system

Abstract

Provide a system. A means for inputting personal history, interests and goals; means for transmitting the input information to a server; A means for using a generative AI model on a server to simulate multiple future scenarios based on input information and evaluate their risks and likelihood of success; A means for selecting an optimal scenario based on the evaluation results and transmitting related information to the terminal again; a means for visually displaying the received evaluation results and the optimal scenario on the terminal; A system including:

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] The anxieties and worries many people face when deciding on their future options and career paths are extremely complex and diverse. In particular, creating specific future plans based on an individual's past experiences, abilities, interests, and goals is difficult for many people because it is difficult to collect and analyze information. Furthermore, there are no reliable tools available to assess the risks and likelihood of success for future scenarios. As a result, selecting the optimal career path requires highly complex judgments and a significant amount of time, and a new system to solve these challenges is needed. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means: a system including means for inputting an individual's past information, interests, and goals, means for transmitting the input information to a server, means for simulating multiple future scenarios based on the input information on the server and using a generative AI model to evaluate their risks and likelihood of success, means for selecting an optimal scenario based on the evaluation results and transmitting related information again to a terminal, and means for visually displaying the evaluation results and the optimal scenario received on the terminal.

[0006] This system allows users to specifically understand multiple future scenarios based on their past experiences, interests, and goals, and evaluate the risks and likelihood of success of each scenario, providing reliable information for choosing the optimal career path. Furthermore, by utilizing natural language processing and machine learning algorithms, the system can perform detailed analysis of the user's input and provide appropriate advice. This allows for smooth implementation of personal future plans.

[0007] "Individual past information" refers to a person's attribute information, work history, qualifications, educational background, past choices and experiences, etc.

[0008] "Interests" refer to areas, themes, genres, etc. in which an individual is particularly interested.

[0009] A "goal" refers to a specific objective or plan that an individual wants to achieve in the future.

[0010] "Means" refer to the methods or processes used to achieve an end.

[0011] "Server" refers to a computer system that receives, analyzes, processes data, and returns results.

[0012] A "generative AI model" refers to an algorithm that uses artificial intelligence to analyze data and generate and evaluate multiple scenarios.

[0013] "Future scenarios" refer to multiple future developments predicted based on different choices and actions.

[0014] "Risk" refers to the possibility of failure, problems, or potential failures associated with a future scenario.

[0015] "Probability of success" refers to the likelihood or probability that a particular scenario will succeed.

[0016] "Evaluation results" refers to the information resulting from the analysis by the generative AI model, including the risks and likelihood of success associated with each scenario.

[0017] "Visually displaying" refers to presenting data or information to the user in an intuitively easy-to-understand format, such as a graph, chart, or timeline.

[0018] "Analysis" refers to the process of examining data in detail to find specific patterns or trends.

[0019] "Natural language processing" refers to the technology that enables computers to understand and analyze human language.

[0020] "Machine learning algorithms" refer to artificial intelligence techniques that automatically generate models and predictions based on data. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] This invention provides a system in which a user inputs their own past information, interests, and goals, and a generative AI model simulates multiple future scenarios based on that information. Specific embodiments of the system include the following:

[0043] Program processing

[0044] 1. User Input

[0045] Users enter their past information (such as work history, educational history, and qualifications), areas of interest, and future goals through a dedicated interface. This information is provided in a form format.

[0046] 2. Data Transmission

[0047] The terminal formats the data entered by the user and sends it to the server using a secure communication protocol, and also verifies that the data is formatted correctly.

[0048] 3. Data Reception and Analysis

[0049] The server receives the data, validates it, and checks for any incorrect or missing data before beginning its analysis, using natural language processing and machine learning algorithms.

[0050] 4. Scenario generation and evaluation

[0051] The server uses a generative AI model to generate multiple future scenarios based on the analysis results. For each scenario, it evaluates the risk (factors that could hinder success) and probability of success (the probability that the scenario will succeed). Specifically, it takes into account market trends, the difficulty of acquiring skills, and related success and failure cases.

[0052] 5. Selecting the optimal scenario and returning the data

[0053] Based on the evaluation results, the server selects the future scenario that best suits the user and generates detailed information related to it (e.g., a future timeline and an analysis of risks and the likelihood of success), which is then sent back to the terminal.

[0054] 6. Data reception and display

[0055] The device receives the information sent from the server and displays it in a user-friendly format, including future timelines, risk simulation diagrams, and specific action plans. A visually appealing design is used to make the information easy to understand.

[0056] Specific examples

[0057] Example 1: A user considering changing jobs to a different industry

[0058] The user enters their own information (e.g., "Current occupation: Engineer," "Area of ​​interest: Data science," "Goal: Change careers to become a data scientist").

[0059] The terminal sends this information to the server.

[0060] The server analyzes the data and generates scenarios, including "Career Change to Data Science Industry," assessing risks (e.g., skill gaps) and likelihood of success (e.g., industry demand).

[0061] The server selects the optimal scenario, which is to "take an online course to acquire skills, then start job hunting," and sends the relevant data to the terminal.

[0062] The device displays this scenario as a future timeline and risk simulation diagram, providing the user with an optimal action plan.

[0063] Example 2: A user considering starting a business

[0064] The user inputs his / her own information (for example, "current occupation: marketing manager", "area of ​​interest: e-commerce business", "goal: launching one's own e-commerce site").

[0065] The terminal sends this information to the server.

[0066] The server analyzes the input data, generates a scenario including "launching an e-commerce business," and evaluates risks (e.g., market competition, fundraising) and success probability (e.g., market demand, competitive analysis).

[0067] The server selects the scenario of "fundraising plan, marketing strategy development, and initial investment amount setting" as the optimal plan and transmits the related information to the terminal.

[0068] The device displays a future timeline, risk simulation diagrams, and specific action plans to support users in making decisions.

[0069] The system helps users choose the best path to their goals and create specific plans. By visually presenting the risks and success probability of scenarios, users can make decisions with more confidence.

[0070] The processing flow will be explained below.

[0071] Step 1:

[0072] Users enter their past information (work history, educational history, qualifications, etc.), areas of interest, and future goals through a dedicated interface (such as a web form or application form). This information is provided in form format.

[0073] Step 2:

[0074] The terminal formats the data entered by the user into the required format and sends it to the server using a secure communication protocol (e.g. HTTPS), and also verifies that the data is formatted correctly.

[0075] Step 3:

[0076] The server receives the data and verifies its contents. After checking for any invalid or missing data, it begins analyzing it.

[0077] Step 4:

[0078] The server analyzes user information using natural language processing and machine learning algorithms, for example, to identify the user's skill set based on their work history and educational background, and evaluates their suitability for the goals.

[0079] Step 5:

[0080] The server uses the generative AI model to generate multiple future scenarios based on the user's goals, such as "changing jobs to a different industry," "starting a business," and "career advancement."

[0081] Step 6:

[0082] The server evaluates the risk of each scenario (e.g., difficulty of acquiring the necessary skills, possibility of failure) and the likelihood of success (e.g., market demand, success stories), taking into account past data and current market trends.

[0083] Step 7:

[0084] Based on the evaluation results, the server selects the most suitable scenario for the user, based on criteria such as the user's goals, past experience, and market demand.

[0085] Step 8:

[0086] The server generates a report containing detailed information about the selected scenario (such as a future timeline and analysis of risks and likelihood of success) and sends it to the terminal.

[0087] Step 9:

[0088] The terminal receives the information sent from the server and displays it in a user-friendly format, including a future timeline, risk simulation diagrams, and specific action plans.

[0089] Step 10:

[0090] The user refers to the displayed information, and if they have further questions or need additional information, they return to step 1 and enter additional information.

[0091] Example 1

[0092] 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."

[0093] Conventional systems require a lot of effort to generate and evaluate future scenarios based on an individual's past information and goals. Furthermore, it is difficult to properly evaluate and present the risks and success probability of these scenarios, resulting in a lack of specific and reliable information for users to create optimal action plans. This makes it difficult for users to make optimal choices toward their future goals.

[0094] 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.

[0095] In this invention, the server includes means for verifying received data and analyzing the data using natural language processing and machine learning algorithms, means for generating multiple future scenarios using a generative AI model based on the analysis results and evaluating the risks and success probability of each scenario, and means for selecting the optimal scenario based on the evaluation results and returning data including detailed information to the terminal. This allows the user to obtain a specific and reliable future scenario based on their own past information and goals, and to visually understand the risks and success probability of that scenario.

[0096] A "user" is an individual who utilizes the system to input information about their past, interests, and future goals.

[0097] A "terminal" is a device that formats information entered by a user, transmits it to a server using a secure communications protocol, and receives and visually displays information returned from the server.

[0098] The "server" is a device that verifies the received data, analyzes the data using natural language processing and machine learning algorithms, and generates and evaluates scenarios using a generative AI model based on the results.

[0099] "Input data" is information including past information, interests and future goals entered by a user through a dedicated interface.

[0100] "Natural language processing" is an information processing technology that allows computers to understand, interpret, and manipulate human language.

[0101] A "machine learning algorithm" is a technology that allows computers to automatically acquire knowledge and patterns by analyzing and learning from data.

[0102] A "generative AI model" is an artificial intelligence model that generates future scenarios based on analysis results.

[0103] A "scenario" is a plan showing multiple possible futures created by a generative AI model based on user input data.

[0104] A "risk" is a factor that prevents a scenario from being successful.

[0105] "Probability of success" is the probability or likelihood that a scenario will succeed.

[0106] "Evaluation results" are the results of evaluating the risks and likelihood of success for each scenario generated by the generative AI model.

[0107] "Detailed information" refers to specific data related to the scenario, such as a future timeline and an analysis of risks and likelihood of success.

[0108] This invention provides a system in which a user inputs their past information, interests, and goals, and a generative AI model simulates multiple future scenarios based on that information. Specific embodiments are as follows.

[0109] Through a dedicated interface, users enter their past information (e.g., work history, educational history, qualifications, etc.), areas of interest, and future goals. This information is provided in the form of a form. This interface is an application that can be used on a web browser and is developed using HTML and JavaScript (registered trademark).

[0110] The terminal formats the data entered by the user into JSON format and sends it to the server using a secure communication protocol (e.g., HTTPS). At this time, the terminal also has a function to verify the redundancy and inconsistency of the input data. The terminal is an ordinary personal computer or smartphone, and is operated via a web browser.

[0111] The server verifies the received data and performs data analysis using natural language processing (e.g., NLTK or SpaCy) and machine learning algorithms. The server may be, for example, a high-performance cloud server or a server in a data center. After analyzing the data, it generates multiple future scenarios using a generative AI model (e.g., GPT-3 (registered trademark)) and evaluates the risks (factors that hinder success) and success probability (probability of the scenario succeeding) for each scenario. Statistical models and probabilistic methods are used for the evaluation.

[0112] The server selects the most suitable future scenario for the user based on the evaluation results. This selection is based on multiple evaluation indicators. Detailed information related to the selected scenario (e.g., a future timeline and analysis results of risks and success probability) is generated and sent back to the terminal. The returned data is compressed using a compression algorithm (e.g., gzip) for efficient transmission.

[0113] The terminal receives the information sent from the server and displays it in a user-friendly format, including future timelines, risk simulation diagrams, and specific action plans, using a visually appealing design and front-end libraries (e.g., React).

[0114] As a concrete example, consider the case of a user considering changing jobs to a different industry. The user enters their own information (e.g., "Current occupation: engineer," "Area of ​​interest: data science," "Goal: change jobs as a data scientist"). The device sends this information to a server, which analyzes the data and generates scenarios that include "changing jobs to the data science industry." It evaluates risks (e.g., skill gaps) and likelihood of success (e.g., industry demand) and selects the optimal scenario: "Take online courses to acquire skills, then begin job hunting." The device then displays these scenarios as a future timeline and risk simulation diagram, providing the user with an optimal action plan.

[0115] The same applies to users considering starting a business. The user enters their personal information (e.g., "Current occupation: marketing manager," "Area of ​​interest: e-commerce business," "Goal: launching their own e-commerce website") and their device sends this information to the server. The server analyzes the input data, generates scenarios including "launching an e-commerce business," and evaluates the risks (e.g., market competition, fundraising) and likelihood of success (e.g., market demand, competitive analysis). It selects the optimal scenario, "planning fundraising, formulating a marketing strategy, and setting an initial investment amount," and sends the relevant information to the device. The device then displays a future timeline, a risk simulation diagram, and a specific action plan to support the user's decision-making. This system makes it easy for users to select the optimal path toward their goals and create a specific plan. By visually presenting the scenario's risks and likelihood of success, users can make decisions with greater confidence.

[0116] Prompt Sentence Examples

[0117] "I'm an engineer with an interest in data science. I'd like to change careers and become a data scientist. How can I achieve this goal?"

[0118] "I'm a marketing manager and I'm interested in the e-commerce business. I'm considering launching my own e-commerce site, but what approach should I take to ensure success?"

[0119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0120] Step 1:

[0121] Through a dedicated interface, users input their past information (work history, educational background, qualifications, etc.), areas of interest, and future goals. For example, a user might enter information such as "Current occupation: engineer," "Area of ​​interest: data science," and "Goal: change jobs to become a data scientist" into a web form. The input data is entered as text into each field of the form, and users can proceed to the next step by clicking the submit button.

[0122] Step 2:

[0123] The terminal formats the data entered by the user into JSON format and verifies the accuracy and integrity of the data. Specifically, it checks whether the input data is complete and whether all required fields are filled in. It then sends the formatted data to the server using a secure communication protocol (HTTPS). The input is the information entered by the user, and the output is the data converted to JSON format.

[0124] Step 3:

[0125] The server receives the data sent from the terminal. After receiving it, it verifies the validity of the data again and checks for invalid or missing data. If there is a problem at this stage, it generates an error message and returns it to the terminal. The input is JSON-formatted data, and the output is the validated data or an error message.

[0126] Step 4:

[0127] The server analyzes the received data using a natural language processing (NLP) engine or machine learning algorithm. Specifically, it uses an NLP library (e.g., NLTK or SpaCy) to automatically extract the user's interests and goals. The input is the validated data, and the output is the analysis results (e.g., keyword extraction and category classification).

[0128] Step 5:

[0129] The server uses a generative AI model (e.g., GPT-3) to generate multiple future scenarios based on the analysis results. The input is the analyzed data, and the output is the generated scenarios. Specifically, the generative AI model uses a text generation engine to create multiple scenarios showing possible future outcomes.

[0130] Step 6:

[0131] The server evaluates the risk and probability of success for each generated scenario. Statistical models and probabilistic methods are used for risk evaluation. The input is the generated scenario, and the output is the evaluation results of the risk and probability of success for each scenario.

[0132] Step 7:

[0133] The server selects the optimal scenario based on the evaluation results. Specifically, it scores each scenario based on multiple evaluation indicators and selects the scenario with the highest score. The input is the evaluation results of risk and probability of success, and the output is the optimal scenario.

[0134] Step 8:

[0135] The server generates detailed information related to the selected scenario (future timeline and analysis results of risks and success probability), compresses it, and sends it back to the terminal. The input is the optimal scenario, and the output is the compressed detailed information.

[0136] Step 9:

[0137] The terminal receives the information sent from the server and checks the validity of the data. If there are no problems with the received data, it decompresses the data and visually displays it in a format that is easy for the user to understand. A front-end library (e.g., React) is used for the display. The input is the compressed detailed information, and the output is a visual display of the decompressed data.

[0138] (Application example 1)

[0139] 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."

[0140] Conventional advertising systems only consider a user's past behavior and interests, and are unable to provide personalized advertisements based on future purchasing scenarios. This makes it difficult to display the most beneficial advertisements to users at the right time. A solution to this problem is needed.

[0141] 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.

[0142] In this invention, the server includes: means for inputting an individual's past information, interests, and goals; means for transmitting the input information to the server; means for using a generative AI model on the server to simulate multiple future scenarios based on the input information and evaluate their risks and likelihood of success; means for selecting an optimal scenario based on the evaluation results and transmitting related information again to the terminal; means for visually displaying the received evaluation results and the optimal scenario on the terminal; and means for displaying appropriate advertisements to the user based on the generated scenarios. This makes it possible to provide personalized advertisements based on the future purchasing scenarios that are most suitable for the user.

[0143] "Personal past information" refers to data such as the user's work history, educational background, qualifications, and purchasing history.

[0144] "Interests" refers to areas or matters in which a user is interested, specifically, hobbies, purchasing preferences, etc.

[0145] "Goals" are specific goals or expected results that a user wants to achieve in the future.

[0146] A "generative AI model" is an artificial intelligence model used to simulate future scenarios based on input data and assess their risks and likelihood of success.

[0147] A "server" is a computer system that receives data submitted by users and analyzes and evaluates the data using a generative AI model.

[0148] A "terminal" is a device that allows a user to input data and visually display evaluation results and scenarios sent from a server.

[0149] A "future scenario" is a future progression pattern that is simulated based on the user's current information and goals.

[0150] "Risks and likelihood of success" is an assessment of the obstacles and feasibility of each scenario coming to fruition.

[0151] "Visually displaying" means presenting information graphically in a format that is easy for a user to understand.

[0152] "Advertising" is a means of communication for providing users with information about products and services.

[0153] "Personalized advertising" refers to advertising that is optimized based on a user's past information and future purchasing scenarios.

[0154] The system for implementing this invention inputs an individual's past information, interests, and goals, and then uses a generative AI model to simulate future purchasing scenarios based on that information, providing optimal personalized advertising to the user. Specific embodiments are described below.

[0155] The system consists of the following main components:

[0156] 1. User device: Includes a means for inputting personal information (such as work history, educational background, qualifications, and purchasing history), interests (hobbies and purchasing preferences), and goals (specific goals and desired outcomes for the future). The device can be a smartphone, smart glasses, or a head-mounted display.

[0157] 2. Server: Receives information input from the user device and analyzes and evaluates it using a generative AI model. The server uses natural language processing and machine learning algorithms to generate future scenarios and evaluates the risk and likelihood of success for each scenario.

[0158] 3. Generative AI model: An artificial intelligence model that analyzes input data from users and simulates multiple future scenarios.

[0159] 4. Advertisement display function: Includes a means for visually displaying the most suitable advertisement for the user based on the evaluation results and optimal scenario received from the server.

[0160] Users input their purchasing history, interests, and future purchase plans via a smartphone app or smart glasses. This information is sent to a server using a secure communication protocol. The server then analyzes the input data using a generative AI model to generate and evaluate multiple future scenarios. The results of these evaluations are then sent back to the device, and the user receives the information in a visually easy-to-understand format. At the same time, personalized advertisements based on the generated scenarios are displayed, providing the most useful advertisements to the user at the right time.

[0161] For example:

[0162] For example, if a user inputs the goal of "buying a new smartphone," the generative AI model will generate an optimal future scenario based on the user's past purchasing history (e.g., "bought a laptop a year ago" and "bought a tablet six months ago") and interests (e.g., "gadgets"). Based on this scenario, the user will be shown a discount promotional ad for a new smartphone. In this way, the user can receive ads that best suit their purchasing intentions.

[0163] Example prompt sentence:

[0164] "Please describe your past purchase history and future interests. For example, 'Past: Smartphones', 'Interest: Gadgets', 'Goal: Buy a new smartphone'."

[0165] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0166] Step 1: The user inputs their past purchasing history, interests, and future goals into the user device. Specifically, they input information in a form using a smartphone app or smart glasses. This input data includes, for example, "smartphone," "gadget," and "purchase a new smartphone." The input in this step is the user's past information, interests, and goals, and the output is the formatted input data.

[0167] Step 2: The terminal sends the user's input data to the server using a secure communication protocol. This transmission also includes formatting and verifying the correctness of the data. The input of this step is the user's input data, and the output is the data sent to the server.

[0168] Step 3: The server receives the data sent from the device, checks for any invalid or missing data, and begins analysis. This analysis uses natural language processing and machine learning algorithms. The input of this step is the received data, and the output is the analysis result.

[0169] Step 4: The server uses the generative AI model to generate multiple future scenarios based on the analysis results and evaluates the risk and likelihood of success for each scenario. Specifically, it refers to data such as market trends, difficulty of acquiring skills, and related success and failure cases. The input for this step is the analyzed data, and the output is the evaluated scenario.

[0170] Step 5: Based on the evaluation results, the server selects the future scenario that best suits the user and generates detailed information about it. The detailed information includes a future timeline, risk, and success probability analysis results. The input to this step is the evaluated scenario, and the output is the selected optimal scenario and its detailed information.

[0171] Step 6: The server again transmits the selected optimal scenario and related detailed information to the terminal. The input of this step is the detailed information of the selected optimal scenario, and the output is the data transmitted to the terminal.

[0172] Step 7: The terminal receives the information sent from the server and visually displays it in a user-friendly format. This display includes a future timeline, a risk simulation diagram, and a specific action plan. Appropriate advertisements are also displayed at the same time. The input of this step is the scenario information received from the server, and the output is the displayed information and advertisements.

[0173] Step 8: The user checks the information and advertisements displayed on the device and selects the appropriate action. The user makes purchasing decisions and makes purchases based on this information. The input to this step is the information and advertisements displayed on the device, and the output is the user's decision to act.

[0174] 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.

[0175] This invention combines a system in which a user inputs their past information, interests, and goals, and a generative AI model simulates multiple future scenarios based on that information, with an emotion engine that recognizes the user's emotions, enabling more precise scenario generation and selection that takes the user's emotions into account.

