feelings of the user.

[1259] The processing flow will be explained below.

[1260] Step 1:

[1261] A user logs in to an application. The application provides a login screen, and the user enters their account information to pass authentication.

[1262] Step 2:

[1263] When a user first uses the device, they enter basic information, including their name, age, gender, school, hobbies, interests, strengths, and weaknesses. Once the information is entered, the device stores this information locally.

[1264] Step 3:

[1265] The device sends basic information stored locally to the server, which receives this information and stores it in a database.

[1266] Step 4:

[1267] The user starts a conversation session by clicking the "Start conversation" button. The device launches an AI conversation bot session, and the user and the AI ​​conversation bot converse in natural language.

[1268] Step 5:

[1269] During an interaction session, the terminal analyzes the user's emotions using an emotion engine, which analyzes voice tone, facial expressions, text input, etc. to detect the user's emotional state.

[1270] Step 6:

[1271] The user engages in a conversation with an AI conversation bot. The AI ​​conversation bot responds to the user's questions and records the content in real time. The device temporarily saves the collected conversation content (conversation log).

[1272] Step 7:

[1273] The device sends the content of the conversation and the results of emotion analysis to the server, which receives this information and stores it in a database.

[1274] Step 8:

[1275] The server integrates and analyzes the basic information, conversation content, and emotion analysis results obtained from the user, using machine learning algorithms to extract the user's tendencies, interests, and emotional state.

[1276] Step 9:

[1277] Based on the analysis results, the server generates a future image of the user, including occupation, place of residence, lifestyle, fields of study, personality traits to be aware of, etc. The generated future image and specific advice are customized based on the results of the sentiment analysis.

[1278] Step 10:

[1279] The server sends the generated future image and advice to the terminal, which receives this information and presents it to the user.

[1280] As a concrete example, consider the case where a junior high school student user accesses the system and enters basic information when logging in for the first time. The basic information includes name "Mr. B," age "14 years old," gender "male," school "junior high school," hobbies "music, basketball," interests "computer science," strengths "programming," and weaknesses "history."

[1281] A conversation session begins, and as the conversation progresses with the AI ​​conversation bot, the user expresses concern about their future career path. During the conversation, the emotion engine analyzes the user's tone of voice and facial expressions to detect anxiety or stress. This information is sent to the server and stored.

[1282] The server analyzes this information and, based on the user's interest in computer science and their strengths in programming, predicts a future career of "software engineer." Taking into account the anxiety expressed in the emotion analysis results, the server offers advice on setting aside time for relaxation and hobbies, as well as specific study methods for improving programming skills.

[1283] The generated future image and advice are presented to the user via the terminal, and the user can receive help in concretely drawing out their future image.

[1284] Example 2

[1285] 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."

[1286] Conventional dialogue systems generate future visions based only on the user's basic information and the content of the dialogue, making it difficult to provide advice that fully reflects the user's emotions and psychological state. Furthermore, due to the lack of emotion analysis functionality, there was an issue of being unable to properly evaluate emotions such as anxiety and stress that the user is experiencing.

[1287] 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.

[1288] In this invention, the server includes means for acquiring basic information from a user, means for transmitting the basic information to the server, means for engaging in a dialogue with the user and recording the dialogue content and emotion analysis results, means for transmitting the dialogue content and emotion analysis results to the server, means for the server to store the basic information, dialogue content, and emotion analysis results, means for the server to analyze the basic information, dialogue content, and emotion analysis results and generate a future image, and means for presenting the generated future image and specific advice to the user. This makes it possible to generate more specific and personalized future images and advice that reflect the user's emotions and psychological state.

[1289] "User" refers to an individual who uses the system to enter basic information and engage in interactive sessions.

[1290] "Basic Information" refers to personal information entered by a User into the System, including name, age, gender, educational institution, recreational activities, interests, strengths and weaknesses.

