System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting personal information of a user; means for receiving and storing the input personal information; means for generating digital twins based on the received personal information; means for performing behavior simulation based on the generated digital twins to calculate a behavior prediction and a risk level; and means for providing a calculation result to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, the decisions and actions individuals make on a daily basis are becoming increasingly complex, involving numerous options and risks. This makes it difficult to make appropriate decisions, and making the wrong choice, especially in areas such as investment, health management, and accident prevention, can result in significant losses and dangers. It is also difficult to identify potential risks that individuals are unaware of and to determine the best options. To solve these problems, a system is needed that can predict individuals' behavior and provide optimal advice. [Means for solving the problem]

[0005] The present invention relates to a system that uses a user's personal information to generate a digital twin and conducts behavioral simulations using the digital twin. The system includes a means for inputting the user's personal information, a means for receiving and storing the input personal information, a means for generating a digital twin based on the received personal information, a means for running a behavioral simulation based on the generated digital twin and calculating behavioral predictions and risk levels, and a means for providing the calculation results to the user. The system also includes a means for receiving feedback from the user and using the feedback to improve the accuracy of the digital twin, and a means for providing a dedicated personal page for inputting the user's purchase history, behavioral history, and family information. This configuration makes it possible to provide optimal advice and behavioral predictions tailored to each individual user and minimize risk.

[0006] "User" refers to an individual who uses this system and provides personal information.

[0007] "Personal information" refers to information about the user and their family, including name, age, gender, health information, purchase history, behavioral history, etc.

[0008] A "digital twin" is a virtual model created based on a user's personal information and used to simulate the user's behavior.

[0009] "Behavioral simulation" refers to the process of using a digital twin to predict a user's future behavior and make calculations based on various scenarios.

[0010] "Behavioral prediction" refers to predicting a user's future behavioral patterns obtained through behavioral simulation.

[0011] "Risk level" refers to an index that indicates the degree of risk associated with predicted behavior.

[0012] "Advice" refers to recommendations and instructions regarding options provided to users based on the results of behavioral simulations.

[0013] "Feedback" refers to information provided by users regarding actual results of their actions and satisfaction.

[0014] "Accuracy enhancement" refers to using feedback to improve the digital twin's predictive capabilities.

[0015] "My Page" refers to the web-based interface provided to users for entering and managing personal information and other data.

[0016] "Storage" refers to storing the received data in a database in an appropriate format.

[0017] "Receiving" refers to the server receiving data sent from a user or device.

[0018] A "generative AI model" refers to an artificial intelligence model that learns a user's personal information and generates a digital twin. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

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

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

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

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0034] 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.

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

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The present invention relates to a system that creates a digital twin based on personal information provided by a user and provides predictions and advice through behavioral simulations using the digital twin. The specific configuration of the system and program processing are described below.

[0041] System Overview

[0042] The system consists of three elements: a server, a device, and the user. The user enters personal information via the device, and the server receives, stores, and analyzes that information to generate a digital twin. The generated digital twin then performs a behavioral simulation, and the resulting behavioral predictions and advice are provided to the user via the device.

[0043] 1. Server Processing

[0044] Data reception and storage

[0045] The server receives personal information (such as name, age, gender, health information, purchase history, and behavioral history) entered by the user and stores it in a database. This data is used as the basis for generating digital twins and simulating their behavior.

[0046] Creating a digital twin

[0047] The server runs a generative AI model based on the stored personal information to generate a digital twin of the user. The generated digital twin is a virtual model that mimics the user's characteristics and behavioral patterns. This model is used to simulate future behavior.

[0048] Behavioral simulation and prediction

[0049] The server simulates the behavior of the digital twin in response to the user's request, and calculates predicted behavior and risk levels. This simulation utilizes past data and general behavioral patterns to predict multiple options the user may choose.

[0050] Providing advice

[0051] Based on the simulation results, the server generates advice on the optimal options and risk levels for the user and sends this information to the device.

[0052] 2. Terminal Processing

[0053] User Data Input

[0054] The device provides an interface where users can enter information about themselves and their family members, and the data they enter is then sent to a server.

[0055] Sending data

[0056] The device checks the integrity of the personal information entered and then transmits it securely to the server using an encryption protocol to prevent information leaks.

[0057] Displaying Advice

[0058] The device displays the advice and risk level information sent from the server in an easy-to-read format for the user, who can then make decisions about their actions based on this information.