[0176] Program processing

[0177] 1. User Input and Emotion Recognition

[0178] Through a dedicated interface, users input their past information (work history, educational background, qualifications, etc.), areas of interest, and future goals. In addition, the emotion engine recognizes the user's emotional state in real time as they input and interact.

[0179] 2. Data Transmission

[0180] The terminal formats the data input by the user and the emotion data detected by the emotion engine into the required format and transmits it to the server using a secure communication protocol.

[0181] 3. Data Reception and Analysis

[0182] The server receives the transmitted data, verifies its contents, and begins data analysis after checking for any invalid or missing data.

[0183] 4. Scenario generation and emotional reflection

[0184] The server uses natural language processing and machine learning algorithms to analyze user and emotional information, for example, to identify a user's skill set based on their work history and educational background, and to assess its suitability for their goals and emotional state.

[0185] The server uses a generative AI model to generate multiple future scenarios that take into account the user's goals and emotions. Emotional data is used to modify the scenarios to reflect the user's stress level and excitement state.

[0186] 5. Evaluating the scenario

[0187] The server evaluates the risk of each scenario (e.g., difficulty of acquiring the required skills, probability of failure) and the likelihood of success (e.g., market demand, success stories). The evaluation takes into account not only past data and current market trends, but also the user's emotional state.

[0188] 6. Selecting the optimal scenario and returning the data

[0189] The server selects the most suitable scenario for the user based on the evaluation results, with the selection criteria including the user's goals, past experiences, market demands, and the user's emotional state.

[0190] The server generates a report containing detailed information about the selected scenario (future timeline, analysis results of risks and probability of success, and emotional reflection results) and sends it to the terminal.

[0191] 7. Data reception and display

[0192] The device receives the information sent from the server and displays it in a user-friendly format, including a future timeline, risk simulation diagrams, specific action plans, and advice based on emotional state.

[0193] 8. User Feedback

[0194] The user can refer to the displayed information and, if they have further questions or need additional information, they can enter the initial information again. The new input and emotion data will be used for further analysis.

[0195] Specific examples

[0196] Example 1: A user considering changing jobs to a different industry

[0197] Users enter their own information (e.g., "Current occupation: engineer," "Area of ​​interest: data science," "Goal: change careers to become a data scientist"), and the emotion engine also recognizes their emotions (e.g., excitement, anticipation).

[0198] The terminal transmits this information and emotion data to the server.

[0199] The server analyzes the data and generates scenarios, including "Career Change to Data Science Industry." It evaluates modified scenarios taking into account risks (e.g., skill gaps), likelihood of success (e.g., industry demand), and sentiment.

[0200] The server selects the optimal scenario, which is to "take an online course to acquire skills, then start job hunting," and sends the relevant data to the terminal.

[0201] The device displays this scenario as a future timeline and risk simulation diagram, providing the user with an optimal action plan.

[0202] Example 2: A user considering starting a business

[0203] Users enter their own information (e.g., "Current occupation: Marketing manager," "Area of ​​interest: E-commerce business," "Goal: Launching their own e-commerce site"), and the emotion engine also recognizes their emotions at the time (e.g., anxiety, expectation).

[0204] The terminal transmits this information and emotion data to the server.

[0205] The server analyzes the input data and sentiment data to generate scenarios including "launching an e-commerce business." It evaluates revised scenarios taking into account risks (e.g., market competition, fundraising), success probability (e.g., market demand, competitive analysis), and sentiment.

[0206] The server selects the scenario of "fundraising plan, marketing strategy development, and initial investment amount setting" as the optimal plan and transmits the related information to the terminal.

[0207] The device displays future timelines, risk simulation diagrams, and specific action plans to support users' decisions and provide additional emotional advice.

[0208] This system enables more precise scenario generation and optimization that takes into account the user's emotions, providing more reliable future planning.

[0209] The processing flow will be explained below.

[0210] Step 1:

[0211] Through a dedicated interface, users input their past information (such as work history, educational background, and qualifications), areas of interest, and future goals. At the same time, the emotion engine recognizes the user's emotional state (e.g., excitement, anticipation, anxiety) in real time from their facial expressions and input patterns as they input and interact.

[0212] Step 2:

[0213] The terminal formats the data input by the user and the emotion data analyzed by the emotion engine into the required format and transmits it to the server using a secure communication protocol (e.g., HTTPS).

[0214] Step 3:

[0215] The server receives the transmitted data, verifies its contents, and begins data analysis after checking for any invalid or missing data.

[0216] Step 4:

[0217] The server uses natural language processing and machine learning algorithms to analyze user and emotional information, for example, to identify a user's skill set based on their work history and educational background, and to assess its suitability for their goals and emotional state.

[0218] Step 5:

[0219] The server uses a generative AI model to generate multiple future scenarios that take into account the user's goals and emotions. Specifically, it generates scenarios such as "changing jobs to a different industry," "starting a business," and "career advancement," and then modifies and customizes the scenarios based on emotional data.

[0220] Step 6:

[0221] The server evaluates the risk of each scenario (e.g., difficulty of acquiring the required skills, probability of failure) and the likelihood of success (e.g., market demand, success stories). The evaluation takes into account not only past data and current market trends, but also the user's emotional state.

[0222] Step 7:

[0223] The server selects the most suitable scenario for the user based on the evaluation results, with the selection criteria including the user's goals, past experiences, market demands, and the user's emotional state.

[0224] Step 8:

[0225] The server generates a report containing detailed information about the selected scenario (future timeline, analysis results of risks and probability of success, and emotional reflection results) and sends it to the terminal.

[0226] Step 9:

[0227] The device receives the information sent from the server and displays it in a user-friendly format, including future timelines, risk simulation diagrams, specific action plans, and advice based on the user's emotional state.

[0228] Step 10:

[0229] The user can refer to the displayed information and, if they have further questions or need additional information, they can enter the initial information again. The new input and emotion data will be used for further analysis.

[0230] Example 2

[0231] 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."

[0232] Conventional systems were able to simulate future scenarios based on an individual's past information and goals, but they were unable to generate or evaluate scenarios that took the user's emotional state into account. This meant that they were unable to provide accurate scenarios that reflected the user's real-time emotional state. Furthermore, scenarios that did not take emotional state into account often presented problems in that they were not realistic or effective for the user.

[0233] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting an individual's past information, interests, and goals; means for transmitting the input information and the user's emotional data to the server; means for using a generative AI model on the server to simulate multiple future scenarios based on the input information and emotional data and evaluate their risks and likelihood of success; means for selecting an optimal scenario based on the evaluation and transmitting related information again to the terminal; and means for visually displaying the received evaluation results and the optimal scenario on the terminal. This enables precise scenario generation and evaluation that takes the user's emotional state into consideration, making it possible to provide more realistic and effective future scenarios.

[0234] "Personal past information" is data on the user's past experiences, such as work history, educational history, and qualifications.

[0235] "Interests" are areas or activities that interest a user.

[0236] A "goal" is a specific objective or result that a user wants to achieve in the future.

[0237] "Input means" refers to the interface or device through which a user provides information to the system.

[0238] "Emotion data" is data that detects the user's emotional state in real time and expresses it as numerical values ​​or categories.

[0239] "Server" refers to a central processing unit that receives input information and emotion data, processes them, and analyzes them.

[0240] "Multiple future scenarios" are plans showing multiple different future possibilities that are generated based on the user's input information and emotion data.

[0241] A "generative AI model" is a program that uses artificial intelligence and machine learning algorithms to analyze data and generate future scenarios.

[0242] "Evaluation means" refers to methods and tools for analyzing risks and the likelihood of success based on the generated scenarios.

[0243] "Means for selecting the optimal scenario" refers to the method or process for selecting the most suitable scenario for the user based on the evaluation results.

[0244] "Terminal" refers to a device or computer system through which a user can enter information or view results.

[0245] "Visual display means" refers to a graphical interface or display device that displays analysis results and scenarios in an easy-to-understand manner for the user.

[0246] "Natural language processing" is a technology for analyzing and understanding human language.

[0247] "Machine learning algorithms" refer to mathematical methods and programs that automatically learn patterns and knowledge from data.

[0248] "Market trends" are data that show the current market situation and future trends.

[0249] "Skill acquisition difficulty" refers to the time and effort required to master a particular skill.

[0250] "Success stories and failure stories" refer to specific cases of success and failure obtained from past experience and data.

[0251] "JSON" is a lightweight data exchange format for structuring data.

[0252] "HTTPS" is a protocol for securely transmitting data over the Internet.

[0253] This invention is a system that uses a generative AI model to simulate multiple future scenarios based on user input information and emotion data, and evaluates the risk and likelihood of success of each scenario. An embodiment of this system is described in detail below.

[0254] System configuration

[0255] The system consists of the following major hardware and software components:

[0256] 1. User terminal: Provides an interface for users to input information (e.g., PC, smartphone, tablet).

[0257] 2. Emotion engine: Hardware (e.g., camera, microphone) and software (e.g., facial expression recognition program, voice analysis software) for detecting the user's emotional state in real time.

[0258] 3. Server: A central processing unit that receives data, analyzes, generates scenarios, and evaluates them. It is equipped with generative AI models and machine learning algorithms.

[0259] 4. Secure communication protocols: Protocols to ensure the safety of data transmission and reception (e.g. HTTPS).

[0260] Data Entry and Emotion Recognition

[0261] Users input past information (e.g., work history, educational background, qualification status), areas of interest, and future goals through their device. This can be done using a computer or smartphone keyboard or voice input. The emotion engine uses a camera and microphone to detect the user's emotional state in real time while they are typing. For example, when a user types "Current occupation: engineer," "Area of ​​interest: data science," and "Goal: change careers as a data scientist," their emotions (e.g., excitement, anticipation) are simultaneously recognized.

[0262] Data transmission and reception

[0263] The device formats the data and emotion data entered by the user and sends them to the server using a secure communication protocol. Specifically, the data is compiled in JSON format and encrypted using HTTPS before being sent. The server receives the data and verifies that there is no invalid or missing data.

[0264] Scenario generation and evaluation

[0265] The server uses natural language processing (NLP) and machine learning algorithms to analyze the user's input and emotional data. After analysis, a generative AI model is used to generate multiple future scenarios based on the user's goals and current situation. For example, a scenario could be generated in which the user takes an online course to acquire skills, then begins job hunting.

[0266] For each scenario, the server evaluates the risk (e.g., skill gaps, probability of failure) and likelihood of success (e.g., market demand, success stories). This evaluation takes into account market trends and historical data, as well as the user's emotional state. For example, if the user is "excited," challenging scenarios will be prioritized.

[0267] Optimal scenario selection and data return

[0268] Based on the evaluation results, the server selects the most suitable scenario for the user and generates a detailed report, including a future timeline, risk and success probability analysis, and sentiment-based advice. The report is sent to the device in JSON format.

[0269] Visual display and user feedback

[0270] The device receives the report sent from the server and displays the information in a user-friendly format, including a future timeline, risk simulation diagram, specific action plans, and advice based on the user's emotional state. If the user needs additional questions or information, they can re-enter it and send it back to the server.

[0271] Prompt Sentence Examples

[0272] An example prompt might be, "I'm looking to transition my career from engineer to data scientist. What steps should I take?"

[0273] Another prompt reads, "I would like to use my experience as a marketing manager to start an e-commerce business. Please let me know the appropriate plan."

[0274] This system enables more precise scenario generation and evaluation that takes into account the user's emotions, providing more realistic and effective future scenarios.

[0275] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0276] Step 1:

[0277] User Input and Emotion Recognition

[0278] Input: User's past information (work history, education history, qualifications), areas of interest, goals.

[0279] Specific operation: The user enters their past information, interests, and goals through the device interface. For example, they can enter "Current occupation: Engineer," "Area of ​​interest: Data science," and "Goal: Change careers as a data scientist" using keyboard input or voice input.

[0280] Input: User emotion data (real-time).

[0281] Specific operation: The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice to detect their emotional state (e.g., excitement, anticipation).

[0282] Output: Formatted data containing user input and emotion data.

[0283] Step 2:

[0284] Data transmission

[0285] Input: User profile data (history, interests, goals) and sentiment data.

[0286] Specific operation: The device formats the input data in JSON format and sends it to the server using a secure communication protocol (HTTPS). The data is sent in encrypted form.

[0287] Output: The formatted data sent to the server.

[0288] Step 3:

[0289] Data Receipt and Verification

[0290] Input: Submitted data (profile data, emotion data).

[0291] Specific operation: The server receives the data sent from the terminal and verifies whether there is any invalid or missing data, for example, whether all required fields are filled in and whether the data format is correct.

[0292] Output: Validated data.

[0293] Step 4:

[0294] Data analysis

[0295] Input: Verified data (profile data, sentiment data).

[0296] Specific operation: The server uses natural language processing (NLP) and machine learning algorithms to analyze the user's past information and goals. For example, it identifies the user's skill set from their work history and educational background, and matches it to their areas of interest and goals.

[0297] Output: Analysis results (data such as user skill sets, goals, and interests).

[0298] Step 5:

[0299] Scenario Generation

[0300] Input: Analysis results (data such as user skill sets, goals, and interests), sentiment data.

[0301] Specific actions: The server uses the generative AI model to generate multiple future scenarios based on the user's goals and current situation. For example, it creates a specific scenario such as "Take an online course to acquire skills, then start job hunting."

[0302] Output: Multiple future scenarios.

[0303] Step 6:

[0304] Emotional data reflection and scenario evaluation

[0305] Input: Future scenario, emotion data.

[0306] Specific operation: The server reflects the emotion data in the analysis results. This evaluates the risk and success probability of each scenario. For example, it takes into account the user's emotions (e.g., anticipation, excitement) and adjusts the risk items and success factors.

[0307] Output: The evaluated scenario.

[0308] Step 7:

[0309] Optimal scenario selection

[0310] Input: The evaluated scenario.

[0311] Specific operation: The server selects the most suitable scenario for the user based on the evaluation results, for example, the scenario with the lowest risk and the highest probability of success.

[0312] Output: Details of the optimal scenario.

[0313] Step 8:

[0314] Data return and report generation

[0315] Input: Best-case scenario details.

[0316] Specific operation: The server generates a report containing detailed information about the selected scenario and sends it to the device. The report includes a future timeline, an analysis of risks and success rates, and sentiment-based advice. The data is sent in JSON format.

[0317] Output: The report data sent.

[0318] Step 9:

[0319] Displaying received data

[0320] Input: Report data from the server.

[0321] Specific operations: The terminal receives the report sent from the server and displays the information in a user-friendly format, including a future timeline, a risk simulation diagram, a specific action plan, and advice based on the user's emotional state.

[0322] Output: Visually displayed information.

[0323] Step 10:

[0324] User Feedback

[0325] Input: Information displayed, ask additional questions or enter new information.

[0326] Specific operation: The user refers to the displayed information and, if they have further questions or need additional information, they enter the initial information again. The new input and emotion data are then used for further analysis.

[0327] Output: New user input data, emotion data.

[0328] (Application example 2)

[0329] 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."

[0330] Conventional systems generate future scenarios based on an individual's past information and goals, but because they fail to consider the user's emotional state, they may present scenarios that are not always optimal for the user. Furthermore, ignoring the user's emotional state can increase stress and lead to disappointing results when making important decisions such as financial planning, changing jobs, or starting a business.

[0331] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0332] In this invention, the server includes: means for inputting an individual's past information, interests, and goals; means for transmitting the input information and emotional data to the server; means for using a generative AI model on the server to simulate multiple future scenarios based on the input information and emotional data and evaluate their risks and likelihood of success; means for selecting an optimal scenario based on the evaluation results and transmitting related information again to the terminal; and means for visually displaying the received evaluation results and optimal scenario on the terminal. This enables the generation and selection of more precise and personalized scenarios that take the user's emotions into consideration, reducing user stress and achieving expected results.

[0333] "Individual past information" refers to data such as the individual's past work history, educational history, and qualifications.

[0334] "Interest" refers to an individual's interest in a field or area of ​​interest.

[0335] A goal is an objective or goal that an individual wants to achieve in the future.

[0336] "Emotional data" refers to data that detects and records an individual's emotional state in real time.

[0337] A "generative AI model" is an artificial intelligence model that uses natural language processing and machine learning to perform data analysis and simulations.

[0338] A "server" is a remote computer system that performs data analysis, evaluation, and scenario generation.

[0339] A "terminal" is a digital device used by an individual to enter and receive information.

[0340] "Risk" refers to factors or difficulties that could hinder success in a future scenario.

[0341] "Success probability" refers to the likelihood or likelihood that a particular scenario will achieve its goals.

[0342] A "scenario" is a hypothetical plan that details a specific situation or plan for the future.

[0343] The system based on this invention inputs an individual's past information and goals, recognizes emotional data, generates multiple future scenarios, and evaluates their risks and likelihood of success. The main components are a terminal where the user inputs data, a server that transmits, receives, analyzes, and evaluates the data, and a scenario generation function that uses a generative AI model.

[0344] System Program

[0345] The system's program uses a generative AI model incorporating an emotion recognition engine to analyze the user's input data and emotional data, and generate an optimal scenario. The main parts of the program are as follows:

[0346] 1. User Input and Emotion Recognition

[0347] The device allows users to input past information (such as work history, educational background, and qualifications), areas of interest, and future goals through a dedicated interface. At the same time, an emotion recognition engine recognizes the user's emotional state in real time. The emotion recognition engine uses common hardware (such as smartphones and tablets) and emotion recognition software (such as Affectiva).

[0348] 2. Data Transmission

[0349] The device formats the input data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS).

[0350] 3. Data Reception and Analysis

[0351] The server receives the transmitted data and validates it. After checking for missing or invalid data, it begins data analysis. The analysis utilizes cloud infrastructure (e.g., AWS (registered trademark), Azure (registered trademark)) and uses natural language processing and machine learning algorithms.

[0352] 4. Scenario generation and emotional reflection

[0353] The server uses natural language processing and machine learning algorithms to analyze user information and emotional information. It uses a generative AI model to generate multiple future scenarios that take into account the user's goals and emotions. Emotional data is used to modify the scenarios to reflect the user's stress level and excitement state.

[0354] 5. Evaluating the scenario

[0355] The server assesses the risk and likelihood of success of each scenario, taking into account not only historical data and current market trends, but also the user's emotional state.

[0356] 6. Selecting the optimal scenario and returning the data

[0357] The server selects the most suitable scenario for the user based on the evaluation results. The selection criteria include the user's goals, past experiences, market demand, and the user's emotional state. A report containing detailed information about the selected scenario is generated and sent to the terminal.

[0358] 7. Data reception and display

[0359] The device receives the information sent from the server and displays it in a user-friendly format, including a future timeline, risk simulation diagrams, specific action plans, and advice based on emotional state.

[0360] Specific examples

[0361] For example, a user can input their "past purchase history" and "future goals" via their smartphone, and the emotion recognition engine will monitor their emotional state at the time. The device sends this data to a server, which analyzes it and generates an optimal financial planning scenario, which is then sent back to the smartphone. The user can then confirm, modify, and accept the scenario displayed on their smartphone, resulting in a more precise and personalized financial plan.

[0362] Prompt Sentence Examples

[0363] "Enter your past purchasing history and future goals. And give us the permission we need to recognize your emotions in real time as you type. We'll give you the best financial plan."

[0364] The above is the basic form for carrying out the present invention. This makes it possible to generate and select scenarios that take into account the user's emotions, thereby more effectively supporting the user's decision-making.

[0365] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0366] Step 1:

[0367] User Input and Emotion Recognition

[0368] Using a dedicated interface, users input their past information (such as work history, education, and qualifications), areas of interest, and future goals. An emotion recognition engine also recognizes the user's emotional state (e.g., happiness, stress) in real time as they input. This input data and emotional data form the basis for subsequent processing steps.

[0369] Step 2:

[0370] Data transmission

[0371] The device formats the data entered by the user and the emotion data obtained by the emotion recognition engine into the required format, and sends it to the server using a secure communication protocol (e.g., HTTPS). The input data format can be JSON or XML.

[0372] Step 3:

[0373] Data reception and analysis

[0374] The server receives the data sent from the device. After receiving it, it validates the data and checks for and eliminates missing or invalid data. It then uses natural language processing and machine learning algorithms to analyze this data. Cloud infrastructure (e.g., AWS, Azure) supports this analysis process.

[0375] Step 4:

[0376] Scenario generation and emotional reflection

[0377] The server analyzes the received data using a generative AI model. It generates multiple future scenarios based on the user's past information, emotional data, and goals. The generated scenarios are then modified to reflect the user's emotional state (e.g., stress level, excitement level). This results in the generation of the optimal scenario for the user.

[0378] Step 5:

[0379] Scenario evaluation

[0380] The server evaluates the risk (e.g., probability of going over budget) and likelihood of success (e.g., probability of achieving goals) of each generated scenario, taking into account market trends, skill acquisition difficulty, past successes and failures, and sentiment data.

[0381] Step 6:

[0382] Optimal scenario selection and data return

[0383] The server selects the most suitable scenario for the user based on the evaluation results, generates a report containing detailed information about the selected scenario (e.g., future timeline, analysis of risks and success probability, and emotional reflection results), and sends it to the terminal.

[0384] Step 7:

[0385] Data reception and display

[0386] The terminal receives the information sent from the server and displays it in a user-friendly format. The displayed content includes a future timeline, a risk simulation diagram, a specific action plan, and advice based on the user's emotional state. The user can then make decisions based on this information.