[1291] "Server" refers to a central processing unit that accumulates and analyzes basic information, dialogue content, and emotion analysis results sent by users, and generates future visions and advice.

[1292] An "interactive session" refers to the process in which a user converses with an AI interactive bot in natural language on the system, and the content of the conversation is recorded and analyzed.

[1293] "Emotion analysis" refers to the process of analyzing a user's voice tone, facial expressions, and text input to detect their emotional state in real time.

[1294] "Future image" refers to the future image of the user, including their occupation, place of residence, lifestyle, areas of study, personality traits, etc., generated by the server based on the analysis results.

[1295] "Advice" refers to recommendations such as specific action plans and study methods based on the generated future vision and the results of emotional analysis.

[1296] "Machine learning algorithm" refers to an algorithm that analyzes patterns and trends and generates future visions based on basic information, dialogue content, and emotion analysis results obtained by the server from users.

[1297] This invention is a system that acquires basic information from a user, generates a future image of the user using data collected through dialogue with an AI dialogue bot and emotion analysis results, and provides specific advice. This system is implemented using a user terminal, a server, an AI dialogue bot, and an emotion engine.

[1298] Hardware and Software

[1299] This system uses the following hardware and software:

[1300] User terminal: A personal computer, smartphone, tablet, etc., which functions as a basic information input and interaction interface.

[1301] Server: A central processing unit that stores data, analyzes it, and generates future images.

[1302] AI dialogue bot: A dialogue system using a natural language processing engine.

[1303] Emotion Engine: A software engine that analyzes voice tone, facial expressions, and text input to detect emotional states.

[1304] Program processing explanation

[1305] Obtaining basic information

[1306] When a user first accesses the system, they log in and enter basic information such as their name, age, gender, educational institution, recreational activities, interests, strengths and weaknesses, etc. The terminal acquires this basic information and sends it to the server, which then stores it in a database.

[1307] Starting an interactive session

[1308] When the user clicks the "Start Dialogue" button, the device launches an AI conversation bot session. The user and the AI ​​conversation bot converse in natural language, and the conversation is recorded in real time.

[1309] Analysis by emotion engine

[1310] During a conversation session, the device uses an emotion engine to analyze the user's voice tone, facial expressions, and text input to detect their emotional state. Based on the analysis, the device classifies their mood and psychological state. The device then sends the collected conversation content and emotion analysis results to a server, which stores this information in a database.

[1311] Data integration and analysis

[1312] The server combines the basic information, dialogue content, and sentiment analysis results obtained from the user and analyzes them using a machine learning algorithm. This analysis extracts the user's tendencies, interests, and emotional state, and generates a future image based on this. The generated future image includes occupation, place of residence, lifestyle, areas of study, personality traits, etc. The generated future image and specific advice are customized based on the sentiment analysis results.

[1313] Providing advice

[1314] The server transmits the generated future image and advice to the terminal, which then presents it to the user.

[1315] Specific examples

[1316] Consider a case where a junior high school student uses the system. The user logs in to the system for the first time and enters basic information, including name "Mr. B," age "14," gender "male," educational institution "junior high school," recreational activities "music, basketball," interests "computer science," areas of expertise "programming," and areas of expertise "history." The user begins a dialogue session and tells the AI ​​dialogue bot, "I'm worried about my future career path." During the dialogue, the emotion engine analyzes the voice tone and facial expressions to detect anxiety. This data is sent to and stored on the server. The server analyzes this information and generates a future image, such as "software engineer," and customizes specific advice on relaxation and study methods based on the emotion analysis results. The generated future image and advice are then displayed on the device.

[1317] Generative AI Models and Prompts

[1318] Example of an input prompt for a generative AI model:

[1319] Basic information obtained from the user: name "Mr. B", age "14 years old", gender "male", educational institution "junior high school", recreational activities "music, basketball", interests "computer science", areas of expertise "programming", areas of weakness "history".