[0059] 3. User Actions

[0060] Entering information

[0061] Users access a dedicated personal page on their device and enter their personal information, past behavioral history, etc. This improves the accuracy of the digital twin and enables more reliable predictions.

[0062] Check the advice and choose your course of action

[0063] Users can review the advice provided via their device and choose the best course of action, taking into account the risk level and recommendations.

[0064] Providing Feedback

[0065] Users provide feedback on the results of their actions and their satisfaction with them, which is sent to a server and used to improve the accuracy of the digital twin's predictive model.

[0066] Specific examples

[0067] For example, if a user is considering investing in stocks:

[0068] 1. The user enters their past investment history and current asset status via the terminal.

[0069] 2. The server receives this information and creates a digital twin.

[0070] 3. The server uses the generated digital twin to simulate the user's investments and calculates the optimal investment options and their risk levels.

[0071] 4. The server provides this information to the user, who then confirms it via their device.

[0072] 5. The user decides on investment actions based on the advice provided and enters the results into the system as feedback.

[0073] In this way, the system according to the present invention assists in predicting user behavior and managing the risks involved, enabling safer and more efficient decision-making.

[0074] The processing flow will be explained below.

[0075] Program processing steps

[0076] 1. Server Processing Steps

[0077] Step 1: Receiving and storing data

[0078] The server receives personal information entered by the user, including name, age, gender, health information, purchase history, and behavioral history.

[0079] The server performs initial processing on the received data and stores it in a database in an appropriate format.

[0080] Step 2: Create a digital twin

[0081] The server runs a generative AI model based on the stored personal information.

[0082] The server stores the generated digital twin profile in a database.

[0083] Step 3: Behavioral simulation and prediction

[0084] The server performs a simulation of a specific behavior by the digital twin based on the user's request.

[0085] The server analyzes the simulation results and calculates behavior predictions and risk levels.

[0086] Step 4: Providing advice

[0087] The server generates optimal options and risk advice based on the analysis results and sends this information to the terminal.

[0088] 2. Terminal Processing Steps

[0089] Step 1: User Data Input

[0090] The device provides users with a dedicated personal page and displays an interface for entering personal information, family composition, purchasing history, behavioral history, etc.

[0091] Step 2: Sending data

[0092] The terminal executes protocol processing to transmit the input data to the server.

[0093] The terminal checks the integrity of the transmitted data and sends it to the server using an encryption protocol.

[0094] Step 3: Receive and view advice

[0095] The device receives advice and risk level information sent from the server.

[0096] The terminal displays the received information in a user-friendly format.

[0097] 3. User Processing Steps

[0098] Step 1: Enter your information

[0099] Users access their dedicated personal page via their terminal and enter personal information, past behavioral history, purchasing history, etc.

[0100] Step 2: Review the advice

[0101] The user checks the advice and risk level displayed on the device and chooses the most appropriate course of action.

[0102] Step 3: Provide feedback

[0103] The user provides feedback about the actual results of the action and the level of satisfaction to the server through the terminal.

[0104] Users contribute to improving the accuracy of the predictive model through feedback.

[0105] Specific examples

[0106] For example, if a user is considering investing in stocks:

[0107] Step 1:

[0108] The user enters past investment history and current asset status into their personal page via a terminal.

[0109] Step 2:

[0110] The terminal transmits this information to the server.

[0111] Step 3:

[0112] The server uses this information to create a digital twin.

[0113] Step 4:

[0114] The server uses the created digital twin to run a stock investment simulation and calculate the optimal investment options and their risk levels.

[0115] Step 5:

[0116] The server sends the calculation results to the terminal.

[0117] Step 6:

[0118] The terminal displays the investment advice and risk level sent from the server.

[0119] Step 7:

[0120] The user makes investment decisions based on the displayed advice.

[0121] Step 8:

[0122] Users provide feedback on their investment results and satisfaction via their terminals.

[0123] Through this series of steps, the system according to the present invention predicts user behavior and supports better decision-making.

[0124] Example 1

[0125] 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."

[0126] Conventional simulation systems have had limitations in accuracy and practicality when it comes to predicting user behavior and managing risks. Issues also exist regarding the handling of personal information and data security. Furthermore, they lack a mechanism for continuously improving the accuracy of the system through feedback, limiting the credibility and effectiveness of the advice users receive. The purpose of this invention is to solve these issues and provide users with more accurate predictions and useful advice.

[0127] 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.