[0387] Specific examples

[0388] For example, a user can input their "past purchase history" and "future goals" via their smartphone, and the emotion recognition engine will monitor their emotional state at the time. The device sends this data to a server, which analyzes it and generates an optimal financial planning scenario, which is then sent back to the smartphone. The user can then confirm, modify, and accept the scenario displayed on their smartphone, resulting in a more precise and personalized financial plan.

[0389] Prompt Sentence Examples

[0390] "Enter your past purchasing history and future goals. And give us the permission we need to recognize your emotions in real time as you type. We'll give you the best financial plan."

[0391] 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.

[0392] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0393] 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.

[0394] [Second embodiment]

[0395] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0396] 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.

[0397] 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).

[0398] 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.

[0399] 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.

[0400] 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).

[0401] 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. 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] In the smart glasses 214, 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.

[0406] 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."

[0407] This invention provides a system in which a user inputs their own past information, interests, and goals, and a generative AI model simulates multiple future scenarios based on that information. Specific embodiments of the system include the following:

[0408] Program processing

[0409] 1. User Input

[0410] Users enter their past information (such as work history, educational history, and qualifications), areas of interest, and future goals through a dedicated interface. This information is provided in a form format.

[0411] 2. Data Transmission

[0412] The terminal formats the data entered by the user and sends it to the server using a secure communication protocol, and also verifies that the data is formatted correctly.

[0413] 3. Data Reception and Analysis

[0414] The server receives the data, validates it, and checks for any incorrect or missing data before beginning its analysis, using natural language processing and machine learning algorithms.

[0415] 4. Scenario generation and evaluation

[0416] The server uses a generative AI model to generate multiple future scenarios based on the analysis results. For each scenario, it evaluates the risk (factors that could hinder success) and probability of success (the probability that the scenario will succeed). Specifically, it takes into account market trends, the difficulty of acquiring skills, and related success and failure cases.

[0417] 5. Selecting the optimal scenario and returning the data

[0418] Based on the evaluation results, the server selects the future scenario that best suits the user and generates detailed information related to it (e.g., a future timeline and an analysis of risks and the likelihood of success), which is then sent back to the terminal.

[0419] 6. Data reception and display

[0420] The device receives the information sent from the server and displays it in a user-friendly format, including future timelines, risk simulation diagrams, and specific action plans. A visually appealing design is used to make the information easy to understand.

[0421] Specific examples

[0422] Example 1: A user considering changing jobs to a different industry

[0423] The user enters their own information (e.g., "Current occupation: Engineer," "Area of ​​interest: Data science," "Goal: Change careers to become a data scientist").

[0424] The terminal sends this information to the server.

[0425] The server analyzes the data and generates scenarios, including "Career Change to Data Science Industry," assessing risks (e.g., skill gaps) and likelihood of success (e.g., industry demand).

[0426] The server selects the optimal scenario, which is to "take an online course to acquire skills, then start job hunting," and sends the relevant data to the terminal.

[0427] The device displays this scenario as a future timeline and risk simulation diagram, providing the user with an optimal action plan.

[0428] Example 2: A user considering starting a business

[0429] The user inputs his / her own information (for example, "current occupation: marketing manager", "area of ​​interest: e-commerce business", "goal: launching one's own e-commerce site").

[0430] The terminal sends this information to the server.

[0431] The server analyzes the input data, generates a scenario including "launching an e-commerce business," and evaluates risks (e.g., market competition, fundraising) and success probability (e.g., market demand, competitive analysis).

[0432] The server selects the scenario of "fundraising plan, marketing strategy development, and initial investment amount setting" as the optimal plan and transmits the related information to the terminal.

[0433] The device displays a future timeline, risk simulation diagrams, and specific action plans to support users in making decisions.

[0434] The system helps users choose the best path to their goals and create specific plans. By visually presenting the risks and success probability of scenarios, users can make decisions with more confidence.

[0435] The processing flow will be explained below.

[0436] Step 1:

[0437] Users enter their past information (work history, educational history, qualifications, etc.), areas of interest, and future goals through a dedicated interface (such as a web form or application form). This information is provided in form format.

[0438] Step 2:

[0439] The terminal formats the data entered by the user into the required format and sends it to the server using a secure communication protocol (e.g. HTTPS), and also verifies that the data is formatted correctly.

[0440] Step 3:

[0441] The server receives the data and verifies its contents. After checking for any invalid or missing data, it begins analyzing it.

[0442] Step 4:

[0443] The server analyzes user information using natural language processing and machine learning algorithms, for example, to identify the user's skill set based on their work history and educational background, and evaluates their suitability for the goals.

[0444] Step 5:

[0445] The server uses the generative AI model to generate multiple future scenarios based on the user's goals, such as "changing jobs to a different industry," "starting a business," and "career advancement."

[0446] Step 6:

[0447] The server evaluates the risk of each scenario (e.g., difficulty of acquiring the necessary skills, possibility of failure) and the likelihood of success (e.g., market demand, success stories), taking into account past data and current market trends.

[0448] Step 7:

[0449] Based on the evaluation results, the server selects the most suitable scenario for the user, based on criteria such as the user's goals, past experience, and market demand.

[0450] Step 8:

[0451] The server generates a report containing detailed information about the selected scenario (such as a future timeline and analysis of risks and likelihood of success) and sends it to the terminal.

[0452] Step 9:

[0453] The terminal receives the information sent from the server and displays it in a user-friendly format, including a future timeline, risk simulation diagrams, and specific action plans.

[0454] Step 10:

[0455] The user refers to the displayed information, and if they have further questions or need additional information, they return to step 1 and enter additional information.

[0456] Example 1

[0457] 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."

[0458] Conventional systems require a lot of effort to generate and evaluate future scenarios based on an individual's past information and goals. Furthermore, it is difficult to properly evaluate and present the risks and success probability of these scenarios, resulting in a lack of specific and reliable information for users to create optimal action plans. This makes it difficult for users to make optimal choices toward their future goals.

[0459] 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.

[0460] In this invention, the server includes means for verifying received data and analyzing the data using natural language processing and machine learning algorithms, means for generating multiple future scenarios using a generative AI model based on the analysis results and evaluating the risks and success probability of each scenario, and means for selecting the optimal scenario based on the evaluation results and returning data including detailed information to the terminal. This allows the user to obtain a specific and reliable future scenario based on their own past information and goals, and to visually understand the risks and success probability of that scenario.

[0461] A "user" is an individual who utilizes the system to input information about their past, interests, and future goals.

[0462] A "terminal" is a device that formats information entered by a user, transmits it to a server using a secure communications protocol, and receives and visually displays information returned from the server.

[0463] The "server" is a device that verifies the received data, analyzes the data using natural language processing and machine learning algorithms, and generates and evaluates scenarios using a generative AI model based on the results.

[0464] "Input data" is information including past information, interests and future goals entered by a user through a dedicated interface.

[0465] "Natural language processing" is an information processing technology that allows computers to understand, interpret, and manipulate human language.

[0466] A "machine learning algorithm" is a technology that allows computers to automatically acquire knowledge and patterns by analyzing and learning from data.

[0467] A "generative AI model" is an artificial intelligence model that generates future scenarios based on analysis results.

[0468] A "scenario" is a plan showing multiple possible futures created by a generative AI model based on user input data.

[0469] A "risk" is a factor that prevents a scenario from being successful.

[0470] "Probability of success" is the probability or likelihood that a scenario will succeed.

[0471] "Evaluation results" are the results of evaluating the risks and likelihood of success for each scenario generated by the generative AI model.

[0472] "Detailed information" refers to specific data related to the scenario, such as a future timeline and an analysis of risks and likelihood of success.

[0473] This invention provides a system in which a user inputs their past information, interests, and goals, and a generative AI model simulates multiple future scenarios based on that information. Specific embodiments are as follows.

[0474] Users enter their past information (e.g., work history, educational history, qualifications, etc.), areas of interest, and future goals through a dedicated interface. This information is provided in the form of a form. This interface is an application that can be used on a web browser and is developed using HTML and JavaScript.

[0475] The terminal formats the data entered by the user into JSON format and sends it to the server using a secure communication protocol (e.g., HTTPS). At this time, the terminal also has a function to verify the redundancy and inconsistency of the input data. The terminal is an ordinary personal computer or smartphone, and is operated via a web browser.

[0476] The server verifies the received data and performs data analysis using natural language processing (e.g., NLTK or SpaCy) and machine learning algorithms. The server may, for example, be a high-performance cloud server or a server in a data center. After analyzing the data, it generates multiple future scenarios using a generative AI model (e.g., GPT-3) and evaluates the risks (factors that hinder success) and success probability (the probability that the scenario will succeed) for each scenario. Statistical models and probabilistic methods are used for the evaluation.

[0477] The server selects the most suitable future scenario for the user based on the evaluation results. This selection is based on multiple evaluation indicators. Detailed information related to the selected scenario (e.g., a future timeline and analysis results of risks and success probability) is generated and sent back to the terminal. The returned data is compressed using a compression algorithm (e.g., gzip) for efficient transmission.

[0478] The terminal receives the information sent from the server and displays it in a user-friendly format, including future timelines, risk simulation diagrams, and specific action plans, using a visually appealing design and front-end libraries (e.g., React).

[0479] As a concrete example, consider the case of a user considering changing jobs to a different industry. The user enters their own information (e.g., "Current occupation: engineer," "Area of ​​interest: data science," "Goal: change jobs as a data scientist"). The device sends this information to a server, which analyzes the data and generates scenarios that include "changing jobs to the data science industry." It evaluates risks (e.g., skill gaps) and likelihood of success (e.g., industry demand) and selects the optimal scenario: "Take online courses to acquire skills, then begin job hunting." The device then displays these scenarios as a future timeline and risk simulation diagram, providing the user with an optimal action plan.

[0480] The same applies to users considering starting a business. The user enters their personal information (e.g., "Current occupation: marketing manager," "Area of ​​interest: e-commerce business," "Goal: launching their own e-commerce website") and their device sends this information to the server. The server analyzes the input data, generates scenarios including "launching an e-commerce business," and evaluates the risks (e.g., market competition, fundraising) and likelihood of success (e.g., market demand, competitive analysis). It selects the optimal scenario, "planning fundraising, formulating a marketing strategy, and setting an initial investment amount," and sends the relevant information to the device. The device then displays a future timeline, a risk simulation diagram, and a specific action plan to support the user's decision-making. This system makes it easy for users to select the optimal path toward their goals and create a specific plan. By visually presenting the scenario's risks and likelihood of success, users can make decisions with greater confidence.

[0481] Prompt Sentence Examples

[0482] "I'm an engineer with an interest in data science. I'd like to change careers and become a data scientist. How can I achieve this goal?"

[0483] "I'm a marketing manager and I'm interested in the e-commerce business. I'm considering launching my own e-commerce site, but what approach should I take to ensure success?"

[0484] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0485] Step 1:

[0486] Through a dedicated interface, users input their past information (work history, educational background, qualifications, etc.), areas of interest, and future goals. For example, a user might enter information such as "Current occupation: engineer," "Area of ​​interest: data science," and "Goal: change jobs to become a data scientist" into a web form. The input data is entered as text into each field of the form, and users can proceed to the next step by clicking the submit button.

[0487] Step 2:

[0488] The terminal formats the data entered by the user into JSON format and verifies the accuracy and integrity of the data. Specifically, it checks whether the input data is complete and whether all required fields are filled in. It then sends the formatted data to the server using a secure communication protocol (HTTPS). The input is the information entered by the user, and the output is the data converted to JSON format.

[0489] Step 3:

[0490] The server receives the data sent from the terminal. After receiving it, it verifies the validity of the data again and checks for invalid or missing data. If there is a problem at this stage, it generates an error message and returns it to the terminal. The input is JSON-formatted data, and the output is the validated data or an error message.

[0491] Step 4:

[0492] The server analyzes the received data using a natural language processing (NLP) engine or machine learning algorithm. Specifically, it uses an NLP library (e.g., NLTK or SpaCy) to automatically extract the user's interests and goals. The input is the validated data, and the output is the analysis results (e.g., keyword extraction and category classification).

[0493] Step 5:

[0494] The server uses a generative AI model (e.g., GPT-3) to generate multiple future scenarios based on the analysis results. The input is the analyzed data, and the output is the generated scenarios. Specifically, the generative AI model uses a text generation engine to create multiple scenarios showing possible future outcomes.

[0495] Step 6:

[0496] The server evaluates the risk and probability of success for each generated scenario. Statistical models and probabilistic methods are used for risk evaluation. The input is the generated scenario, and the output is the evaluation results of the risk and probability of success for each scenario.

[0497] Step 7:

[0498] The server selects the optimal scenario based on the evaluation results. Specifically, it scores each scenario based on multiple evaluation indicators and selects the scenario with the highest score. The input is the evaluation results of risk and probability of success, and the output is the optimal scenario.

[0499] Step 8:

[0500] The server generates detailed information related to the selected scenario (future timeline and analysis results of risks and success probability), compresses it, and sends it back to the terminal. The input is the optimal scenario, and the output is the compressed detailed information.

[0501] Step 9:

[0502] The terminal receives the information sent from the server and checks the validity of the data. If there are no problems with the received data, it decompresses the data and visually displays it in a format that is easy for the user to understand. A front-end library (e.g., React) is used for the display. The input is the compressed detailed information, and the output is a visual display of the decompressed data.

[0503] (Application example 1)

[0504] 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."

[0505] Conventional advertising systems only consider a user's past behavior and interests, and are unable to provide personalized advertisements based on future purchasing scenarios. This makes it difficult to display the most beneficial advertisements to users at the right time. A solution to this problem is needed.

[0506] 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.

[0507] In this invention, the server includes: means for inputting an individual's past information, interests, and goals; means for transmitting the input information to the server; means for using a generative AI model on the server to simulate multiple future scenarios based on the input information and evaluate their risks and likelihood of success; means for selecting an optimal scenario based on the evaluation results and transmitting related information again to the terminal; means for visually displaying the received evaluation results and the optimal scenario on the terminal; and means for displaying appropriate advertisements to the user based on the generated scenarios. This makes it possible to provide personalized advertisements based on the future purchasing scenarios that are most suitable for the user.

[0508] "Personal past information" refers to data such as the user's work history, educational background, qualifications, and purchasing history.

[0509] "Interests" refers to areas or matters in which a user is interested, specifically, hobbies, purchasing preferences, etc.

[0510] "Goals" are specific goals or expected results that a user wants to achieve in the future.

[0511] A "generative AI model" is an artificial intelligence model used to simulate future scenarios based on input data and assess their risks and likelihood of success.

[0512] A "server" is a computer system that receives data submitted by users and analyzes and evaluates the data using a generative AI model.

[0513] A "terminal" is a device that allows a user to input data and visually display evaluation results and scenarios sent from a server.

[0514] A "future scenario" is a future progression pattern that is simulated based on the user's current information and goals.

[0515] "Risks and likelihood of success" is an assessment of the obstacles and feasibility of each scenario coming to fruition.

[0516] "Visually displaying" means presenting information graphically in a format that is easy for a user to understand.

[0517] "Advertising" is a means of communication for providing users with information about products and services.

[0518] "Personalized advertising" refers to advertising that is optimized based on a user's past information and future purchasing scenarios.

[0519] The system for implementing this invention inputs an individual's past information, interests, and goals, and then uses a generative AI model to simulate future purchasing scenarios based on that information, providing optimal personalized advertising to the user. Specific embodiments are described below.

[0520] The system consists of the following main components:

[0521] 1. User device: Includes a means for inputting personal information (such as work history, educational background, qualifications, and purchasing history), interests (hobbies and purchasing preferences), and goals (specific goals and desired outcomes for the future). The device can be a smartphone, smart glasses, or a head-mounted display.

[0522] 2. Server: Receives information input from the user device and analyzes and evaluates it using a generative AI model. The server uses natural language processing and machine learning algorithms to generate future scenarios and evaluates the risk and likelihood of success for each scenario.

[0523] 3. Generative AI model: An artificial intelligence model that analyzes input data from users and simulates multiple future scenarios.

[0524] 4. Advertisement display function: Includes a means for visually displaying the most suitable advertisement for the user based on the evaluation results and optimal scenario received from the server.

[0525] Users input their purchasing history, interests, and future purchase plans via a smartphone app or smart glasses. This information is sent to a server using a secure communication protocol. The server then analyzes the input data using a generative AI model to generate and evaluate multiple future scenarios. The results of these evaluations are then sent back to the device, and the user receives the information in a visually easy-to-understand format. At the same time, personalized advertisements based on the generated scenarios are displayed, providing the most useful advertisements to the user at the right time.

[0526] For example:

[0527] For example, if a user inputs the goal of "buying a new smartphone," the generative AI model will generate an optimal future scenario based on the user's past purchasing history (e.g., "bought a laptop a year ago" and "bought a tablet six months ago") and interests (e.g., "gadgets"). Based on this scenario, the user will be shown a discount promotional ad for a new smartphone. In this way, the user can receive ads that best suit their purchasing intentions.

[0528] Example prompt sentence:

[0529] "Please describe your past purchase history and future interests. For example, 'Past: Smartphones', 'Interest: Gadgets', 'Goal: Buy a new smartphone'."

[0530] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0531] Step 1: The user inputs their past purchasing history, interests, and future goals into the user device. Specifically, they input information in a form using a smartphone app or smart glasses. This input data includes, for example, "smartphone," "gadget," and "purchase a new smartphone." The input in this step is the user's past information, interests, and goals, and the output is the formatted input data.

[0532] Step 2: The terminal sends the user's input data to the server using a secure communication protocol. This transmission also includes formatting and verifying the correctness of the data. The input of this step is the user's input data, and the output is the data sent to the server.

[0533] Step 3: The server receives the data sent from the device, checks for any invalid or missing data, and begins analysis. This analysis uses natural language processing and machine learning algorithms. The input of this step is the received data, and the output is the analysis result.

[0534] Step 4: The server uses the generative AI model to generate multiple future scenarios based on the analysis results and evaluates the risk and likelihood of success for each scenario. Specifically, it refers to data such as market trends, difficulty of acquiring skills, and related success and failure cases. The input for this step is the analyzed data, and the output is the evaluated scenario.

[0535] Step 5: Based on the evaluation results, the server selects the future scenario that best suits the user and generates detailed information about it. The detailed information includes a future timeline, risk, and success probability analysis results. The input to this step is the evaluated scenario, and the output is the selected optimal scenario and its detailed information.

[0536] Step 6: The server again transmits the selected optimal scenario and related detailed information to the terminal. The input of this step is the detailed information of the selected optimal scenario, and the output is the data transmitted to the terminal.

[0537] Step 7: The terminal receives the information sent from the server and visually displays it in a user-friendly format. This display includes a future timeline, a risk simulation diagram, and a specific action plan. Appropriate advertisements are also displayed at the same time. The input of this step is the scenario information received from the server, and the output is the displayed information and advertisements.

[0538] Step 8: The user checks the information and advertisements displayed on the device and selects the appropriate action. The user makes purchasing decisions and makes purchases based on this information. The input to this step is the information and advertisements displayed on the device, and the output is the user's decision to act.

[0539] 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.

[0540] This invention combines a system in which a user inputs their past information, interests, and goals, and a generative AI model simulates multiple future scenarios based on that information, with an emotion engine that recognizes the user's emotions, enabling more precise scenario generation and selection that takes the user's emotions into account.

[0541] Program processing

[0542] 1. User Input and Emotion Recognition

[0543] Through a dedicated interface, users input their past information (work history, educational background, qualifications, etc.), areas of interest, and future goals. In addition, the emotion engine recognizes the user's emotional state in real time as they input and interact.

[0544] 2. Data Transmission

[0545] The terminal formats the data input by the user and the emotion data detected by the emotion engine into the required format and transmits it to the server using a secure communication protocol.

[0546] 3. Data Reception and Analysis

[0547] The server receives the transmitted data, verifies its contents, and begins data analysis after checking for any invalid or missing data.

[0548] 4. Scenario generation and emotional reflection

[0549] The server uses natural language processing and machine learning algorithms to analyze user and emotional information, for example, to identify a user's skill set based on their work history and educational background, and to assess its suitability for their goals and emotional state.

[0550] The server uses a generative AI model to generate multiple future scenarios that take into account the user's goals and emotions. Emotional data is used to modify the scenarios to reflect the user's stress level and excitement state.

[0551] 5. Evaluating the scenario

[0552] The server evaluates the risk of each scenario (e.g., difficulty of acquiring the required skills, probability of failure) and the likelihood of success (e.g., market demand, success stories). The evaluation takes into account not only past data and current market trends, but also the user's emotional state.

[0553] 6. Selecting the optimal scenario and returning the data

[0554] The server selects the most suitable scenario for the user based on the evaluation results, with the selection criteria including the user's goals, past experiences, market demands, and the user's emotional state.

[0555] The server generates a report containing detailed information about the selected scenario (future timeline, analysis results of risks and probability of success, and emotional reflection results) and sends it to the terminal.

[0556] 7. Data reception and display

[0557] The device receives the information sent from the server and displays it in a user-friendly format, including a future timeline, risk simulation diagrams, specific action plans, and advice based on emotional state.

[0558] 8. User Feedback

[0559] The user can refer to the displayed information and, if they have further questions or need additional information, they can enter the initial information again. The new input and emotion data will be used for further analysis.

[0560] Specific examples

[0561] Example 1: A user considering changing jobs to a different industry

[0562] Users enter their own information (e.g., "Current occupation: engineer," "Area of ​​interest: data science," "Goal: change careers to become a data scientist"), and the emotion engine also recognizes their emotions (e.g., excitement, anticipation).