[1320] Dialogue content: "I'm worried about my future career path"

[1321] Emotion analysis results: "I feel anxious and stressed"

[1322] Based on this information, I would like you to generate a vision for Mr. B's future and specific advice.

[1323] In this way, the system of the present invention makes it possible to provide a specific vision of the future and a plan of action that meets the individual needs and feelings of the user.

[1324] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1325] Step 1: Enter basic information

[1326] When a user accesses the system for the first time, they log in. As input, they enter their name, age, gender, educational institution, recreational activities, interests, strengths and weaknesses. The terminal acquires this basic information and sends it to the server. As output, the basic information is correctly sent to the server. Specifically, this involves entering data into an input form and pressing the "Submit" button.

[1327] Step 2: Save your basic information

[1328] The server receives basic information and stores it in a database. As input, it requires basic information sent from the device. As output, it stores the basic information in a database. During this process, the server uses a database access library to persist the data. Specific operations include connecting to the database and executing SQL queries.

[1329] Step 3: Starting an interactive session

[1330] The user clicks the "Start Dialogue" button. The user's click is required as input. The device launches an AI dialogue bot session. As output, the dialogue bot is launched and ready for dialogue. Specific actions include launching the dialogue bot and updating the user interface.

[1331] Step 4: Continuing the conversation

[1332] The user and the AI ​​conversation bot converse in natural language. The user's voice or text input is required as input. The device records the conversation in real time. The conversation is output as text. Specific operations include converting voice data to text and saving the text data.

[1333] Step 5: Perform sentiment analysis

[1334] During an interaction session, the device uses an emotion engine to analyze the user's voice tone, facial expressions, and text input to detect their emotional state. The inputs required are the user's voice data, facial expression data, and text data. The output is an emotion analysis result. Specific operations include the use of voice analysis software, facial recognition software, and text analysis algorithms.

[1335] Step 6: Sending dialogue content and sentiment analysis results

[1336] The terminal sends the collected dialogue content and emotion analysis results to the server. The dialogue content and emotion analysis results are required as input. The dialogue content and emotion analysis results are sent to the server as output. Specific operations include creating a data package and sending it over the network.

[1337] Step 7: Data accumulation

[1338] The server accumulates the dialogue content and emotion analysis results in a database. As input, the dialogue content and emotion analysis results sent from the device are required. As output, the dialogue content and emotion analysis results are saved in the database. Specific operations include connecting to the database and executing SQL queries.

[1339] Step 8: Data synthesis and analysis

[1340] The server integrates basic information, dialogue content, and sentiment analysis results and analyzes them using machine learning algorithms. Basic information, dialogue content, and sentiment analysis results are required as input. As output, the user's tendencies, interests, and emotional state are extracted, and a future image is generated. Specific operations include data preprocessing, feature extraction, and the application of machine learning models.

[1341] Step 9: Generate vision and advice

[1342] The server generates a future image based on the analysis results and customizes specific advice based on the sentiment analysis results. The data analysis results are required as input. The future image and specific advice are generated as output. Specific operations include using a generative AI model and generating prompt sentences.

[1343] Step 10: Providing advice

[1344] The server sends the generated future image and advice to the terminal. The generated future image and advice are required as input. The future image and advice are presented to the user as output. Specific operations include network transmission and updating of the user interface.

[1345] (Application example 2)

[1346] 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."

[1347] Conventional user assistance systems present a vision of the future by analyzing a user's basic information and dialogue. However, these systems do not take into account the user's transaction history or purchasing patterns, and therefore have the problem of being unable to provide specific advice, particularly in financial matters. As a result, they are unable to predict the user's future financial situation and provide specific support based on that prediction. To solve these problems, the present invention aims to provide a system that predicts the user's future financial situation based on transaction history and purchasing patterns and provides individually customized financial advice.