[0563] The terminal transmits this information and emotion data to the server.

[0564] The server analyzes the data and generates scenarios, including "Career Change to Data Science Industry." It evaluates modified scenarios taking into account risks (e.g., skill gaps), likelihood of success (e.g., industry demand), and sentiment.

[0565] The server selects the optimal scenario, which is to "take an online course to acquire skills, then start job hunting," and sends the relevant data to the terminal.

[0566] The device displays this scenario as a future timeline and risk simulation diagram, providing the user with an optimal action plan.

[0567] Example 2: A user considering starting a business

[0568] Users enter their own information (e.g., "Current occupation: Marketing manager," "Area of ​​interest: E-commerce business," "Goal: Launching their own e-commerce site"), and the emotion engine also recognizes their emotions at the time (e.g., anxiety, expectation).

[0569] The terminal transmits this information and emotion data to the server.

[0570] The server analyzes the input data and sentiment data to generate scenarios including "launching an e-commerce business." It evaluates revised scenarios taking into account risks (e.g., market competition, fundraising), success probability (e.g., market demand, competitive analysis), and sentiment.

[0571] The server selects the scenario of "fundraising plan, marketing strategy development, and initial investment amount setting" as the optimal plan and transmits the related information to the terminal.

[0572] The device displays future timelines, risk simulation diagrams, and specific action plans to support users' decisions and provide additional emotional advice.

[0573] This system enables more precise scenario generation and optimization that takes into account the user's emotions, providing more reliable future planning.

[0574] The processing flow will be explained below.

[0575] Step 1:

[0576] Through a dedicated interface, users input their past information (such as work history, educational background, and qualifications), areas of interest, and future goals. At the same time, the emotion engine recognizes the user's emotional state (e.g., excitement, anticipation, anxiety) in real time from their facial expressions and input patterns as they input and interact.

[0577] Step 2:

[0578] The terminal formats the data input by the user and the emotion data analyzed by the emotion engine into the required format and transmits it to the server using a secure communication protocol (e.g., HTTPS).

[0579] Step 3:

[0580] The server receives the transmitted data, verifies its contents, and begins data analysis after checking for any invalid or missing data.

[0581] Step 4:

[0582] The server uses natural language processing and machine learning algorithms to analyze user and emotional information, for example, to identify a user's skill set based on their work history and educational background, and to assess its suitability for their goals and emotional state.

[0583] Step 5:

[0584] The server uses a generative AI model to generate multiple future scenarios that take into account the user's goals and emotions. Specifically, it generates scenarios such as "changing jobs to a different industry," "starting a business," and "career advancement," and then modifies and customizes the scenarios based on emotional data.

[0585] Step 6:

[0586] The server evaluates the risk of each scenario (e.g., difficulty of acquiring the required skills, probability of failure) and the likelihood of success (e.g., market demand, success stories). The evaluation takes into account not only past data and current market trends, but also the user's emotional state.

[0587] Step 7:

[0588] The server selects the most suitable scenario for the user based on the evaluation results, with the selection criteria including the user's goals, past experiences, market demands, and the user's emotional state.

[0589] Step 8:

[0590] The server generates a report containing detailed information about the selected scenario (future timeline, analysis results of risks and probability of success, and emotional reflection results) and sends it to the terminal.

[0591] Step 9:

[0592] The device receives the information sent from the server and displays it in a user-friendly format, including future timelines, risk simulation diagrams, specific action plans, and advice based on the user's emotional state.

[0593] Step 10:

[0594] The user can refer to the displayed information and, if they have further questions or need additional information, they can enter the initial information again. The new input and emotion data will be used for further analysis.

[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 systems were able to simulate future scenarios based on an individual's past information and goals, but they were unable to generate or evaluate scenarios that took the user's emotional state into account. This meant that they were unable to provide accurate scenarios that reflected the user's real-time emotional state. Furthermore, scenarios that did not take emotional state into account often presented problems in that they were not realistic or effective for the user.

[0598] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting an individual's past information, interests, and goals; means for transmitting the input information and the user's emotional data to the server; means for using a generative AI model on the server to simulate multiple future scenarios based on the input information and emotional data and evaluate their risks and likelihood of success; means for selecting an optimal scenario based on the evaluation and transmitting related information again to the terminal; and means for visually displaying the received evaluation results and the optimal scenario on the terminal. This enables precise scenario generation and evaluation that takes the user's emotional state into consideration, making it possible to provide more realistic and effective future scenarios.

[0599] "Personal past information" is data on the user's past experiences, such as work history, educational history, and qualifications.

[0600] "Interests" are areas or activities that interest a user.

[0601] A "goal" is a specific objective or result that a user wants to achieve in the future.

[0602] "Input means" refers to the interface or device through which a user provides information to the system.

[0603] "Emotion data" is data that detects the user's emotional state in real time and expresses it as numerical values ​​or categories.

[0604] "Server" refers to a central processing unit that receives input information and emotion data, processes them, and analyzes them.

[0605] "Multiple future scenarios" are plans showing multiple different future possibilities that are generated based on the user's input information and emotion data.

[0606] A "generative AI model" is a program that uses artificial intelligence and machine learning algorithms to analyze data and generate future scenarios.

[0607] "Evaluation means" refers to methods and tools for analyzing risks and the likelihood of success based on the generated scenarios.

[0608] "Means for selecting the optimal scenario" refers to the method or process for selecting the most suitable scenario for the user based on the evaluation results.

[0609] "Terminal" refers to a device or computer system through which a user can enter information or view results.

[0610] "Visual display means" refers to a graphical interface or display device that displays analysis results and scenarios in an easy-to-understand manner for the user.

[0611] "Natural language processing" is a technology for analyzing and understanding human language.

[0612] "Machine learning algorithms" refer to mathematical methods and programs that automatically learn patterns and knowledge from data.

[0613] "Market trends" are data that show the current market situation and future trends.

[0614] "Skill acquisition difficulty" refers to the time and effort required to master a particular skill.

[0615] "Success stories and failure stories" refer to specific cases of success and failure obtained from past experience and data.

[0616] "JSON" is a lightweight data exchange format for structuring data.

[0617] "HTTPS" is a protocol for securely transmitting data over the Internet.

[0618] This invention is a system that uses a generative AI model to simulate multiple future scenarios based on user input information and emotion data, and evaluates the risk and likelihood of success of each scenario. An embodiment of this system is described in detail below.

[0619] System configuration

[0620] The system consists of the following major hardware and software components:

[0621] 1. User terminal: Provides an interface for users to input information (e.g., PC, smartphone, tablet).

[0622] 2. Emotion engine: Hardware (e.g., camera, microphone) and software (e.g., facial expression recognition program, voice analysis software) for detecting the user's emotional state in real time.

[0623] 3. Server: A central processing unit that receives data, analyzes, generates scenarios, and evaluates them. It is equipped with generative AI models and machine learning algorithms.

[0624] 4. Secure communication protocols: Protocols to ensure the safety of data transmission and reception (e.g. HTTPS).

[0625] Data Entry and Emotion Recognition

[0626] Users input past information (e.g., work history, educational background, qualification status), areas of interest, and future goals through their device. This can be done using a computer or smartphone keyboard or voice input. The emotion engine uses a camera and microphone to detect the user's emotional state in real time while they are typing. For example, when a user types "Current occupation: engineer," "Area of ​​interest: data science," and "Goal: change careers as a data scientist," their emotions (e.g., excitement, anticipation) are simultaneously recognized.

[0627] Data transmission and reception

[0628] The device formats the data and emotion data entered by the user and sends them to the server using a secure communication protocol. Specifically, the data is compiled in JSON format and encrypted using HTTPS before being sent. The server receives the data and verifies that there is no invalid or missing data.

[0629] Scenario generation and evaluation

[0630] The server uses natural language processing (NLP) and machine learning algorithms to analyze the user's input and emotional data. After analysis, a generative AI model is used to generate multiple future scenarios based on the user's goals and current situation. For example, a scenario could be generated in which the user takes an online course to acquire skills, then begins job hunting.

[0631] For each scenario, the server evaluates the risk (e.g., skill gaps, probability of failure) and likelihood of success (e.g., market demand, success stories). This evaluation takes into account market trends and historical data, as well as the user's emotional state. For example, if the user is "excited," challenging scenarios will be prioritized.

[0632] Optimal scenario selection and data return

[0633] Based on the evaluation results, the server selects the most suitable scenario for the user and generates a detailed report, including a future timeline, risk and success probability analysis, and sentiment-based advice. The report is sent to the device in JSON format.

[0634] Visual display and user feedback

[0635] The device receives the report sent from the server and displays the information in a user-friendly format, including a future timeline, risk simulation diagram, specific action plans, and advice based on the user's emotional state. If the user needs additional questions or information, they can re-enter it and send it back to the server.

[0636] Prompt Sentence Examples

[0637] An example prompt might be, "I'm looking to transition my career from engineer to data scientist. What steps should I take?"

[0638] Another prompt reads, "I would like to use my experience as a marketing manager to start an e-commerce business. Please let me know the appropriate plan."

[0639] This system enables more precise scenario generation and evaluation that takes into account the user's emotions, providing more realistic and effective future scenarios.

[0640] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0641] Step 1:

[0642] User Input and Emotion Recognition

[0643] Input: User's past information (work history, education history, qualifications), areas of interest, goals.

[0644] Specific operation: The user enters their past information, interests, and goals through the device interface. For example, they can enter "Current occupation: Engineer," "Area of ​​interest: Data science," and "Goal: Change careers as a data scientist" using keyboard input or voice input.

[0645] Input: User emotion data (real-time).

[0646] Specific operation: The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice to detect their emotional state (e.g., excitement, anticipation).

[0647] Output: Formatted data containing user input and emotion data.

[0648] Step 2:

[0649] Data transmission

[0650] Input: User profile data (history, interests, goals) and sentiment data.

[0651] Specific operation: The device formats the input data in JSON format and sends it to the server using a secure communication protocol (HTTPS). The data is sent in encrypted form.

[0652] Output: The formatted data sent to the server.

[0653] Step 3:

[0654] Data Receipt and Verification

[0655] Input: Submitted data (profile data, emotion data).

[0656] Specific operation: The server receives the data sent from the terminal and verifies whether there is any invalid or missing data, for example, whether all required fields are filled in and whether the data format is correct.

[0657] Output: Validated data.

[0658] Step 4:

[0659] Data analysis

[0660] Input: Verified data (profile data, sentiment data).

[0661] Specific operation: The server uses natural language processing (NLP) and machine learning algorithms to analyze the user's past information and goals. For example, it identifies the user's skill set from their work history and educational background, and matches it to their areas of interest and goals.

[0662] Output: Analysis results (data such as user skill sets, goals, and interests).

[0663] Step 5:

[0664] Scenario Generation

[0665] Input: Analysis results (data such as user skill sets, goals, and interests), sentiment data.

[0666] Specific actions: The server uses the generative AI model to generate multiple future scenarios based on the user's goals and current situation. For example, it creates a specific scenario such as "Take an online course to acquire skills, then start job hunting."

[0667] Output: Multiple future scenarios.

[0668] Step 6:

[0669] Emotional data reflection and scenario evaluation

[0670] Input: Future scenario, emotion data.

[0671] Specific operation: The server reflects the emotion data in the analysis results. This evaluates the risk and success probability of each scenario. For example, it takes into account the user's emotions (e.g., anticipation, excitement) and adjusts the risk items and success factors.

[0672] Output: The evaluated scenario.

[0673] Step 7:

[0674] Optimal scenario selection

[0675] Input: The evaluated scenario.

[0676] Specific operation: The server selects the most suitable scenario for the user based on the evaluation results, for example, the scenario with the lowest risk and the highest probability of success.

[0677] Output: Details of the optimal scenario.

[0678] Step 8:

[0679] Data return and report generation

[0680] Input: Best-case scenario details.

[0681] Specific operation: The server generates a report containing detailed information about the selected scenario and sends it to the device. The report includes a future timeline, an analysis of risks and success rates, and sentiment-based advice. The data is sent in JSON format.

[0682] Output: The report data sent.

[0683] Step 9:

[0684] Displaying received data

[0685] Input: Report data from the server.

[0686] Specific operations: The terminal receives the report sent from the server and displays the information in a user-friendly format, including a future timeline, a risk simulation diagram, a specific action plan, and advice based on the user's emotional state.

[0687] Output: Visually displayed information.

[0688] Step 10:

[0689] User Feedback

[0690] Input: Information displayed, ask additional questions or enter new information.

[0691] Specific operation: The user refers to the displayed information and, if they have further questions or need additional information, they enter the initial information again. The new input and emotion data are then used for further analysis.

[0692] Output: New user input data, emotion data.

[0693] (Application example 2)

[0694] 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."

[0695] Conventional systems generate future scenarios based on an individual's past information and goals, but because they fail to consider the user's emotional state, they may present scenarios that are not always optimal for the user. Furthermore, ignoring the user's emotional state can increase stress and lead to disappointing results when making important decisions such as financial planning, changing jobs, or starting a business.

[0696] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0697] In this invention, the server includes: means for inputting an individual's past information, interests, and goals; means for transmitting the input information and emotional data to the server; means for using a generative AI model on the server to simulate multiple future scenarios based on the input information and emotional data and evaluate their risks and likelihood of success; means for selecting an optimal scenario based on the evaluation results and transmitting related information again to the terminal; and means for visually displaying the received evaluation results and optimal scenario on the terminal. This enables the generation and selection of more precise and personalized scenarios that take the user's emotions into consideration, reducing user stress and achieving expected results.

[0698] "Individual past information" refers to data such as the individual's past work history, educational history, and qualifications.

[0699] "Interest" refers to an individual's interest in a field or area of ​​interest.

[0700] A goal is an objective or goal that an individual wants to achieve in the future.

[0701] "Emotional data" refers to data that detects and records an individual's emotional state in real time.

[0702] A "generative AI model" is an artificial intelligence model that uses natural language processing and machine learning to perform data analysis and simulations.

[0703] A "server" is a remote computer system that performs data analysis, evaluation, and scenario generation.

[0704] A "terminal" is a digital device used by an individual to enter and receive information.

[0705] "Risk" refers to factors or difficulties that could hinder success in a future scenario.

[0706] "Success probability" refers to the likelihood or likelihood that a particular scenario will achieve its goals.

[0707] A "scenario" is a hypothetical plan that details a specific situation or plan for the future.

[0708] The system based on this invention inputs an individual's past information and goals, recognizes emotional data, generates multiple future scenarios, and evaluates their risks and likelihood of success. The main components are a terminal where the user inputs data, a server that transmits, receives, analyzes, and evaluates the data, and a scenario generation function that uses a generative AI model.

[0709] System Program

[0710] The system's program uses a generative AI model incorporating an emotion recognition engine to analyze the user's input data and emotional data, and generate an optimal scenario. The main parts of the program are as follows:

[0711] 1. User Input and Emotion Recognition

[0712] The device allows users to input past information (such as work history, educational background, and qualifications), areas of interest, and future goals through a dedicated interface. At the same time, an emotion recognition engine recognizes the user's emotional state in real time. The emotion recognition engine uses common hardware (such as smartphones and tablets) and emotion recognition software (such as Affectiva).

[0713] 2. Data Transmission

[0714] The device formats the input data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS).

[0715] 3. Data Reception and Analysis

[0716] The server receives the data and validates it. After checking for missing or invalid data, it begins analyzing the data. The analysis uses cloud infrastructure (e.g., AWS, Azure) and natural language processing and machine learning algorithms.

[0717] 4. Scenario generation and emotional reflection

[0718] The server uses natural language processing and machine learning algorithms to analyze user information and emotional information. It uses a generative AI model to generate multiple future scenarios that take into account the user's goals and emotions. Emotional data is used to modify the scenarios to reflect the user's stress level and excitement state.

[0719] 5. Evaluating the scenario

[0720] The server assesses the risk and likelihood of success of each scenario, taking into account not only historical data and current market trends, but also the user's emotional state.

[0721] 6. Selecting the optimal scenario and returning the data

[0722] The server selects the most suitable scenario for the user based on the evaluation results. The selection criteria include the user's goals, past experiences, market demand, and the user's emotional state. A report containing detailed information about the selected scenario is generated and sent to the terminal.

[0723] 7. Data reception and display

[0724] The device receives the information sent from the server and displays it in a user-friendly format, including a future timeline, risk simulation diagrams, specific action plans, and advice based on emotional state.

[0725] Specific examples

[0726] For example, a user can input their "past purchase history" and "future goals" via their smartphone, and the emotion recognition engine will monitor their emotional state at the time. The device sends this data to a server, which analyzes it and generates an optimal financial planning scenario, which is then sent back to the smartphone. The user can then confirm, modify, and accept the scenario displayed on their smartphone, resulting in a more precise and personalized financial plan.

[0727] Prompt Sentence Examples

[0728] "Enter your past purchasing history and future goals. And give us the permission we need to recognize your emotions in real time as you type. We'll give you the best financial plan."

[0729] The above is the basic form for carrying out the present invention. This makes it possible to generate and select scenarios that take into account the user's emotions, thereby more effectively supporting the user's decision-making.

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

[0731] Step 1:

[0732] User Input and Emotion Recognition

[0733] Using a dedicated interface, users input their past information (such as work history, education, and qualifications), areas of interest, and future goals. An emotion recognition engine also recognizes the user's emotional state (e.g., happiness, stress) in real time as they input. This input data and emotional data form the basis for subsequent processing steps.

[0734] Step 2:

[0735] Data transmission

[0736] The device formats the data entered by the user and the emotion data obtained by the emotion recognition engine into the required format, and sends it to the server using a secure communication protocol (e.g., HTTPS). The input data format can be JSON or XML.

[0737] Step 3:

[0738] Data reception and analysis

[0739] The server receives the data sent from the device. After receiving it, it validates the data and checks for and eliminates missing or invalid data. It then uses natural language processing and machine learning algorithms to analyze this data. Cloud infrastructure (e.g., AWS, Azure) supports this analysis process.

[0740] Step 4:

[0741] Scenario generation and emotional reflection

[0742] The server analyzes the received data using a generative AI model. It generates multiple future scenarios based on the user's past information, emotional data, and goals. The generated scenarios are then modified to reflect the user's emotional state (e.g., stress level, excitement level). This results in the generation of the optimal scenario for the user.

[0743] Step 5:

[0744] Scenario evaluation

[0745] The server evaluates the risk (e.g., probability of going over budget) and likelihood of success (e.g., probability of achieving goals) of each generated scenario, taking into account market trends, skill acquisition difficulty, past successes and failures, and sentiment data.

[0746] Step 6:

[0747] Optimal scenario selection and data return

[0748] The server selects the most suitable scenario for the user based on the evaluation results, generates a report containing detailed information about the selected scenario (e.g., future timeline, analysis of risks and success probability, and emotional reflection results), and sends it to the terminal.

[0749] Step 7:

[0750] Data reception and display

[0751] The terminal receives the information sent from the server and displays it in a user-friendly format. The displayed content includes a future timeline, a risk simulation diagram, a specific action plan, and advice based on the user's emotional state. The user can then make decisions based on this information.

[0752] Specific examples

[0753] For example, a user can input their "past purchase history" and "future goals" via their smartphone, and the emotion recognition engine will monitor their emotional state at the time. The device sends this data to a server, which analyzes it and generates an optimal financial planning scenario, which is then sent back to the smartphone. The user can then confirm, modify, and accept the scenario displayed on their smartphone, resulting in a more precise and personalized financial plan.

[0754] Prompt Sentence Examples

[0755] "Enter your past purchasing history and future goals. And give us the permission we need to recognize your emotions in real time as you type. We'll give you the best financial plan."

[0756] 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.

[0757] 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.

[0758] 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.

[0759] [Third embodiment]

[0760] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0761] 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.

[0762] 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).

[0763] 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.

[0764] 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.

[0765] 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).

[0766] 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. 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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."

[0772] This invention provides a system in which a user inputs their own past information, interests, and goals, and a generative AI model simulates multiple future scenarios based on that information. Specific embodiments of the system include the following:

[0773] Program processing

[0774] 1. User Input

[0775] Users enter their past information (such as work history, educational history, and qualifications), areas of interest, and future goals through a dedicated interface. This information is provided in a form format.

[0776] 2. Data Transmission

[0777] The terminal formats the data entered by the user and sends it to the server using a secure communication protocol, and also verifies that the data is formatted correctly.

[0778] 3. Data Reception and Analysis

[0779] The server receives the data, validates it, and checks for any incorrect or missing data before beginning its analysis, using natural language processing and machine learning algorithms.

[0780] 4. Scenario generation and evaluation

[0781] The server uses a generative AI model to generate multiple future scenarios based on the analysis results. For each scenario, it evaluates the risk (factors that could hinder success) and probability of success (the probability that the scenario will succeed). Specifically, it takes into account market trends, the difficulty of acquiring skills, and related success and failure cases.

[0782] 5. Selecting the optimal scenario and returning the data

[0783] Based on the evaluation results, the server selects the future scenario that best suits the user and generates detailed information related to it (e.g., a future timeline and an analysis of risks and the likelihood of success), which is then sent back to the terminal.

[0784] 6. Data reception and display

[0785] The device receives the information sent from the server and displays it in a user-friendly format, including future timelines, risk simulation diagrams, and specific action plans. A visually appealing design is used to make the information easy to understand.