[1348] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring basic information from the user; means for transmitting the basic information to the server; means for engaging in a dialogue with the user and recording the dialogue; means for transmitting the dialogue to the server; means for the server to store the basic information and the dialogue; means for the server to analyze the basic information and the dialogue and generate a future image; means for presenting the generated future image to the user; means for acquiring a transaction history and a purchasing pattern and transmitting them to the server; means for predicting a future financial situation based on the transaction history and purchasing pattern; and means for generating financial advice based on the prediction results and presenting it to the user. This makes it possible to specifically predict a user's future financial situation and provide individually customized financial advice.

[1349] "Basic Information" refers to personal information or profile information about a user, including name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, income, daily expenses, etc.

[1350] A "server" is a computer system that stores, analyzes, and manages information sent by users.

[1351] "Dialogue content" refers to a record of the conversation between the user and the AI ​​dialogue bot.

[1352] "Transaction history" is a record of a user's past financial transactions and purchasing activities.

[1353] "Purchase patterns" are data that indicate the trends and characteristics of a user's purchasing behavior.

[1354] "Future image" is a prediction about the user's future lifestyle, occupation, etc., generated by the server by analyzing basic information and the content of the conversation.

[1355] "Financial situation" refers to the user's predicted future financial situation and asset status.

[1356] "Financial Advice" means specific financial assistance or advice based on a user's current financial data and projected future financial situation.

[1357] "Customization" refers to optimizing services and advice to suit the user's individual needs and circumstances.

[1358] This invention is a system that provides individually customized financial advice by acquiring basic information from users, conducting dialogue and emotion analysis, and predicting future financial situations based on transaction history and purchasing patterns. The system consists of a user terminal, a server, an AI dialogue bot, an emotion engine, and a database.

[1359] Hardware and Software Configuration

[1360] Hardware: The user device is a smartphone.

[1361] software:

[1362] AI conversation bot: Uses large-scale language models such as GPT-3.5 and later.

[1363] Emotion engine: Uses IBM Watson Tone Analyzer.

[1364] Database: Use Amazon RDS.

[1365] Frontend: Built with React Native.

[1366] Backend: Built with Node.js and Express.

[1367] Entering and submitting user information

[1368] When a user logs in for the first time, they enter basic information such as their name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, income, and daily expenses. This basic information is sent from the user's device to the server and stored in a database.

[1369] Starting and Recording an Interactive Session

[1370] To start a conversation, the user taps the "Start conversation" button. The user's device launches an AI conversation bot session and engages in a conversation in natural language. The conversation is recorded in real time and sent to the server.

[1371] Emotion analysis

[1372] During the interaction session, the user's device analyzes the user's emotions using an emotion engine. The emotion engine analyzes the voice tone and text input to detect the user's emotional state (e.g., stress, anxiety). The analysis results are also sent to the server and stored in a database.

[1373] Transaction data collection and analysis

[1374] The system captures the user's transaction history and purchasing patterns across electronic payment services and sends them to a server, which uses this data to analyze the user's financial habits.

[1375] Generate future financial forecasts and advice

[1376] The server integrates and analyzes basic information, conversation content, sentiment analysis results, and transaction history. This analysis uses machine learning algorithms to predict the user's future financial situation. Based on this prediction, the server generates individually customized financial advice and sends it to the user's device.

[1377] Specific examples

[1378] For example, when a 25-year-old user uses the "Future Wallet" app, they enter their income and daily expenses when they first log in. If they ask the AI ​​conversation bot, "I've been worried about rent and food lately," the emotion engine will detect the anxiety and analyze it against past transaction data. The system will then suggest specific ways to cut down on unnecessary spending and provide advice on achieving future savings goals.

[1379] Prompt Sentence Examples

[1380] text

[1381] Based on the past six months of transaction data and the user's conversations, predict his or her future financial situation and provide the best savings plan and investment strategy. The user is 25 years old and spends the same amount on rent and food every month, but based on recent conversations, he is worried about his messy spending.

[1382] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1383] Step 1:

[1384] When a user logs in for the first time, they enter basic information such as name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, income, and daily expenses. The device collects this information and sends it to the server. The server stores the received basic information in a database. In this step, the input is provided by the user and the output is the basic information stored in the database.