[0786] Specific examples

[0787] Example 1: A user considering changing jobs to a different industry

[0788] The user enters their own information (e.g., "Current occupation: Engineer," "Area of ​​interest: Data science," "Goal: Change careers to become a data scientist").

[0789] The terminal sends this information to the server.

[0790] The server analyzes the data and generates scenarios, including "Career Change to Data Science Industry," assessing risks (e.g., skill gaps) and likelihood of success (e.g., industry demand).

[0791] The server selects the optimal scenario, which is to "take an online course to acquire skills, then start job hunting," and sends the relevant data to the terminal.

[0792] The device displays this scenario as a future timeline and risk simulation diagram, providing the user with an optimal action plan.

[0793] Example 2: A user considering starting a business

[0794] The user inputs his / her own information (for example, "current occupation: marketing manager", "area of ​​interest: e-commerce business", "goal: launching one's own e-commerce site").

[0795] The terminal sends this information to the server.

[0796] The server analyzes the input data, generates a scenario including "launching an e-commerce business," and evaluates risks (e.g., market competition, fundraising) and success probability (e.g., market demand, competitive analysis).

[0797] The server selects the scenario of "fundraising plan, marketing strategy development, and initial investment amount setting" as the optimal plan and transmits the related information to the terminal.

[0798] The device displays a future timeline, risk simulation diagrams, and specific action plans to support users in making decisions.

[0799] The system helps users choose the best path to their goals and create specific plans. By visually presenting the risks and success probability of scenarios, users can make decisions with more confidence.

[0800] The processing flow will be explained below.

[0801] Step 1:

[0802] Users enter their past information (work history, educational history, qualifications, etc.), areas of interest, and future goals through a dedicated interface (such as a web form or application form). This information is provided in form format.

[0803] Step 2:

[0804] The terminal formats the data entered by the user into the required format and sends it to the server using a secure communication protocol (e.g. HTTPS), and also verifies that the data is formatted correctly.

[0805] Step 3:

[0806] The server receives the data and verifies its contents. After checking for any invalid or missing data, it begins analyzing it.

[0807] Step 4:

[0808] The server analyzes user information using natural language processing and machine learning algorithms, for example, to identify the user's skill set based on their work history and educational background, and evaluates their suitability for the goals.

[0809] Step 5:

[0810] The server uses the generative AI model to generate multiple future scenarios based on the user's goals, such as "changing jobs to a different industry," "starting a business," and "career advancement."

[0811] Step 6:

[0812] The server evaluates the risk of each scenario (e.g., difficulty of acquiring the necessary skills, possibility of failure) and the likelihood of success (e.g., market demand, success stories), taking into account past data and current market trends.

[0813] Step 7:

[0814] Based on the evaluation results, the server selects the most suitable scenario for the user, based on criteria such as the user's goals, past experience, and market demand.

[0815] Step 8:

[0816] The server generates a report containing detailed information about the selected scenario (such as a future timeline and analysis of risks and likelihood of success) and sends it to the terminal.

[0817] Step 9:

[0818] The terminal receives the information sent from the server and displays it in a user-friendly format, including a future timeline, risk simulation diagrams, and specific action plans.

[0819] Step 10:

[0820] The user refers to the displayed information, and if they have further questions or need additional information, they return to step 1 and enter additional information.

[0821] Example 1

[0822] 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."

[0823] Conventional systems require a lot of effort to generate and evaluate future scenarios based on an individual's past information and goals. Furthermore, it is difficult to properly evaluate and present the risks and success probability of these scenarios, resulting in a lack of specific and reliable information for users to create optimal action plans. This makes it difficult for users to make optimal choices toward their future goals.

[0824] 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.

[0825] In this invention, the server includes means for verifying received data and analyzing the data using natural language processing and machine learning algorithms, means for generating multiple future scenarios using a generative AI model based on the analysis results and evaluating the risks and success probability of each scenario, and means for selecting the optimal scenario based on the evaluation results and returning data including detailed information to the terminal. This allows the user to obtain a specific and reliable future scenario based on their own past information and goals, and to visually understand the risks and success probability of that scenario.

[0826] A "user" is an individual who utilizes the system to input information about their past, interests, and future goals.

[0827] A "terminal" is a device that formats information entered by a user, transmits it to a server using a secure communications protocol, and receives and visually displays information returned from the server.

[0828] The "server" is a device that verifies the received data, analyzes the data using natural language processing and machine learning algorithms, and generates and evaluates scenarios using a generative AI model based on the results.

[0829] "Input data" is information including past information, interests and future goals entered by a user through a dedicated interface.

[0830] "Natural language processing" is an information processing technology that allows computers to understand, interpret, and manipulate human language.

[0831] A "machine learning algorithm" is a technology that allows computers to automatically acquire knowledge and patterns by analyzing and learning from data.

[0832] A "generative AI model" is an artificial intelligence model that generates future scenarios based on analysis results.

[0833] A "scenario" is a plan showing multiple possible futures created by a generative AI model based on user input data.

[0834] A "risk" is a factor that prevents a scenario from being successful.

[0835] "Probability of success" is the probability or likelihood that a scenario will succeed.

[0836] "Evaluation results" are the results of evaluating the risks and likelihood of success for each scenario generated by the generative AI model.

[0837] "Detailed information" refers to specific data related to the scenario, such as a future timeline and an analysis of risks and likelihood of success.

[0838] This invention provides a system in which a user inputs their past information, interests, and goals, and a generative AI model simulates multiple future scenarios based on that information. Specific embodiments are as follows.

[0839] Users enter their past information (e.g., work history, educational history, qualifications, etc.), areas of interest, and future goals through a dedicated interface. This information is provided in the form of a form. This interface is an application that can be used on a web browser and is developed using HTML and JavaScript.

[0840] The terminal formats the data entered by the user into JSON format and sends it to the server using a secure communication protocol (e.g., HTTPS). At this time, the terminal also has a function to verify the redundancy and inconsistency of the input data. The terminal is an ordinary personal computer or smartphone, and is operated via a web browser.

[0841] The server verifies the received data and performs data analysis using natural language processing (e.g., NLTK or SpaCy) and machine learning algorithms. The server may, for example, be a high-performance cloud server or a server in a data center. After analyzing the data, it generates multiple future scenarios using a generative AI model (e.g., GPT-3) and evaluates the risks (factors that hinder success) and success probability (the probability that the scenario will succeed) for each scenario. Statistical models and probabilistic methods are used for the evaluation.

[0842] The server selects the most suitable future scenario for the user based on the evaluation results. This selection is based on multiple evaluation indicators. Detailed information related to the selected scenario (e.g., a future timeline and analysis results of risks and success probability) is generated and sent back to the terminal. The returned data is compressed using a compression algorithm (e.g., gzip) for efficient transmission.

[0843] The terminal receives the information sent from the server and displays it in a user-friendly format, including future timelines, risk simulation diagrams, and specific action plans, using a visually appealing design and front-end libraries (e.g., React).

[0844] As a concrete example, consider the case of a user considering changing jobs to a different industry. The user enters their own information (e.g., "Current occupation: engineer," "Area of ​​interest: data science," "Goal: change jobs as a data scientist"). The device sends this information to a server, which analyzes the data and generates scenarios that include "changing jobs to the data science industry." It evaluates risks (e.g., skill gaps) and likelihood of success (e.g., industry demand) and selects the optimal scenario: "Take online courses to acquire skills, then begin job hunting." The device then displays these scenarios as a future timeline and risk simulation diagram, providing the user with an optimal action plan.

[0845] The same applies to users considering starting a business. The user enters their personal information (e.g., "Current occupation: marketing manager," "Area of ​​interest: e-commerce business," "Goal: launching their own e-commerce website") and their device sends this information to the server. The server analyzes the input data, generates scenarios including "launching an e-commerce business," and evaluates the risks (e.g., market competition, fundraising) and likelihood of success (e.g., market demand, competitive analysis). It selects the optimal scenario, "planning fundraising, formulating a marketing strategy, and setting an initial investment amount," and sends the relevant information to the device. The device then displays a future timeline, a risk simulation diagram, and a specific action plan to support the user's decision-making. This system makes it easy for users to select the optimal path toward their goals and create a specific plan. By visually presenting the scenario's risks and likelihood of success, users can make decisions with greater confidence.

[0846] Prompt Sentence Examples

[0847] "I'm an engineer with an interest in data science. I'd like to change careers and become a data scientist. How can I achieve this goal?"

[0848] "I'm a marketing manager and I'm interested in the e-commerce business. I'm considering launching my own e-commerce site, but what approach should I take to ensure success?"

[0849] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0850] Step 1:

[0851] Through a dedicated interface, users input their past information (work history, educational background, qualifications, etc.), areas of interest, and future goals. For example, a user might enter information such as "Current occupation: engineer," "Area of ​​interest: data science," and "Goal: change jobs to become a data scientist" into a web form. The input data is entered as text into each field of the form, and users can proceed to the next step by clicking the submit button.

[0852] Step 2:

[0853] The terminal formats the data entered by the user into JSON format and verifies the accuracy and integrity of the data. Specifically, it checks whether the input data is complete and whether all required fields are filled in. It then sends the formatted data to the server using a secure communication protocol (HTTPS). The input is the information entered by the user, and the output is the data converted to JSON format.

[0854] Step 3:

[0855] The server receives the data sent from the terminal. After receiving it, it verifies the validity of the data again and checks for invalid or missing data. If there is a problem at this stage, it generates an error message and returns it to the terminal. The input is JSON-formatted data, and the output is the validated data or an error message.

[0856] Step 4:

[0857] The server analyzes the received data using a natural language processing (NLP) engine or machine learning algorithm. Specifically, it uses an NLP library (e.g., NLTK or SpaCy) to automatically extract the user's interests and goals. The input is the validated data, and the output is the analysis results (e.g., keyword extraction and category classification).

[0858] Step 5:

[0859] The server uses a generative AI model (e.g., GPT-3) to generate multiple future scenarios based on the analysis results. The input is the analyzed data, and the output is the generated scenarios. Specifically, the generative AI model uses a text generation engine to create multiple scenarios showing possible future outcomes.

[0860] Step 6:

[0861] The server evaluates the risk and probability of success for each generated scenario. Statistical models and probabilistic methods are used for risk evaluation. The input is the generated scenario, and the output is the evaluation results of the risk and probability of success for each scenario.

[0862] Step 7:

[0863] The server selects the optimal scenario based on the evaluation results. Specifically, it scores each scenario based on multiple evaluation indicators and selects the scenario with the highest score. The input is the evaluation results of risk and probability of success, and the output is the optimal scenario.

[0864] Step 8:

[0865] The server generates detailed information related to the selected scenario (future timeline and analysis results of risks and success probability), compresses it, and sends it back to the terminal. The input is the optimal scenario, and the output is the compressed detailed information.

[0866] Step 9:

[0867] The terminal receives the information sent from the server and checks the validity of the data. If there are no problems with the received data, it decompresses the data and visually displays it in a format that is easy for the user to understand. A front-end library (e.g., React) is used for the display. The input is the compressed detailed information, and the output is a visual display of the decompressed data.

[0868] (Application example 1)

[0869] 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."

[0870] Conventional advertising systems only consider a user's past behavior and interests, and are unable to provide personalized advertisements based on future purchasing scenarios. This makes it difficult to display the most beneficial advertisements to users at the right time. A solution to this problem is needed.

[0871] 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.

[0872] In this invention, the server includes: means for inputting an individual's past information, interests, and goals; means for transmitting the input information to the server; means for using a generative AI model on the server to simulate multiple future scenarios based on the input information and evaluate their risks and likelihood of success; means for selecting an optimal scenario based on the evaluation results and transmitting related information again to the terminal; means for visually displaying the received evaluation results and the optimal scenario on the terminal; and means for displaying appropriate advertisements to the user based on the generated scenarios. This makes it possible to provide personalized advertisements based on the future purchasing scenarios that are most suitable for the user.

[0873] "Personal past information" refers to data such as the user's work history, educational background, qualifications, and purchasing history.

[0874] "Interests" refers to areas or matters in which a user is interested, specifically, hobbies, purchasing preferences, etc.

[0875] "Goals" are specific goals or expected results that a user wants to achieve in the future.

[0876] A "generative AI model" is an artificial intelligence model used to simulate future scenarios based on input data and assess their risks and likelihood of success.

[0877] A "server" is a computer system that receives data submitted by users and analyzes and evaluates the data using a generative AI model.

[0878] A "terminal" is a device that allows a user to input data and visually display evaluation results and scenarios sent from a server.

[0879] A "future scenario" is a future progression pattern that is simulated based on the user's current information and goals.

[0880] "Risks and likelihood of success" is an assessment of the obstacles and feasibility of each scenario coming to fruition.

[0881] "Visually displaying" means presenting information graphically in a format that is easy for a user to understand.

[0882] "Advertising" is a means of communication for providing users with information about products and services.

[0883] "Personalized advertising" refers to advertising that is optimized based on a user's past information and future purchasing scenarios.

[0884] The system for implementing this invention inputs an individual's past information, interests, and goals, and then uses a generative AI model to simulate future purchasing scenarios based on that information, providing optimal personalized advertising to the user. Specific embodiments are described below.

[0885] The system consists of the following main components:

[0886] 1. User device: Includes a means for inputting personal information (such as work history, educational background, qualifications, and purchasing history), interests (hobbies and purchasing preferences), and goals (specific goals and desired outcomes for the future). The device can be a smartphone, smart glasses, or a head-mounted display.

[0887] 2. Server: Receives information input from the user device and analyzes and evaluates it using a generative AI model. The server uses natural language processing and machine learning algorithms to generate future scenarios and evaluates the risk and likelihood of success for each scenario.

[0888] 3. Generative AI model: An artificial intelligence model that analyzes input data from users and simulates multiple future scenarios.

[0889] 4. Advertisement display function: Includes a means for visually displaying the most suitable advertisement for the user based on the evaluation results and optimal scenario received from the server.

[0890] Users input their purchasing history, interests, and future purchase plans via a smartphone app or smart glasses. This information is sent to a server using a secure communication protocol. The server then analyzes the input data using a generative AI model to generate and evaluate multiple future scenarios. The results of these evaluations are then sent back to the device, and the user receives the information in a visually easy-to-understand format. At the same time, personalized advertisements based on the generated scenarios are displayed, providing the most useful advertisements to the user at the right time.

[0891] For example:

[0892] For example, if a user inputs the goal of "buying a new smartphone," the generative AI model will generate an optimal future scenario based on the user's past purchasing history (e.g., "bought a laptop a year ago" and "bought a tablet six months ago") and interests (e.g., "gadgets"). Based on this scenario, the user will be shown a discount promotional ad for a new smartphone. In this way, the user can receive ads that best suit their purchasing intentions.

[0893] Example prompt sentence:

[0894] "Please describe your past purchase history and future interests. For example, 'Past: Smartphones', 'Interest: Gadgets', 'Goal: Buy a new smartphone'."

[0895] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0896] Step 1: The user inputs their past purchasing history, interests, and future goals into the user device. Specifically, they input information in a form using a smartphone app or smart glasses. This input data includes, for example, "smartphone," "gadget," and "purchase a new smartphone." The input in this step is the user's past information, interests, and goals, and the output is the formatted input data.

[0897] Step 2: The terminal sends the user's input data to the server using a secure communication protocol. This transmission also includes formatting and verifying the correctness of the data. The input of this step is the user's input data, and the output is the data sent to the server.

[0898] Step 3: The server receives the data sent from the device, checks for any invalid or missing data, and begins analysis. This analysis uses natural language processing and machine learning algorithms. The input of this step is the received data, and the output is the analysis result.

[0899] Step 4: The server uses the generative AI model to generate multiple future scenarios based on the analysis results and evaluates the risk and likelihood of success for each scenario. Specifically, it refers to data such as market trends, difficulty of acquiring skills, and related success and failure cases. The input for this step is the analyzed data, and the output is the evaluated scenario.

[0900] Step 5: Based on the evaluation results, the server selects the future scenario that best suits the user and generates detailed information about it. The detailed information includes a future timeline, risk, and success probability analysis results. The input to this step is the evaluated scenario, and the output is the selected optimal scenario and its detailed information.

[0901] Step 6: The server again transmits the selected optimal scenario and related detailed information to the terminal. The input of this step is the detailed information of the selected optimal scenario, and the output is the data transmitted to the terminal.

[0902] Step 7: The terminal receives the information sent from the server and visually displays it in a user-friendly format. This display includes a future timeline, a risk simulation diagram, and a specific action plan. Appropriate advertisements are also displayed at the same time. The input of this step is the scenario information received from the server, and the output is the displayed information and advertisements.

[0903] Step 8: The user checks the information and advertisements displayed on the device and selects the appropriate action. The user makes purchasing decisions and makes purchases based on this information. The input to this step is the information and advertisements displayed on the device, and the output is the user's decision to act.

[0904] 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.

[0905] This invention combines a system in which a user inputs their past information, interests, and goals, and a generative AI model simulates multiple future scenarios based on that information, with an emotion engine that recognizes the user's emotions, enabling more precise scenario generation and selection that takes the user's emotions into account.

[0906] Program processing

[0907] 1. User Input and Emotion Recognition

[0908] Through a dedicated interface, users input their past information (work history, educational background, qualifications, etc.), areas of interest, and future goals. In addition, the emotion engine recognizes the user's emotional state in real time as they input and interact.

[0909] 2. Data Transmission

[0910] The terminal formats the data input by the user and the emotion data detected by the emotion engine into the required format and transmits it to the server using a secure communication protocol.

[0911] 3. Data Reception and Analysis

[0912] The server receives the transmitted data, verifies its contents, and begins data analysis after checking for any invalid or missing data.

[0913] 4. Scenario generation and emotional reflection

[0914] The server uses natural language processing and machine learning algorithms to analyze user and emotional information, for example, to identify a user's skill set based on their work history and educational background, and to assess its suitability for their goals and emotional state.

[0915] The server uses a generative AI model to generate multiple future scenarios that take into account the user's goals and emotions. Emotional data is used to modify the scenarios to reflect the user's stress level and excitement state.

[0916] 5. Evaluating the scenario

[0917] The server evaluates the risk of each scenario (e.g., difficulty of acquiring the required skills, probability of failure) and the likelihood of success (e.g., market demand, success stories). The evaluation takes into account not only past data and current market trends, but also the user's emotional state.

[0918] 6. Selecting the optimal scenario and returning the data

[0919] The server selects the most suitable scenario for the user based on the evaluation results, with the selection criteria including the user's goals, past experiences, market demands, and the user's emotional state.

[0920] The server generates a report containing detailed information about the selected scenario (future timeline, analysis results of risks and probability of success, and emotional reflection results) and sends it to the terminal.

[0921] 7. Data reception and display

[0922] The device receives the information sent from the server and displays it in a user-friendly format, including a future timeline, risk simulation diagrams, specific action plans, and advice based on emotional state.

[0923] 8. User Feedback

[0924] The user can refer to the displayed information and, if they have further questions or need additional information, they can enter the initial information again. The new input and emotion data will be used for further analysis.

[0925] Specific examples

[0926] Example 1: A user considering changing jobs to a different industry

[0927] Users enter their own information (e.g., "Current occupation: engineer," "Area of ​​interest: data science," "Goal: change careers to become a data scientist"), and the emotion engine also recognizes their emotions (e.g., excitement, anticipation).

[0928] The terminal transmits this information and emotion data to the server.

[0929] The server analyzes the data and generates scenarios, including "Career Change to Data Science Industry." It evaluates modified scenarios taking into account risks (e.g., skill gaps), likelihood of success (e.g., industry demand), and sentiment.

[0930] The server selects the optimal scenario, which is to "take an online course to acquire skills, then start job hunting," and sends the relevant data to the terminal.

[0931] The device displays this scenario as a future timeline and risk simulation diagram, providing the user with an optimal action plan.

[0932] Example 2: A user considering starting a business

[0933] Users enter their own information (e.g., "Current occupation: Marketing manager," "Area of ​​interest: E-commerce business," "Goal: Launching their own e-commerce site"), and the emotion engine also recognizes their emotions at the time (e.g., anxiety, expectation).

[0934] The terminal transmits this information and emotion data to the server.

[0935] The server analyzes the input data and sentiment data to generate scenarios including "launching an e-commerce business." It evaluates revised scenarios taking into account risks (e.g., market competition, fundraising), success probability (e.g., market demand, competitive analysis), and sentiment.

[0936] The server selects the scenario of "fundraising plan, marketing strategy development, and initial investment amount setting" as the optimal plan and transmits the related information to the terminal.

[0937] The device displays future timelines, risk simulation diagrams, and specific action plans to support users' decisions and provide additional emotional advice.

[0938] This system enables more precise scenario generation and optimization that takes into account the user's emotions, providing more reliable future planning.

[0939] The processing flow will be explained below.

[0940] Step 1:

[0941] Through a dedicated interface, users input their past information (such as work history, educational background, and qualifications), areas of interest, and future goals. At the same time, the emotion engine recognizes the user's emotional state (e.g., excitement, anticipation, anxiety) in real time from their facial expressions and input patterns as they input and interact.

[0942] Step 2:

[0943] The terminal formats the data input by the user and the emotion data analyzed by the emotion engine into the required format and transmits it to the server using a secure communication protocol (e.g., HTTPS).

[0944] Step 3:

[0945] The server receives the transmitted data, verifies its contents, and begins data analysis after checking for any invalid or missing data.