[1385] Step 2:

[1386] The user clicks the "Start Dialogue" button to start a dialogue session. The device launches an AI dialogue bot session and begins the dialogue. The user and the AI ​​dialogue bot converse in natural language, and the content of the dialogue is recorded in real time. The content of the dialogue is collected as data and sent from the device to the server. The server stores this data in a database. The input is the dialogue content, and the output is the dialogue content saved in the database.

[1387] Step 3:

[1388] During an interactive session, the device uses an emotion engine to analyze the user's emotions. The emotion engine analyzes voice tone and text input to detect the user's emotional state (e.g., stress, anxiety) in real time. The results of this analysis are collected as data and sent from the device to a server. The server stores this emotion data in a database. The input is the user's voice and text data, and the output is analyzed emotion data.

[1389] Step 4:

[1390] The terminal acquires the user's transaction history and purchasing patterns for electronic payment services and sends them to the server. The server stores the received transaction data in a database. The input is the transaction history and purchasing patterns, and the output is the transaction data stored in the database.

[1391] Step 5:

[1392] The server integrates the basic information, dialogue content, emotional data, and transaction data stored in the database and performs analysis using machine learning algorithms. The server predicts the user's future financial situation and generates specific financial advice such as savings goals and investment strategies. This prediction is made using a generative AI model. The input is the integrated data set, and the output is the predicted future financial situation and generated financial advice.

[1393] Step 6:

[1394] The server sends the generated financial advice to the user terminal, which presents it to the user for viewing. The input is the financial advice sent by the server, and the output is the advice presented to the user.

[1395] The above is a specific flow of processing in the system that realizes the application example, and its explanation.

[1396] 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.

[1397] 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.

[1398] 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.

[1399] 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.

[1400] 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.

[1401] 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.

[1402] 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).

[1403] 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.

[1404] 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."

[1405] 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.

[1406] 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).

[1407] 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.

[1408] 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.

[1409] 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.

[1410] 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.

[1411] 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.

[1412] 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.

[1413] 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.

[1414] 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.

[1415] 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.

[1416] 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.

[1417] The following is further disclosed regarding the above embodiment.

[1418] (Claim 1)

[1419] a means for obtaining basic information from a user;

[1420] means for transmitting the basic information to a server;

[1421] A means for interacting with a user and recording the content of the interaction;

[1422] means for transmitting the dialogue content to a server;

[1423] a server storing means for storing the basic information and the dialogue content;

[1424] A means for a server to analyze the basic information and the dialogue content and generate a future image;

[1425] means for presenting the generated future image to a user;

[1426] A system including:

[1427] (Claim 2)

[1428] 2. The system according to claim 1, wherein the basic information input by the user includes name, age, sex, school, hobbies, interests, strengths and weaknesses.

[1429] (Claim 3)

[1430] 2. The system according to claim 1, wherein the future image generated by the server includes occupation, place of residence, lifestyle, field of study, and personality traits to be careful of.

[1431] "Example 1"

[1432] (Claim 1)

[1433] a means for obtaining basic information from a user;

[1434] means for transmitting the basic information to a server via a communication terminal;

[1435] A means for users to have a conversation with an AI conversation bot and record the content of the conversation;

[1436] means for transmitting the content of the dialogue to a server via a communication terminal;

[1437] a means for the server to store the basic information and the content of the conversation in a database;

[1438] a means for a server to analyze the basic information and the content of the dialogue using a machine learning algorithm and generate a future image;

[1439] means for presenting the generated future image and advice to a communication terminal of a user;

[1440] A system including:

[1441] (Claim 2)

[1442] 2. The system according to claim 1, wherein the basic information input by the user includes name, age, sex, educational institution, preferences, areas of interest, strengths and weaknesses.