[0946] Step 4:

[0947] The server uses natural language processing and machine learning algorithms to analyze user and emotional information, for example, to identify a user's skill set based on their work history and educational background, and to assess its suitability for their goals and emotional state.

[0948] Step 5:

[0949] The server uses a generative AI model to generate multiple future scenarios that take into account the user's goals and emotions. Specifically, it generates scenarios such as "changing jobs to a different industry," "starting a business," and "career advancement," and then modifies and customizes the scenarios based on emotional data.

[0950] Step 6:

[0951] The server evaluates the risk of each scenario (e.g., difficulty of acquiring the required skills, probability of failure) and the likelihood of success (e.g., market demand, success stories). The evaluation takes into account not only past data and current market trends, but also the user's emotional state.

[0952] Step 7:

[0953] The server selects the most suitable scenario for the user based on the evaluation results, with the selection criteria including the user's goals, past experiences, market demands, and the user's emotional state.

[0954] Step 8:

[0955] The server generates a report containing detailed information about the selected scenario (future timeline, analysis results of risks and probability of success, and emotional reflection results) and sends it to the terminal.

[0956] Step 9:

[0957] The device receives the information sent from the server and displays it in a user-friendly format, including future timelines, risk simulation diagrams, specific action plans, and advice based on the user's emotional state.

[0958] Step 10:

[0959] The user can refer to the displayed information and, if they have further questions or need additional information, they can enter the initial information again. The new input and emotion data will be used for further analysis.

[0960] Example 2

[0961] 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."

[0962] Conventional systems were able to simulate future scenarios based on an individual's past information and goals, but they were unable to generate or evaluate scenarios that took the user's emotional state into account. This meant that they were unable to provide accurate scenarios that reflected the user's real-time emotional state. Furthermore, scenarios that did not take emotional state into account often presented problems in that they were not realistic or effective for the user.

[0963] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting an individual's past information, interests, and goals; means for transmitting the input information and the user's emotional data to the server; means for using a generative AI model on the server to simulate multiple future scenarios based on the input information and emotional data and evaluate their risks and likelihood of success; means for selecting an optimal scenario based on the evaluation and transmitting related information again to the terminal; and means for visually displaying the received evaluation results and the optimal scenario on the terminal. This enables precise scenario generation and evaluation that takes the user's emotional state into consideration, making it possible to provide more realistic and effective future scenarios.

[0964] "Personal past information" is data on the user's past experiences, such as work history, educational history, and qualifications.

[0965] "Interests" are areas or activities that interest a user.

[0966] A "goal" is a specific objective or result that a user wants to achieve in the future.

[0967] "Input means" refers to the interface or device through which a user provides information to the system.

[0968] "Emotion data" is data that detects the user's emotional state in real time and expresses it as numerical values ​​or categories.

[0969] "Server" refers to a central processing unit that receives input information and emotion data, processes them, and analyzes them.

[0970] "Multiple future scenarios" are plans showing multiple different future possibilities that are generated based on the user's input information and emotion data.

[0971] A "generative AI model" is a program that uses artificial intelligence and machine learning algorithms to analyze data and generate future scenarios.

[0972] "Evaluation means" refers to methods and tools for analyzing risks and the likelihood of success based on the generated scenarios.

[0973] "Means for selecting the optimal scenario" refers to the method or process for selecting the most suitable scenario for the user based on the evaluation results.

[0974] "Terminal" refers to a device or computer system through which a user can enter information or view results.

[0975] "Visual display means" refers to a graphical interface or display device that displays analysis results and scenarios in an easy-to-understand manner for the user.

[0976] "Natural language processing" is a technology for analyzing and understanding human language.

[0977] "Machine learning algorithms" refer to mathematical methods and programs that automatically learn patterns and knowledge from data.

[0978] "Market trends" are data that show the current market situation and future trends.

[0979] "Skill acquisition difficulty" refers to the time and effort required to master a particular skill.

[0980] "Success stories and failure stories" refer to specific cases of success and failure obtained from past experience and data.

[0981] "JSON" is a lightweight data exchange format for structuring data.

[0982] "HTTPS" is a protocol for securely transmitting data over the Internet.

[0983] This invention is a system that uses a generative AI model to simulate multiple future scenarios based on user input information and emotion data, and evaluates the risk and likelihood of success of each scenario. An embodiment of this system is described in detail below.

[0984] System configuration

[0985] The system consists of the following major hardware and software components:

[0986] 1. User terminal: Provides an interface for users to input information (e.g., PC, smartphone, tablet).

[0987] 2. Emotion engine: Hardware (e.g., camera, microphone) and software (e.g., facial expression recognition program, voice analysis software) for detecting the user's emotional state in real time.

[0988] 3. Server: A central processing unit that receives data, analyzes, generates scenarios, and evaluates them. It is equipped with generative AI models and machine learning algorithms.

[0989] 4. Secure communication protocols: Protocols to ensure the safety of data transmission and reception (e.g. HTTPS).

[0990] Data Entry and Emotion Recognition

[0991] Users input past information (e.g., work history, educational background, qualification status), areas of interest, and future goals through their device. This can be done using a computer or smartphone keyboard or voice input. The emotion engine uses a camera and microphone to detect the user's emotional state in real time while they are typing. For example, when a user types "Current occupation: engineer," "Area of ​​interest: data science," and "Goal: change careers as a data scientist," their emotions (e.g., excitement, anticipation) are simultaneously recognized.

[0992] Data transmission and reception

[0993] The device formats the data and emotion data entered by the user and sends them to the server using a secure communication protocol. Specifically, the data is compiled in JSON format and encrypted using HTTPS before being sent. The server receives the data and verifies that there is no invalid or missing data.

[0994] Scenario generation and evaluation

[0995] The server uses natural language processing (NLP) and machine learning algorithms to analyze the user's input and emotional data. After analysis, a generative AI model is used to generate multiple future scenarios based on the user's goals and current situation. For example, a scenario could be generated in which the user takes an online course to acquire skills, then begins job hunting.

[0996] For each scenario, the server evaluates the risk (e.g., skill gaps, probability of failure) and likelihood of success (e.g., market demand, success stories). This evaluation takes into account market trends and historical data, as well as the user's emotional state. For example, if the user is "excited," challenging scenarios will be prioritized.

[0997] Optimal scenario selection and data return

[0998] Based on the evaluation results, the server selects the most suitable scenario for the user and generates a detailed report, including a future timeline, risk and success probability analysis, and sentiment-based advice. The report is sent to the device in JSON format.

[0999] Visual display and user feedback

[1000] The device receives the report sent from the server and displays the information in a user-friendly format, including a future timeline, risk simulation diagram, specific action plans, and advice based on the user's emotional state. If the user needs additional questions or information, they can re-enter it and send it back to the server.

[1001] Prompt Sentence Examples

[1002] An example prompt might be, "I'm looking to transition my career from engineer to data scientist. What steps should I take?"

[1003] Another prompt reads, "I would like to use my experience as a marketing manager to start an e-commerce business. Please let me know the appropriate plan."

[1004] This system enables more precise scenario generation and evaluation that takes into account the user's emotions, providing more realistic and effective future scenarios.

[1005] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1006] Step 1:

[1007] User Input and Emotion Recognition

[1008] Input: User's past information (work history, education history, qualifications), areas of interest, goals.

[1009] Specific operation: The user enters their past information, interests, and goals through the device interface. For example, they can enter "Current occupation: Engineer," "Area of ​​interest: Data science," and "Goal: Change careers as a data scientist" using keyboard input or voice input.

[1010] Input: User emotion data (real-time).

[1011] Specific operation: The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice to detect their emotional state (e.g., excitement, anticipation).

[1012] Output: Formatted data containing user input and emotion data.

[1013] Step 2:

[1014] Data transmission

[1015] Input: User profile data (history, interests, goals) and sentiment data.

[1016] Specific operation: The device formats the input data in JSON format and sends it to the server using a secure communication protocol (HTTPS). The data is sent in encrypted form.

[1017] Output: The formatted data sent to the server.

[1018] Step 3:

[1019] Data Receipt and Verification

[1020] Input: Submitted data (profile data, emotion data).

[1021] Specific operation: The server receives the data sent from the terminal and verifies whether there is any invalid or missing data, for example, whether all required fields are filled in and whether the data format is correct.

[1022] Output: Validated data.

[1023] Step 4:

[1024] Data analysis

[1025] Input: Verified data (profile data, sentiment data).

[1026] Specific operation: The server uses natural language processing (NLP) and machine learning algorithms to analyze the user's past information and goals. For example, it identifies the user's skill set from their work history and educational background, and matches it to their areas of interest and goals.

[1027] Output: Analysis results (data such as user skill sets, goals, and interests).

[1028] Step 5:

[1029] Scenario Generation

[1030] Input: Analysis results (data such as user skill sets, goals, and interests), sentiment data.

[1031] Specific actions: The server uses the generative AI model to generate multiple future scenarios based on the user's goals and current situation. For example, it creates a specific scenario such as "Take an online course to acquire skills, then start job hunting."

[1032] Output: Multiple future scenarios.

[1033] Step 6:

[1034] Emotional data reflection and scenario evaluation

[1035] Input: Future scenario, emotion data.

[1036] Specific operation: The server reflects the emotion data in the analysis results. This evaluates the risk and success probability of each scenario. For example, it takes into account the user's emotions (e.g., anticipation, excitement) and adjusts the risk items and success factors.

[1037] Output: The evaluated scenario.

[1038] Step 7:

[1039] Optimal scenario selection

[1040] Input: The evaluated scenario.

[1041] Specific operation: The server selects the most suitable scenario for the user based on the evaluation results, for example, the scenario with the lowest risk and the highest probability of success.

[1042] Output: Details of the optimal scenario.

[1043] Step 8:

[1044] Data return and report generation

[1045] Input: Best-case scenario details.

[1046] Specific operation: The server generates a report containing detailed information about the selected scenario and sends it to the device. The report includes a future timeline, an analysis of risks and success rates, and sentiment-based advice. The data is sent in JSON format.

[1047] Output: The report data sent.

[1048] Step 9:

[1049] Displaying received data

[1050] Input: Report data from the server.

[1051] Specific operations: The terminal receives the report sent from the server and displays the information in a user-friendly format, including a future timeline, a risk simulation diagram, a specific action plan, and advice based on the user's emotional state.

[1052] Output: Visually displayed information.

[1053] Step 10:

[1054] User Feedback

[1055] Input: Information displayed, ask additional questions or enter new information.

[1056] Specific operation: The user refers to the displayed information and, if they have further questions or need additional information, they enter the initial information again. The new input and emotion data are then used for further analysis.

[1057] Output: New user input data, emotion data.

[1058] (Application example 2)

[1059] 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."

[1060] Conventional systems generate future scenarios based on an individual's past information and goals, but because they fail to consider the user's emotional state, they may present scenarios that are not always optimal for the user. Furthermore, ignoring the user's emotional state can increase stress and lead to disappointing results when making important decisions such as financial planning, changing jobs, or starting a business.

[1061] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1062] In this invention, the server includes: means for inputting an individual's past information, interests, and goals; means for transmitting the input information and emotional data to the server; means for using a generative AI model on the server to simulate multiple future scenarios based on the input information and emotional data and evaluate their risks and likelihood of success; means for selecting an optimal scenario based on the evaluation results and transmitting related information again to the terminal; and means for visually displaying the received evaluation results and optimal scenario on the terminal. This enables the generation and selection of more precise and personalized scenarios that take the user's emotions into consideration, reducing user stress and achieving expected results.

[1063] "Individual past information" refers to data such as the individual's past work history, educational history, and qualifications.

[1064] "Interest" refers to an individual's interest in a field or area of ​​interest.

[1065] A goal is an objective or goal that an individual wants to achieve in the future.

[1066] "Emotional data" refers to data that detects and records an individual's emotional state in real time.

[1067] A "generative AI model" is an artificial intelligence model that uses natural language processing and machine learning to perform data analysis and simulations.

[1068] A "server" is a remote computer system that performs data analysis, evaluation, and scenario generation.

[1069] A "terminal" is a digital device used by an individual to enter and receive information.

[1070] "Risk" refers to factors or difficulties that could hinder success in a future scenario.

[1071] "Success probability" refers to the likelihood or likelihood that a particular scenario will achieve its goals.

[1072] A "scenario" is a hypothetical plan that details a specific situation or plan for the future.

[1073] The system based on this invention inputs an individual's past information and goals, recognizes emotional data, generates multiple future scenarios, and evaluates their risks and likelihood of success. The main components are a terminal where the user inputs data, a server that transmits, receives, analyzes, and evaluates the data, and a scenario generation function that uses a generative AI model.

[1074] System Program

[1075] The system's program uses a generative AI model incorporating an emotion recognition engine to analyze the user's input data and emotional data, and generate an optimal scenario. The main parts of the program are as follows:

[1076] 1. User Input and Emotion Recognition

[1077] The device allows users to input past information (such as work history, educational background, and qualifications), areas of interest, and future goals through a dedicated interface. At the same time, an emotion recognition engine recognizes the user's emotional state in real time. The emotion recognition engine uses common hardware (such as smartphones and tablets) and emotion recognition software (such as Affectiva).

[1078] 2. Data Transmission

[1079] The device formats the input data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS).

[1080] 3. Data Reception and Analysis

[1081] The server receives the data and validates it. After checking for missing or invalid data, it begins analyzing the data. The analysis uses cloud infrastructure (e.g., AWS, Azure) and natural language processing and machine learning algorithms.

[1082] 4. Scenario generation and emotional reflection

[1083] The server uses natural language processing and machine learning algorithms to analyze user information and emotional information. It uses a generative AI model to generate multiple future scenarios that take into account the user's goals and emotions. Emotional data is used to modify the scenarios to reflect the user's stress level and excitement state.

[1084] 5. Evaluating the scenario

[1085] The server assesses the risk and likelihood of success of each scenario, taking into account not only historical data and current market trends, but also the user's emotional state.

[1086] 6. Selecting the optimal scenario and returning the data

[1087] The server selects the most suitable scenario for the user based on the evaluation results. The selection criteria include the user's goals, past experiences, market demand, and the user's emotional state. A report containing detailed information about the selected scenario is generated and sent to the terminal.

[1088] 7. Data reception and display

[1089] The device receives the information sent from the server and displays it in a user-friendly format, including a future timeline, risk simulation diagrams, specific action plans, and advice based on emotional state.

[1090] Specific examples

[1091] For example, a user can input their "past purchase history" and "future goals" via their smartphone, and the emotion recognition engine will monitor their emotional state at the time. The device sends this data to a server, which analyzes it and generates an optimal financial planning scenario, which is then sent back to the smartphone. The user can then confirm, modify, and accept the scenario displayed on their smartphone, resulting in a more precise and personalized financial plan.

[1092] Prompt Sentence Examples

[1093] "Enter your past purchasing history and future goals. And give us the permission we need to recognize your emotions in real time as you type. We'll give you the best financial plan."

[1094] The above is the basic form for carrying out the present invention. This makes it possible to generate and select scenarios that take into account the user's emotions, thereby more effectively supporting the user's decision-making.

[1095] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1096] Step 1:

[1097] User Input and Emotion Recognition

[1098] Using a dedicated interface, users input their past information (such as work history, education, and qualifications), areas of interest, and future goals. An emotion recognition engine also recognizes the user's emotional state (e.g., happiness, stress) in real time as they input. This input data and emotional data form the basis for subsequent processing steps.

[1099] Step 2:

[1100] Data transmission

[1101] The device formats the data entered by the user and the emotion data obtained by the emotion recognition engine into the required format, and sends it to the server using a secure communication protocol (e.g., HTTPS). The input data format can be JSON or XML.

[1102] Step 3:

[1103] Data reception and analysis

[1104] The server receives the data sent from the device. After receiving it, it validates the data and checks for and eliminates missing or invalid data. It then uses natural language processing and machine learning algorithms to analyze this data. Cloud infrastructure (e.g., AWS, Azure) supports this analysis process.

[1105] Step 4:

[1106] Scenario generation and emotional reflection

[1107] The server analyzes the received data using a generative AI model. It generates multiple future scenarios based on the user's past information, emotional data, and goals. The generated scenarios are then modified to reflect the user's emotional state (e.g., stress level, excitement level). This results in the generation of the optimal scenario for the user.

[1108] Step 5:

[1109] Scenario evaluation

[1110] The server evaluates the risk (e.g., probability of going over budget) and likelihood of success (e.g., probability of achieving goals) of each generated scenario, taking into account market trends, skill acquisition difficulty, past successes and failures, and sentiment data.

[1111] Step 6:

[1112] Optimal scenario selection and data return

[1113] The server selects the most suitable scenario for the user based on the evaluation results, generates a report containing detailed information about the selected scenario (e.g., future timeline, analysis of risks and success probability, and emotional reflection results), and sends it to the terminal.

[1114] Step 7:

[1115] Data reception and display

[1116] The terminal receives the information sent from the server and displays it in a user-friendly format. The displayed content includes a future timeline, a risk simulation diagram, a specific action plan, and advice based on the user's emotional state. The user can then make decisions based on this information.

[1117] Specific examples

[1118] For example, a user can input their "past purchase history" and "future goals" via their smartphone, and the emotion recognition engine will monitor their emotional state at the time. The device sends this data to a server, which analyzes it and generates an optimal financial planning scenario, which is then sent back to the smartphone. The user can then confirm, modify, and accept the scenario displayed on their smartphone, resulting in a more precise and personalized financial plan.

[1119] Prompt Sentence Examples

[1120] "Enter your past purchasing history and future goals. And give us the permission we need to recognize your emotions in real time as you type. We'll give you the best financial plan."

[1121] 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.

[1122] 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.

[1123] 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.

[1124] [Fourth embodiment]

[1125] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1126] 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.

[1127] 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).

[1128] 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.

[1129] 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.

[1130] 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).

[1131] 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. 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.

[1132] 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.

[1133] 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.

[1134] 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.

[1135] 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.

[1136] 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.

[1137] 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."

[1138] This invention provides a system in which a user inputs their own past information, interests, and goals, and a generative AI model simulates multiple future scenarios based on that information. Specific embodiments of the system include the following:

[1139] Program processing

[1140] 1. User Input

[1141] Users enter their past information (such as work history, educational history, and qualifications), areas of interest, and future goals through a dedicated interface. This information is provided in a form format.

[1142] 2. Data Transmission

[1143] The terminal formats the data entered by the user and sends it to the server using a secure communication protocol, and also verifies that the data is formatted correctly.

[1144] 3. Data Reception and Analysis

[1145] The server receives the data, validates it, and checks for any incorrect or missing data before beginning its analysis, using natural language processing and machine learning algorithms.

[1146] 4. Scenario generation and evaluation

[1147] The server uses a generative AI model to generate multiple future scenarios based on the analysis results. For each scenario, it evaluates the risk (factors that could hinder success) and probability of success (the probability that the scenario will succeed). Specifically, it takes into account market trends, the difficulty of acquiring skills, and related success and failure cases.

[1148] 5. Selecting the optimal scenario and returning the data

[1149] Based on the evaluation results, the server selects the future scenario that best suits the user and generates detailed information related to it (e.g., a future timeline and an analysis of risks and the likelihood of success), which is then sent back to the terminal.

[1150] 6. Data reception and display

[1151] The device receives the information sent from the server and displays it in a user-friendly format, including future timelines, risk simulation diagrams, and specific action plans. A visually appealing design is used to make the information easy to understand.

[1152] Specific examples

[1153] Example 1: A user considering changing jobs to a different industry

[1154] The user enters their own information (e.g., "Current occupation: Engineer," "Area of ​​interest: Data science," "Goal: Change careers to become a data scientist").

[1155] The terminal sends this information to the server.

[1156] The server analyzes the data and generates scenarios, including "Career Change to Data Science Industry," assessing risks (e.g., skill gaps) and likelihood of success (e.g., industry demand).

[1157] The server selects the optimal scenario, which is to "take an online course to acquire skills, then start job hunting," and sends the relevant data to the terminal.

[1158] The device displays this scenario as a future timeline and risk simulation diagram, providing the user with an optimal action plan.

[1159] Example 2: A user considering starting a business

[1160] The user inputs his / her own information (for example, "current occupation: marketing manager", "area of ​​interest: e-commerce business", "goal: launching one's own e-commerce site").

[1161] The terminal sends this information to the server.

[1162] The server analyzes the input data, generates a scenario including "launching an e-commerce business," and evaluates risks (e.g., market competition, fundraising) and success probability (e.g., market demand, competitive analysis).

[1163] The server selects the scenario of "fundraising plan, marketing strategy development, and initial investment amount setting" as the optimal plan and transmits the related information to the terminal.

[1164] The device displays a future timeline, risk simulation diagrams, and specific action plans to support users in making decisions.

[1165] The system helps users choose the best path to their goals and create specific plans. By visually presenting the risks and success probability of scenarios, users can make decisions with more confidence.

[1166] The processing flow will be explained below.

[1167] Step 1:

[1168] Users enter their past information (work history, educational history, qualifications, etc.), areas of interest, and future goals through a dedicated interface (such as a web form or application form). This information is provided in form format.

[1169] Step 2:

[1170] The terminal formats the data entered by the user into the required format and sends it to the server using a secure communication protocol (e.g. HTTPS), and also verifies that the data is formatted correctly.

[1171] Step 3:

[1172] The server receives the data and verifies its contents. After checking for any invalid or missing data, it begins analyzing it.