[1443] (Claim 3)

[1444] 2. The system according to claim 1, wherein the future image generated by the server includes occupational aptitude, choice of place of residence, lifestyle, fields of study, and personality traits to be aware of.

[1445] "Application Example 1"

[1446] (Claim 1)

[1447] a means for obtaining basic information from a user;

[1448] means for transmitting the basic information to a server;

[1449] A means for interacting with a user and recording the content of the interaction;

[1450] means for transmitting the dialogue content to a server;

[1451] a server storing means for storing the basic information and the dialogue content;

[1452] A means for a server to analyze the basic information and the dialogue content and generate a future image;

[1453] A means for generating optimal product recommendations for a user based on the generated future image;

[1454] means for presenting the generated product recommendations to a user;

[1455] A system including:

[1456] (Claim 2)

[1457] 2. The system according to claim 1, wherein the basic information input by the user includes name, age, sex, educational institution, hobbies, interests, strengths and weaknesses.

[1458] (Claim 3)

[1459] 2. The system according to claim 1, wherein the future image generated by the server includes occupation, place of residence, lifestyle, fields to study, personality traits to be careful of, and recommended product categories.

[1460] "Example 2: Combining Emotion Engines"

[1461] (Claim 1)

[1462] a means for obtaining basic information from a user;

[1463] means for transmitting the basic information to a server;

[1464] A means for interacting with a user and recording the content of the interaction;

[1465] A means for analyzing the user's emotions during the interaction;

[1466] means for transmitting the dialogue content and emotion analysis results to a server;

[1467] a server storing the basic information, the dialogue content, and the emotion analysis results;

[1468] A server analyzes the basic information, the dialogue content, and the emotion analysis result to generate a future image;

[1469] means for presenting the generated future image and specific advice to a user;

[1470] A system including:

[1471] (Claim 2)

[1472] 2. The system according to claim 1, wherein the basic information input by the user includes name, age, gender, educational institution, recreational activities, interests, strengths and weaknesses.

[1473] (Claim 3)

[1474] 2. The system according to claim 1, wherein the future image generated by the server includes occupation, place of residence, lifestyle, field of study, and personality traits.

[1475] "Application example 2 when combining emotion engines"

[1476] (Claim 1)

[1477] a means for obtaining basic information from a user;

[1478] means for transmitting the basic information to a server;

[1479] A means for interacting with a user and recording the content of the interaction;

[1480] means for transmitting the dialogue content to a server;

[1481] a server storing means for storing the basic information and the dialogue content;

[1482] A means for a server to analyze the basic information and the dialogue content and generate a future image;

[1483] means for presenting the generated future image to a user;

[1484] means for acquiring transaction histories and purchasing patterns and transmitting the same to a server;

[1485] means for predicting future financial situations based on said transaction history and purchasing patterns;

[1486] means for generating financial advice based on the prediction results and presenting the advice to a user;

[1487] A system including:

[1488] (Claim 2)

[1489] The system according to claim 1, wherein the basic information input by the user includes name, age, gender, educational institution, hobbies, interests, strengths and weaknesses, as well as income and daily expenses.

[1490] (Claim 3)

[1491] 2. The system of claim 1, wherein the future image generated by the server includes a savings goal and an investment strategy in addition to occupation, place of residence, lifestyle, field of study, and personality traits to be aware of. [Explanation of symbols]

[1492] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for obtaining basic information from a user; means for transmitting the basic information to a server; A means for interacting with a user and recording the content of the interaction; means for transmitting the dialogue content to a server; a server storing means for storing the basic information and the dialogue content; A means for a server to analyze the basic information and the dialogue content and generate a future image; means for presenting the generated future image to a user; A system including:

2. 2. The system according to claim 1, wherein the basic information input by the user includes name, age, sex, school, hobbies, interests, strengths and weaknesses.

3. 2. The system according to claim 1, wherein the future image generated by said server includes occupation, place of residence, lifestyle, field of study, and personality traits to be careful of.

Images