[1173] Step 4:

[1174] The server analyzes user information using natural language processing and machine learning algorithms, for example, to identify the user's skill set based on their work history and educational background, and evaluates their suitability for the goals.

[1175] Step 5:

[1176] The server uses the generative AI model to generate multiple future scenarios based on the user's goals, such as "changing jobs to a different industry," "starting a business," and "career advancement."

[1177] Step 6:

[1178] The server evaluates the risk of each scenario (e.g., difficulty of acquiring the necessary skills, possibility of failure) and the likelihood of success (e.g., market demand, success stories), taking into account past data and current market trends.

[1179] Step 7:

[1180] Based on the evaluation results, the server selects the most suitable scenario for the user, based on criteria such as the user's goals, past experience, and market demand.

[1181] Step 8:

[1182] The server generates a report containing detailed information about the selected scenario (such as a future timeline and analysis of risks and likelihood of success) and sends it to the terminal.

[1183] Step 9:

[1184] The terminal receives the information sent from the server and displays it in a user-friendly format, including a future timeline, risk simulation diagrams, and specific action plans.

[1185] Step 10:

[1186] The user refers to the displayed information, and if they have further questions or need additional information, they return to step 1 and enter additional information.

[1187] Example 1

[1188] 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."

[1189] Conventional systems require a lot of effort to generate and evaluate future scenarios based on an individual's past information and goals. Furthermore, it is difficult to properly evaluate and present the risks and success probability of these scenarios, resulting in a lack of specific and reliable information for users to create optimal action plans. This makes it difficult for users to make optimal choices toward their future goals.

[1190] 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.

[1191] In this invention, the server includes means for verifying received data and analyzing the data using natural language processing and machine learning algorithms, means for generating multiple future scenarios using a generative AI model based on the analysis results and evaluating the risks and success probability of each scenario, and means for selecting the optimal scenario based on the evaluation results and returning data including detailed information to the terminal. This allows the user to obtain a specific and reliable future scenario based on their own past information and goals, and to visually understand the risks and success probability of that scenario.

[1192] A "user" is an individual who utilizes the system to input information about their past, interests, and future goals.

[1193] A "terminal" is a device that formats information entered by a user, transmits it to a server using a secure communications protocol, and receives and visually displays information returned from the server.

[1194] The "server" is a device that verifies the received data, analyzes the data using natural language processing and machine learning algorithms, and generates and evaluates scenarios using a generative AI model based on the results.

[1195] "Input data" is information including past information, interests and future goals entered by a user through a dedicated interface.

[1196] "Natural language processing" is an information processing technology that allows computers to understand, interpret, and manipulate human language.

[1197] A "machine learning algorithm" is a technology that allows computers to automatically acquire knowledge and patterns by analyzing and learning from data.

[1198] A "generative AI model" is an artificial intelligence model that generates future scenarios based on analysis results.

[1199] A "scenario" is a plan showing multiple possible futures created by a generative AI model based on user input data.

[1200] A "risk" is a factor that prevents a scenario from being successful.

[1201] "Probability of success" is the probability or likelihood that a scenario will succeed.

[1202] "Evaluation results" are the results of evaluating the risks and likelihood of success for each scenario generated by the generative AI model.

[1203] "Detailed information" refers to specific data related to the scenario, such as a future timeline and an analysis of risks and likelihood of success.

[1204] This invention provides a system in which a user inputs their past information, interests, and goals, and a generative AI model simulates multiple future scenarios based on that information. Specific embodiments are as follows.

[1205] Users enter their past information (e.g., work history, educational history, qualifications, etc.), areas of interest, and future goals through a dedicated interface. This information is provided in the form of a form. This interface is an application that can be used on a web browser and is developed using HTML and JavaScript.

[1206] The terminal formats the data entered by the user into JSON format and sends it to the server using a secure communication protocol (e.g., HTTPS). At this time, the terminal also has a function to verify the redundancy and inconsistency of the input data. The terminal is an ordinary personal computer or smartphone, and is operated via a web browser.

[1207] The server verifies the received data and performs data analysis using natural language processing (e.g., NLTK or SpaCy) and machine learning algorithms. The server may, for example, be a high-performance cloud server or a server in a data center. After analyzing the data, it generates multiple future scenarios using a generative AI model (e.g., GPT-3) and evaluates the risks (factors that hinder success) and success probability (the probability that the scenario will succeed) for each scenario. Statistical models and probabilistic methods are used for the evaluation.

[1208] The server selects the most suitable future scenario for the user based on the evaluation results. This selection is based on multiple evaluation indicators. Detailed information related to the selected scenario (e.g., a future timeline and analysis results of risks and success probability) is generated and sent back to the terminal. The returned data is compressed using a compression algorithm (e.g., gzip) for efficient transmission.

[1209] The terminal receives the information sent from the server and displays it in a user-friendly format, including future timelines, risk simulation diagrams, and specific action plans, using a visually appealing design and front-end libraries (e.g., React).

[1210] As a concrete example, consider the case of a user considering changing jobs to a different industry. The user enters their own information (e.g., "Current occupation: engineer," "Area of ​​interest: data science," "Goal: change jobs as a data scientist"). The device sends this information to a server, which analyzes the data and generates scenarios that include "changing jobs to the data science industry." It evaluates risks (e.g., skill gaps) and likelihood of success (e.g., industry demand) and selects the optimal scenario: "Take online courses to acquire skills, then begin job hunting." The device then displays these scenarios as a future timeline and risk simulation diagram, providing the user with an optimal action plan.

[1211] The same applies to users considering starting a business. The user enters their personal information (e.g., "Current occupation: marketing manager," "Area of ​​interest: e-commerce business," "Goal: launching their own e-commerce website") and their device sends this information to the server. The server analyzes the input data, generates scenarios including "launching an e-commerce business," and evaluates the risks (e.g., market competition, fundraising) and likelihood of success (e.g., market demand, competitive analysis). It selects the optimal scenario, "planning fundraising, formulating a marketing strategy, and setting an initial investment amount," and sends the relevant information to the device. The device then displays a future timeline, a risk simulation diagram, and a specific action plan to support the user's decision-making. This system makes it easy for users to select the optimal path toward their goals and create a specific plan. By visually presenting the scenario's risks and likelihood of success, users can make decisions with greater confidence.

[1212] Prompt Sentence Examples

[1213] "I'm an engineer with an interest in data science. I'd like to change careers and become a data scientist. How can I achieve this goal?"

[1214] "I'm a marketing manager and I'm interested in the e-commerce business. I'm considering launching my own e-commerce site, but what approach should I take to ensure success?"

[1215] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1216] Step 1:

[1217] Through a dedicated interface, users input their past information (work history, educational background, qualifications, etc.), areas of interest, and future goals. For example, a user might enter information such as "Current occupation: engineer," "Area of ​​interest: data science," and "Goal: change jobs to become a data scientist" into a web form. The input data is entered as text into each field of the form, and users can proceed to the next step by clicking the submit button.

[1218] Step 2:

[1219] The terminal formats the data entered by the user into JSON format and verifies the accuracy and integrity of the data. Specifically, it checks whether the input data is complete and whether all required fields are filled in. It then sends the formatted data to the server using a secure communication protocol (HTTPS). The input is the information entered by the user, and the output is the data converted to JSON format.

[1220] Step 3:

[1221] The server receives the data sent from the terminal. After receiving it, it verifies the validity of the data again and checks for invalid or missing data. If there is a problem at this stage, it generates an error message and returns it to the terminal. The input is JSON-formatted data, and the output is the validated data or an error message.

[1222] Step 4:

[1223] The server analyzes the received data using a natural language processing (NLP) engine or machine learning algorithm. Specifically, it uses an NLP library (e.g., NLTK or SpaCy) to automatically extract the user's interests and goals. The input is the validated data, and the output is the analysis results (e.g., keyword extraction and category classification).

[1224] Step 5:

[1225] The server uses a generative AI model (e.g., GPT-3) to generate multiple future scenarios based on the analysis results. The input is the analyzed data, and the output is the generated scenarios. Specifically, the generative AI model uses a text generation engine to create multiple scenarios showing possible future outcomes.

[1226] Step 6:

[1227] The server evaluates the risk and probability of success for each generated scenario. Statistical models and probabilistic methods are used for risk evaluation. The input is the generated scenario, and the output is the evaluation results of the risk and probability of success for each scenario.

[1228] Step 7:

[1229] The server selects the optimal scenario based on the evaluation results. Specifically, it scores each scenario based on multiple evaluation indicators and selects the scenario with the highest score. The input is the evaluation results of risk and probability of success, and the output is the optimal scenario.

[1230] Step 8:

[1231] The server generates detailed information related to the selected scenario (future timeline and analysis results of risks and success probability), compresses it, and sends it back to the terminal. The input is the optimal scenario, and the output is the compressed detailed information.

[1232] Step 9:

[1233] The terminal receives the information sent from the server and checks the validity of the data. If there are no problems with the received data, it decompresses the data and visually displays it in a format that is easy for the user to understand. A front-end library (e.g., React) is used for the display. The input is the compressed detailed information, and the output is a visual display of the decompressed data.

[1234] (Application example 1)

[1235] 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."

[1236] Conventional advertising systems only consider a user's past behavior and interests, and are unable to provide personalized advertisements based on future purchasing scenarios. This makes it difficult to display the most beneficial advertisements to users at the right time. A solution to this problem is needed.

[1237] 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.

[1238] In this invention, the server includes: means for inputting an individual's past information, interests, and goals; means for transmitting the input information to the server; means for using a generative AI model on the server to simulate multiple future scenarios based on the input information and evaluate their risks and likelihood of success; means for selecting an optimal scenario based on the evaluation results and transmitting related information again to the terminal; means for visually displaying the received evaluation results and the optimal scenario on the terminal; and means for displaying appropriate advertisements to the user based on the generated scenarios. This makes it possible to provide personalized advertisements based on the future purchasing scenarios that are most suitable for the user.

[1239] "Personal past information" refers to data such as the user's work history, educational background, qualifications, and purchasing history.

[1240] "Interests" refers to areas or matters in which a user is interested, specifically, hobbies, purchasing preferences, etc.

[1241] "Goals" are specific goals or expected results that a user wants to achieve in the future.

[1242] A "generative AI model" is an artificial intelligence model used to simulate future scenarios based on input data and assess their risks and likelihood of success.

[1243] A "server" is a computer system that receives data submitted by users and analyzes and evaluates the data using a generative AI model.

[1244] A "terminal" is a device that allows a user to input data and visually display evaluation results and scenarios sent from a server.

[1245] A "future scenario" is a future progression pattern that is simulated based on the user's current information and goals.

[1246] "Risks and likelihood of success" is an assessment of the obstacles and feasibility of each scenario coming to fruition.

[1247] "Visually displaying" means presenting information graphically in a format that is easy for a user to understand.

[1248] "Advertising" is a means of communication for providing users with information about products and services.

[1249] "Personalized advertising" refers to advertising that is optimized based on a user's past information and future purchasing scenarios.

[1250] The system for implementing this invention inputs an individual's past information, interests, and goals, and then uses a generative AI model to simulate future purchasing scenarios based on that information, providing optimal personalized advertising to the user. Specific embodiments are described below.

[1251] The system consists of the following main components:

[1252] 1. User device: Includes a means for inputting personal information (such as work history, educational background, qualifications, and purchasing history), interests (hobbies and purchasing preferences), and goals (specific goals and desired outcomes for the future). The device can be a smartphone, smart glasses, or a head-mounted display.

[1253] 2. Server: Receives information input from the user device and analyzes and evaluates it using a generative AI model. The server uses natural language processing and machine learning algorithms to generate future scenarios and evaluates the risk and likelihood of success for each scenario.

[1254] 3. Generative AI model: An artificial intelligence model that analyzes input data from users and simulates multiple future scenarios.

[1255] 4. Advertisement display function: Includes a means for visually displaying the most suitable advertisement for the user based on the evaluation results and optimal scenario received from the server.

[1256] Users input their purchasing history, interests, and future purchase plans via a smartphone app or smart glasses. This information is sent to a server using a secure communication protocol. The server then analyzes the input data using a generative AI model to generate and evaluate multiple future scenarios. The results of these evaluations are then sent back to the device, and the user receives the information in a visually easy-to-understand format. At the same time, personalized advertisements based on the generated scenarios are displayed, providing the most useful advertisements to the user at the right time.

[1257] For example:

[1258] For example, if a user inputs the goal of "buying a new smartphone," the generative AI model will generate an optimal future scenario based on the user's past purchasing history (e.g., "bought a laptop a year ago" and "bought a tablet six months ago") and interests (e.g., "gadgets"). Based on this scenario, the user will be shown a discount promotional ad for a new smartphone. In this way, the user can receive ads that best suit their purchasing intentions.

[1259] Example prompt sentence:

[1260] "Please describe your past purchase history and future interests. For example, 'Past: Smartphones', 'Interest: Gadgets', 'Goal: Buy a new smartphone'."

[1261] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1262] Step 1: The user inputs their past purchasing history, interests, and future goals into the user device. Specifically, they input information in a form using a smartphone app or smart glasses. This input data includes, for example, "smartphone," "gadget," and "purchase a new smartphone." The input in this step is the user's past information, interests, and goals, and the output is the formatted input data.

[1263] Step 2: The terminal sends the user's input data to the server using a secure communication protocol. This transmission also includes formatting and verifying the correctness of the data. The input of this step is the user's input data, and the output is the data sent to the server.

[1264] Step 3: The server receives the data sent from the device, checks for any invalid or missing data, and begins analysis. This analysis uses natural language processing and machine learning algorithms. The input of this step is the received data, and the output is the analysis result.

[1265] Step 4: The server uses the generative AI model to generate multiple future scenarios based on the analysis results and evaluates the risk and likelihood of success for each scenario. Specifically, it refers to data such as market trends, difficulty of acquiring skills, and related success and failure cases. The input for this step is the analyzed data, and the output is the evaluated scenario.

[1266] Step 5: Based on the evaluation results, the server selects the future scenario that best suits the user and generates detailed information about it. The detailed information includes a future timeline, risk, and success probability analysis results. The input to this step is the evaluated scenario, and the output is the selected optimal scenario and its detailed information.

[1267] Step 6: The server again transmits the selected optimal scenario and related detailed information to the terminal. The input of this step is the detailed information of the selected optimal scenario, and the output is the data transmitted to the terminal.

[1268] Step 7: The terminal receives the information sent from the server and visually displays it in a user-friendly format. This display includes a future timeline, a risk simulation diagram, and a specific action plan. Appropriate advertisements are also displayed at the same time. The input of this step is the scenario information received from the server, and the output is the displayed information and advertisements.

[1269] Step 8: The user checks the information and advertisements displayed on the device and selects the appropriate action. The user makes purchasing decisions and makes purchases based on this information. The input to this step is the information and advertisements displayed on the device, and the output is the user's decision to act.

[1270] 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.

[1271] This invention combines a system in which a user inputs their past information, interests, and goals, and a generative AI model simulates multiple future scenarios based on that information, with an emotion engine that recognizes the user's emotions, enabling more precise scenario generation and selection that takes the user's emotions into account.

[1272] Program processing

[1273] 1. User Input and Emotion Recognition

[1274] Through a dedicated interface, users input their past information (work history, educational background, qualifications, etc.), areas of interest, and future goals. In addition, the emotion engine recognizes the user's emotional state in real time as they input and interact.

[1275] 2. Data Transmission

[1276] The terminal formats the data input by the user and the emotion data detected by the emotion engine into the required format and transmits it to the server using a secure communication protocol.

[1277] 3. Data Reception and Analysis

[1278] The server receives the transmitted data, verifies its contents, and begins data analysis after checking for any invalid or missing data.

[1279] 4. Scenario generation and emotional reflection

[1280] The server uses natural language processing and machine learning algorithms to analyze user and emotional information, for example, to identify a user's skill set based on their work history and educational background, and to assess its suitability for their goals and emotional state.

[1281] The server uses a generative AI model to generate multiple future scenarios that take into account the user's goals and emotions. Emotional data is used to modify the scenarios to reflect the user's stress level and excitement state.

[1282] 5. Evaluating the scenario

[1283] The server evaluates the risk of each scenario (e.g., difficulty of acquiring the required skills, probability of failure) and the likelihood of success (e.g., market demand, success stories). The evaluation takes into account not only past data and current market trends, but also the user's emotional state.

[1284] 6. Selecting the optimal scenario and returning the data

[1285] The server selects the most suitable scenario for the user based on the evaluation results, with the selection criteria including the user's goals, past experiences, market demands, and the user's emotional state.

[1286] The server generates a report containing detailed information about the selected scenario (future timeline, analysis results of risks and probability of success, and emotional reflection results) and sends it to the terminal.

[1287] 7. Data reception and display

[1288] The device receives the information sent from the server and displays it in a user-friendly format, including a future timeline, risk simulation diagrams, specific action plans, and advice based on emotional state.

[1289] 8. User Feedback

[1290] The user can refer to the displayed information and, if they have further questions or need additional information, they can enter the initial information again. The new input and emotion data will be used for further analysis.

[1291] Specific examples

[1292] Example 1: A user considering changing jobs to a different industry

[1293] Users enter their own information (e.g., "Current occupation: engineer," "Area of ​​interest: data science," "Goal: change careers to become a data scientist"), and the emotion engine also recognizes their emotions (e.g., excitement, anticipation).

[1294] The terminal transmits this information and emotion data to the server.

[1295] The server analyzes the data and generates scenarios, including "Career Change to Data Science Industry." It evaluates modified scenarios taking into account risks (e.g., skill gaps), likelihood of success (e.g., industry demand), and sentiment.

[1296] The server selects the optimal scenario, which is to "take an online course to acquire skills, then start job hunting," and sends the relevant data to the terminal.

[1297] The device displays this scenario as a future timeline and risk simulation diagram, providing the user with an optimal action plan.

[1298] Example 2: A user considering starting a business

[1299] Users enter their own information (e.g., "Current occupation: Marketing manager," "Area of ​​interest: E-commerce business," "Goal: Launching their own e-commerce site"), and the emotion engine also recognizes their emotions at the time (e.g., anxiety, expectation).

[1300] The terminal transmits this information and emotion data to the server.

[1301] The server analyzes the input data and sentiment data to generate scenarios including "launching an e-commerce business." It evaluates revised scenarios taking into account risks (e.g., market competition, fundraising), success probability (e.g., market demand, competitive analysis), and sentiment.

[1302] The server selects the scenario of "fundraising plan, marketing strategy development, and initial investment amount setting" as the optimal plan and transmits the related information to the terminal.

[1303] The device displays future timelines, risk simulation diagrams, and specific action plans to support users' decisions and provide additional emotional advice.

[1304] This system enables more precise scenario generation and optimization that takes into account the user's emotions, providing more reliable future planning.

[1305] The processing flow will be explained below.

[1306] Step 1:

[1307] Through a dedicated interface, users input their past information (such as work history, educational background, and qualifications), areas of interest, and future goals. At the same time, the emotion engine recognizes the user's emotional state (e.g., excitement, anticipation, anxiety) in real time from their facial expressions and input patterns as they input and interact.

[1308] Step 2:

[1309] The terminal formats the data input by the user and the emotion data analyzed by the emotion engine into the required format and transmits it to the server using a secure communication protocol (e.g., HTTPS).

[1310] Step 3:

[1311] The server receives the transmitted data, verifies its contents, and begins data analysis after checking for any invalid or missing data.

[1312] Step 4:

[1313] The server uses natural language processing and machine learning algorithms to analyze user and emotional information, for example, to identify a user's skill set based on their work history and educational background, and to assess its suitability for their goals and emotional state.

[1314] Step 5:

[1315] The server uses a generative AI model to generate multiple future scenarios that take into account the user's goals and emotions. Specifically, it generates scenarios such as "changing jobs to a different industry," "starting a business," and "career advancement," and then modifies and customizes the scenarios based on emotional data.

[1316] Step 6:

[1317] The server evaluates the risk of each scenario (e.g., difficulty of acquiring the required skills, probability of failure) and the likelihood of success (e.g., market demand, success stories). The evaluation takes into account not only past data and current market trends, but also the user's emotional state.

[1318] Step 7:

[1319] The server selects the most suitable scenario for the user based on the evaluation results, with the selection criteria including the user's goals, past experiences, market demands, and the user's emotional state.

[1320] Step 8:

[1321] The server generates a report containing detailed information about the selected scenario (future timeline, analysis results of risks and probability of success, and emotional reflection results) and sends it to the terminal.

[1322] Step 9:

[1323] The device receives the information sent from the server and displays it in a user-friendly format, including future timelines, risk simulation diagrams, specific action plans, and advice based on the user's emotional state.

[1324] Step 10:

[1325] The user can refer to the displayed information and, if they have further questions or need additional information, they can enter the initial information again. The new input and emotion data will be used for further analysis.

[1326] Example 2

[1327] 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."

[1328] Conventional systems were able to simulate future scenarios based on an individual's past information and goals, but they were unable to generate or evaluate scenarios that took the user's emotional state into account. This meant that they were unable to provide accurate scenarios that reflected the user's real-time emotional state. Furthermore, scenarios that did not take emotional state into account often presented problems in that they were not realistic or effective for the user.

[1329] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for inputting an individual's past information, interests, and goals; means for transmitting the input information and the user's emotional data to the server; means for using a generative AI model on the server to simulate multiple future scenarios based on the input information and emotional data and evaluate their risks and likelihood of success; means for selecting an optimal scenario based on the evaluation and transmitting related information again to the terminal; and means for visually displaying the received evaluation results and the optimal scenario on the terminal. This enables precise scenario generation and evaluation that takes the user's emotional state into consideration, making it possible to provide more realistic and effective future scenarios.

[1330] "Personal past information" is data on the user's past experiences, such as work history, educational history, and qualifications.

[1331] "Interests" are areas or activities that interest a user.

[1332] A "goal" is a specific objective or result that a user wants to achieve in the future.

[1333] "Input means" refers to the interface or device through which a user provides information to the system.

[1334] "Emotion data" is data that detects the user's emotional state in real time and expresses it as numerical values ​​or categories.

[1335] "Server" refers to a central processing unit that receives input information and emotion data, processes them, and analyzes them.

[1336] "Multiple future scenarios" are plans showing multiple different future possibilities that are generated based on the user's input information and emotion data.

[1337] A "generative AI model" is a program that uses artificial intelligence and machine learning algorithms to analyze data and generate future scenarios.

[1338] "Evaluation means" refers to methods and tools for analyzing risks and the likelihood of success based on the generated scenarios.

[1339] "Means for selecting the optimal scenario" refers to the method or process for selecting the most suitable scenario for the user based on the evaluation results.

[1340] "Terminal" refers to a device or computer system through which a user can enter information or view results.

[1341] "Visual display means" refers to a graphical interface or display device that displays analysis results and scenarios in an easy-to-understand manner for the user.

[1342] "Natural language processing" is a technology for analyzing and understanding human language.

[1343] "Machine learning algorithms" refer to mathematical methods and programs that automatically learn patterns and knowledge from data.

[1344] "Market trends" are data that show the current market situation and future trends.

[1345] "Skill acquisition difficulty" refers to the time and effort required to master a particular skill.

[1346] "Success stories and failure stories" refer to specific cases of success and failure obtained from past experience and data.

[1347] "JSON" is a lightweight data exchange format for structuring data.

[1348] "HTTPS" is a protocol for securely transmitting data over the Internet.

[1349] This invention is a system that uses a generative AI model to simulate multiple future scenarios based on user input information and emotion data, and evaluates the risk and likelihood of success of each scenario. An embodiment of this system is described in detail below.

[1350] System configuration

[1351] The system consists of the following major hardware and software components:

[1352] 1. User terminal: Provides an interface for users to input information (e.g., PC, smartphone, tablet).

[1353] 2. Emotion engine: Hardware (e.g., camera, microphone) and software (e.g., facial expression recognition program, voice analysis software) for detecting the user's emotional state in real time.

[1354] 3. Server: A central processing unit that receives data, analyzes, generates scenarios, and evaluates them. It is equipped with generative AI models and machine learning algorithms.

[1355] 4. Secure communication protocols: Protocols to ensure the safety of data transmission and reception (e.g. HTTPS).

[1356] Data Entry and Emotion Recognition

[1357] Users input past information (e.g., work history, educational background, qualification status), areas of interest, and future goals through their device. This can be done using a computer or smartphone keyboard or voice input. The emotion engine uses a camera and microphone to detect the user's emotional state in real time while they are typing. For example, when a user types "Current occupation: engineer," "Area of ​​interest: data science," and "Goal: change careers as a data scientist," their emotions (e.g., excitement, anticipation) are simultaneously recognized.

[1358] Data transmission and reception

[1359] The device formats the data and emotion data entered by the user and sends them to the server using a secure communication protocol. Specifically, the data is compiled in JSON format and encrypted using HTTPS before being sent. The server receives the data and verifies that there is no invalid or missing data.

[1360] Scenario generation and evaluation

[1361] The server uses natural language processing (NLP) and machine learning algorithms to analyze the user's input and emotional data. After analysis, a generative AI model is used to generate multiple future scenarios based on the user's goals and current situation. For example, a scenario could be generated in which the user takes an online course to acquire skills, then begins job hunting.

[1362] For each scenario, the server evaluates the risk (e.g., skill gaps, probability of failure) and likelihood of success (e.g., market demand, success stories). This evaluation takes into account market trends and historical data, as well as the user's emotional state. For example, if the user is "excited," challenging scenarios will be prioritized.

[1363] Optimal scenario selection and data return

[1364] Based on the evaluation results, the server selects the most suitable scenario for the user and generates a detailed report, including a future timeline, risk and success probability analysis, and sentiment-based advice. The report is sent to the device in JSON format.

[1365] Visual display and user feedback

[1366] The device receives the report sent from the server and displays the information in a user-friendly format, including a future timeline, risk simulation diagram, specific action plans, and advice based on the user's emotional state. If the user needs additional questions or information, they can re-enter it and send it back to the server.

[1367] Prompt Sentence Examples

[1368] An example prompt might be, "I'm looking to transition my career from engineer to data scientist. What steps should I take?"

[1369] Another prompt reads, "I would like to use my experience as a marketing manager to start an e-commerce business. Please let me know the appropriate plan."

[1370] This system enables more precise scenario generation and evaluation that takes into account the user's emotions, providing more realistic and effective future scenarios.

[1371] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1372] Step 1:

[1373] User Input and Emotion Recognition

[1374] Input: User's past information (work history, education history, qualifications), areas of interest, goals.

[1375] Specific operation: The user enters their past information, interests, and goals through the device interface. For example, they can enter "Current occupation: Engineer," "Area of ​​interest: Data science," and "Goal: Change careers as a data scientist" using keyboard input or voice input.

[1376] Input: User emotion data (real-time).

[1377] Specific operation: The emotion engine uses a camera and microphone to analyze the user's facial expressions and voice to detect their emotional state (e.g., excitement, anticipation).

[1378] Output: Formatted data containing user input and emotion data.

[1379] Step 2:

[1380] Data transmission

[1381] Input: User profile data (history, interests, goals) and sentiment data.

[1382] Specific operation: The device formats the input data in JSON format and sends it to the server using a secure communication protocol (HTTPS). The data is sent in encrypted form.

[1383] Output: The formatted data sent to the server.

[1384] Step 3:

[1385] Data Receipt and Verification

[1386] Input: Submitted data (profile data, emotion data).

[1387] Specific operation: The server receives the data sent from the terminal and verifies whether there is any invalid or missing data, for example, whether all required fields are filled in and whether the data format is correct.

[1388] Output: Validated data.

[1389] Step 4:

[1390] Data analysis

[1391] Input: Verified data (profile data, sentiment data).

[1392] Specific operation: The server uses natural language processing (NLP) and machine learning algorithms to analyze the user's past information and goals. For example, it identifies the user's skill set from their work history and educational background, and matches it to their areas of interest and goals.

[1393] Output: Analysis results (data such as user skill sets, goals, and interests).

[1394] Step 5:

[1395] Scenario Generation

[1396] Input: Analysis results (data such as user skill sets, goals, and interests), sentiment data.

[1397] Specific actions: The server uses the generative AI model to generate multiple future scenarios based on the user's goals and current situation. For example, it creates a specific scenario such as "Take an online course to acquire skills, then start job hunting."

[1398] Output: Multiple future scenarios.

[1399] Step 6:

[1400] Emotional data reflection and scenario evaluation

[1401] Input: Future scenario, emotion data.

[1402] Specific operation: The server reflects the emotion data in the analysis results. This evaluates the risk and success probability of each scenario. For example, it takes into account the user's emotions (e.g., anticipation, excitement) and adjusts the risk items and success factors.

[1403] Output: The evaluated scenario.

[1404] Step 7:

[1405] Optimal scenario selection

[1406] Input: The evaluated scenario.

[1407] Specific operation: The server selects the most suitable scenario for the user based on the evaluation results, for example, the scenario with the lowest risk and the highest probability of success.

[1408] Output: Details of the optimal scenario.

[1409] Step 8:

[1410] Data return and report generation

[1411] Input: Best-case scenario details.

[1412] Specific operation: The server generates a report containing detailed information about the selected scenario and sends it to the device. The report includes a future timeline, an analysis of risks and success rates, and sentiment-based advice. The data is sent in JSON format.

[1413] Output: The report data sent.

[1414] Step 9:

[1415] Displaying received data

[1416] Input: Report data from the server.

[1417] Specific operations: The terminal receives the report sent from the server and displays the information in a user-friendly format, including a future timeline, a risk simulation diagram, a specific action plan, and advice based on the user's emotional state.

[1418] Output: Visually displayed information.

[1419] Step 10:

[1420] User Feedback

[1421] Input: Information displayed, ask additional questions or enter new information.

[1422] Specific operation: The user refers to the displayed information and, if they have further questions or need additional information, they enter the initial information again. The new input and emotion data are then used for further analysis.

[1423] Output: New user input data, emotion data.

[1424] (Application example 2)

[1425] 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."

[1426] Conventional systems generate future scenarios based on an individual's past information and goals, but because they fail to consider the user's emotional state, they may present scenarios that are not always optimal for the user. Furthermore, ignoring the user's emotional state can increase stress and lead to disappointing results when making important decisions such as financial planning, changing jobs, or starting a business.

[1427] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1428] In this invention, the server includes: means for inputting an individual's past information, interests, and goals; means for transmitting the input information and emotional data to the server; means for using a generative AI model on the server to simulate multiple future scenarios based on the input information and emotional data and evaluate their risks and likelihood of success; means for selecting an optimal scenario based on the evaluation results and transmitting related information again to the terminal; and means for visually displaying the received evaluation results and optimal scenario on the terminal. This enables the generation and selection of more precise and personalized scenarios that take the user's emotions into consideration, reducing user stress and achieving expected results.

[1429] "Individual past information" refers to data such as the individual's past work history, educational history, and qualifications.

[1430] "Interest" refers to an individual's interest in a field or area of ​​interest.

[1431] A goal is an objective or goal that an individual wants to achieve in the future.

[1432] "Emotional data" refers to data that detects and records an individual's emotional state in real time.

[1433] A "generative AI model" is an artificial intelligence model that uses natural language processing and machine learning to perform data analysis and simulations.

[1434] A "server" is a remote computer system that performs data analysis, evaluation, and scenario generation.

[1435] A "terminal" is a digital device used by an individual to enter and receive information.

[1436] "Risk" refers to factors or difficulties that could hinder success in a future scenario.

[1437] "Success probability" refers to the likelihood or likelihood that a particular scenario will achieve its goals.

[1438] A "scenario" is a hypothetical plan that details a specific situation or plan for the future.

[1439] The system based on this invention inputs an individual's past information and goals, recognizes emotional data, generates multiple future scenarios, and evaluates their risks and likelihood of success. The main components are a terminal where the user inputs data, a server that transmits, receives, analyzes, and evaluates the data, and a scenario generation function that uses a generative AI model.

[1440] System Program

[1441] The system's program uses a generative AI model incorporating an emotion recognition engine to analyze the user's input data and emotional data, and generate an optimal scenario. The main parts of the program are as follows:

[1442] 1. User Input and Emotion Recognition

[1443] The device allows users to input past information (such as work history, educational background, and qualifications), areas of interest, and future goals through a dedicated interface. At the same time, an emotion recognition engine recognizes the user's emotional state in real time. The emotion recognition engine uses common hardware (such as smartphones and tablets) and emotion recognition software (such as Affectiva).

[1444] 2. Data Transmission

[1445] The device formats the input data and emotion data and transmits it to the server using a secure communication protocol (e.g., HTTPS).

[1446] 3. Data Reception and Analysis

[1447] The server receives the data and validates it. After checking for missing or invalid data, it begins analyzing the data. The analysis uses cloud infrastructure (e.g., AWS, Azure) and natural language processing and machine learning algorithms.

[1448] 4. Scenario generation and emotional reflection

[1449] The server uses natural language processing and machine learning algorithms to analyze user information and emotional information. It uses a generative AI model to generate multiple future scenarios that take into account the user's goals and emotions. Emotional data is used to modify the scenarios to reflect the user's stress level and excitement state.

[1450] 5. Evaluating the scenario

[1451] The server assesses the risk and likelihood of success of each scenario, taking into account not only historical data and current market trends, but also the user's emotional state.

[1452] 6. Selecting the optimal scenario and returning the data

[1453] The server selects the most suitable scenario for the user based on the evaluation results. The selection criteria include the user's goals, past experiences, market demand, and the user's emotional state. A report containing detailed information about the selected scenario is generated and sent to the terminal.

[1454] 7. Data reception and display

[1455] The device receives the information sent from the server and displays it in a user-friendly format, including a future timeline, risk simulation diagrams, specific action plans, and advice based on emotional state.

[1456] Specific examples

[1457] For example, a user can input their "past purchase history" and "future goals" via their smartphone, and the emotion recognition engine will monitor their emotional state at the time. The device sends this data to a server, which analyzes it and generates an optimal financial planning scenario, which is then sent back to the smartphone. The user can then confirm, modify, and accept the scenario displayed on their smartphone, resulting in a more precise and personalized financial plan.

[1458] Prompt Sentence Examples

[1459] "Enter your past purchasing history and future goals. And give us the permission we need to recognize your emotions in real time as you type. We'll give you the best financial plan."

[1460] The above is the basic form for carrying out the present invention. This makes it possible to generate and select scenarios that take into account the user's emotions, thereby more effectively supporting the user's decision-making.

[1461] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1462] Step 1:

[1463] User Input and Emotion Recognition

[1464] Using a dedicated interface, users input their past information (such as work history, education, and qualifications), areas of interest, and future goals. An emotion recognition engine also recognizes the user's emotional state (e.g., happiness, stress) in real time as they input. This input data and emotional data form the basis for subsequent processing steps.

[1465] Step 2:

[1466] Data transmission

[1467] The device formats the data entered by the user and the emotion data obtained by the emotion recognition engine into the required format, and sends it to the server using a secure communication protocol (e.g., HTTPS). The input data format can be JSON or XML.

[1468] Step 3:

[1469] Data reception and analysis

[1470] The server receives the data sent from the device. After receiving it, it validates the data and checks for and eliminates missing or invalid data. It then uses natural language processing and machine learning algorithms to analyze this data. Cloud infrastructure (e.g., AWS, Azure) supports this analysis process.

[1471] Step 4:

[1472] Scenario generation and emotional reflection

[1473] The server analyzes the received data using a generative AI model. It generates multiple future scenarios based on the user's past information, emotional data, and goals. The generated scenarios are then modified to reflect the user's emotional state (e.g., stress level, excitement level). This results in the generation of the optimal scenario for the user.

[1474] Step 5:

[1475] Scenario evaluation

[1476] The server evaluates the risk (e.g., probability of going over budget) and likelihood of success (e.g., probability of achieving goals) of each generated scenario, taking into account market trends, skill acquisition difficulty, past successes and failures, and sentiment data.

[1477] Step 6:

[1478] Optimal scenario selection and data return

[1479] The server selects the most suitable scenario for the user based on the evaluation results, generates a report containing detailed information about the selected scenario (e.g., future timeline, analysis of risks and success probability, and emotional reflection results), and sends it to the terminal.

[1480] Step 7:

[1481] Data reception and display

[1482] The terminal receives the information sent from the server and displays it in a user-friendly format. The displayed content includes a future timeline, a risk simulation diagram, a specific action plan, and advice based on the user's emotional state. The user can then make decisions based on this information.

[1483] Specific examples

[1484] For example, a user can input their "past purchase history" and "future goals" via their smartphone, and the emotion recognition engine will monitor their emotional state at the time. The device sends this data to a server, which analyzes it and generates an optimal financial planning scenario, which is then sent back to the smartphone. The user can then confirm, modify, and accept the scenario displayed on their smartphone, resulting in a more precise and personalized financial plan.

[1485] Prompt Sentence Examples

[1486] "Enter your past purchasing history and future goals. And give us the permission we need to recognize your emotions in real time as you type. We'll give you the best financial plan."

[1487] 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.

[1488] 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.

[1489] 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.

[1490] 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.

[1491] 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.

[1492] 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.

[1493] 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).

[1494] 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.

[1495] 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."

[1496] 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.

[1497] 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).

[1498] 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.

[1499] 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.

[1500] 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.

[1501] 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.

[1502] 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.

[1503] 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.

[1504] 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.

[1505] 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.

[1506] 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.

[1507] 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.

[1508] The following is further disclosed regarding the above embodiment.

[1509] (Claim 1)

[1510] a means for inputting personal history, interests and goals;

[1511] means for transmitting the input information to a server;

[1512] A means for using a generative AI model on a server to simulate multiple future scenarios based on input information and evaluate their risks and likelihood of success;

[1513] A means for selecting an optimal scenario based on the evaluation results and transmitting related information to the terminal again;

[1514] a means for visually displaying the received evaluation results and the optimal scenario on the terminal;

[1515] A system including:

[1516] (Claim 2)

[1517] 2. The system of claim 1, wherein the generative AI model uses natural language processing and machine learning algorithms to analyze an individual's past information and goals and generate future scenarios.

[1518] (Claim 3)

[1519] 2. The system according to claim 1, wherein the server evaluates market trends, difficulty of skill acquisition, and related success stories and failure stories for each scenario, and transmits information including these to the terminal.

[1520] "Example 1"

[1521] (Claim 1)

[1522] a means for users to input their history, interests and goals;

[1523] means for formatting the input information at the terminal and transmitting it to a server using a secure communication protocol;

[1524] means, on the server, for validating the received data and analyzing the data using natural language processing and machine learning algorithms;

[1525] A means for generating multiple future scenarios using a generative AI model based on the analysis results and assessing the risk and likelihood of success for each scenario;

[1526] A means for selecting an optimal scenario based on the evaluation results and returning data including detailed information to the terminal;

[1527] a means for visually displaying the received evaluation results and the optimal scenario at the terminal;

[1528] A system including:

[1529] (Claim 2)

[1530] 2. The system of claim 1, wherein the generative AI model uses natural language processing and machine learning algorithms to analyze an individual's past information and goals and generate future scenarios.

[1531] (Claim 3)

[1532] 2. The system according to claim 1, wherein the server evaluates market trends, difficulty of skill acquisition, and related success stories and failure stories for each scenario, and transmits information including these to the terminal.

[1533] "Application Example 1"

[1534] (Claim 1)

[1535] a means for inputting personal history, interests and goals;

[1536] means for transmitting the input information to a server;

[1537] A means for using a generative AI model on a server to simulate multiple future scenarios based on input information and evaluate their risks and likelihood of success;

[1538] A means for selecting an optimal scenario based on the evaluation results and transmitting related information to the terminal again;

[1539] a means for visually displaying the received evaluation results and the optimal scenario on the terminal;

[1540] a means for displaying an appropriate advertisement to a user based on the generated scenario;

[1541] A system including:

[1542] (Claim 2)

[1543] 2. The system of claim 1, wherein the generative AI model uses natural language processing and machine learning algorithms to analyze an individual's past information and goals and generate future scenarios.

[1544] (Claim 3)

[1545] 2. The system according to claim 1, wherein the server evaluates market trends, acquisition difficulty, and related success stories and failure stories for each scenario, and transmits information including these to the terminal.

[1546] "Example 2: Combining Emotion Engines"

[1547] (Claim 1)

[1548] a means for inputting personal history, interests and goals;

[1549] means for transmitting the input information and user emotion data to a server;

[1550] A means for using a generative AI model on a server to simulate multiple future scenarios based on input information and emotional data and evaluate their risks and likelihood of success;

[1551] A means for selecting an optimal scenario based on the evaluation and transmitting related information to the terminal again;

[1552] a means for visually displaying the received evaluation results and the optimal scenario on the terminal;

[1553] A system including:

[1554] (Claim 2)

[1555] The system of claim 1, wherein the generative AI model uses natural language processing and machine learning algorithms to analyze an individual's past information and goals, as well as emotional data, to generate future scenarios.

[1556] (Claim 3)

[1557] 2. The system according to claim 1, wherein the server evaluates market trends, difficulty of skill acquisition, emotional data, and related success and failure cases for each scenario, and transmits information including these to the terminal.

[1558] "Application example 2 when combining emotion engines"

[1559] (Claim 1)

[1560] a means for inputting personal history, interests and goals;

[1561] means for transmitting the input information and emotion data to a server;

[1562] A means for using a generative AI model on a server to simulate multiple future scenarios based on input information and emotional data and evaluate their risks and likelihood of success;

[1563] A means for selecting an optimal scenario based on the evaluation results and transmitting related information to the terminal again;

[1564] a means for visually displaying the received evaluation results and the optimal scenario on the terminal;

[1565] A system including:

[1566] (Claim 2)

[1567] 2. The system of claim 1, wherein the generative AI model uses natural language processing and machine learning algorithms to analyze an individual's past information, emotional data, and goals to generate future scenarios.

[1568] (Claim 3)

[1569] 2. The system according to claim 1, wherein the server evaluates market trends, difficulty of skill acquisition, and related success and failure cases for each scenario, and transmits information including these and emotional data to the terminal. [Explanation of symbols]

[1570] 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 inputting personal history, interests and goals; means for transmitting the input information to a server; A means for using a generative AI model on a server to simulate multiple future scenarios based on input information and evaluate their risks and likelihood of success; A means for selecting an optimal scenario based on the evaluation results and transmitting related information to the terminal again; a means for visually displaying the received evaluation results and the optimal scenario on the terminal; A system including:

2. 2. The system of claim 1, wherein the generative AI model uses natural language processing and machine learning algorithms to analyze an individual's past information and goals and generate future scenarios.

3. 2. The system according to claim 1, wherein the server evaluates market trends, difficulty of skill acquisition, and related success stories and failure stories for each scenario, and transmits information including these to the terminal.

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