[0128] In this invention, the server includes: means for inputting a user's personal information; means for receiving and storing the input personal information; means for generating a digital twin using a generative AI model based on the received personal information; means for generating prompts for generating the digital twin; means for performing a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and risk level; and means for providing the calculation results to the user. This enables a highly accurate digital twin to be generated based on the user's personal information, enabling reliable behavioral simulation and prediction. Furthermore, the accuracy of the system can be improved by utilizing continuous feedback, enabling more accurate advice to be provided to the user.

[0129] "User personal information" refers to individual data such as name, age, gender, health information, purchase history, and behavioral history that a user provides to the system.

[0130] A "generative AI model" is an artificial intelligence model that generates a digital twin based on a user's personal information.

[0131] A "digital twin" is a virtually generated model that mimics a user's characteristics and behavioral patterns.

[0132] A "prompt" is an instruction provided to a generative AI model to generate a digital twin.

[0133] "Behavioral simulation" is a virtual experiment that uses a generated digital twin to predict future user behavior.

[0134] "Behavioral prediction" is a prediction of a user's future behavior obtained through behavioral simulation.

[0135] The "risk level" is the degree of risk of the actions that the user should take, calculated based on the simulation results.

[0136] "Feedback" is information provided by users about the results of their actual actions and their satisfaction, and is used to improve the accuracy of the system.

[0137] The present invention relates to a system that creates a digital twin based on personal information provided by a user and provides predictions and advice through behavioral simulations using the digital twin. The specific configuration of the system and program processing are described below.

[0138] System Overview

[0139] The system consists of three elements: a server, a device, and the user. The user enters personal information via the device, and the server receives, stores, and analyzes that information to generate a digital twin. The generated digital twin then performs a behavioral simulation, and the resulting behavioral predictions and advice are provided to the user via the device.

[0140] Server Processing

[0141] Data reception and storage

[0142] The server receives personal information entered by the user (such as name, age, gender, health information, purchase history, and behavioral history) and stores it in a database. This data is used as the basis for generating digital twins and simulating their behavior. Suitable databases include MySQL and PostgreSQL.

[0143] Creating a digital twin

[0144] The server runs a generative AI model (e.g., OpenAI's GPT-4) based on the stored personal information to generate a digital twin for the user. Specifically, the generative AI model is run using the prompt, "Generate a digital twin based on the user's name, age, gender, and health information." The generated digital twin is a virtual model that mimics the user's characteristics and behavioral patterns. The generated digital twin is then stored back in the database.

[0145] Behavioral simulation and prediction

[0146] The server simulates the behavior of the digital twin in response to the user's request and calculates predicted behavior and risk levels. This simulation utilizes past data and general behavioral patterns. For example, a simulation can be performed using the prompt, "Predict the investment risk level for the next year using the target user's digital twin."

[0147] Providing advice

[0148] Based on the simulation results, the server generates advice on the user's optimal options and risk level and sends that information to the device, providing specific advice such as "regular exercise and a balanced diet will reduce risk by 30%."

[0149] Terminal handling

[0150] User Data Input

[0151] The device provides an interface where users can enter information about themselves and their family members, and the data they enter is then sent to a server.

[0152] Sending data

[0153] The device checks the integrity of the personal information entered and then securely transmits it to the server using encryption protocols such as SSL / TLS to prevent information leaks.

[0154] Displaying Advice

[0155] The terminal displays the advice and risk level information sent from the server in an easy-to-read format for the user, such as a dashboard displaying information like "Best investment option: Stock A, Estimated risk level: Low."

[0156] User Action

[0157] Entering information

[0158] Users access a dedicated personal page on their device and enter personal information, past activity history, etc. For example, by entering "past exercise habits: 30 minutes of jogging three times a week," the accuracy of the digital twin improves, enabling more reliable predictions.

[0159] Check the advice and choose your course of action

[0160] Users can review the advice provided via their device and choose the most appropriate course of action. They can make decisions based on their risk level and recommendations. For example, they can select "Perform recommended exercise habits" and actually start exercising.

[0161] Providing Feedback

[0162] Users provide feedback on the results of their actual actions and their satisfaction with them, for example, "After exercising for a month, my health condition has improved," and the server receives this feedback and uses it to improve the accuracy of the digital twin's prediction model.

[0163] Specific examples

[0164] For example, if a user is considering investing in stocks:

[0165] 1. The user enters their past investment history and current asset status via the terminal (e.g., "Investment history: past 3 years, current assets: 10 million yen").

[0166] 2. The server receives this information and generates a digital twin (e.g., prompt: "Generate a digital twin based on the user's investment history and current asset status").

[0167] 3. The server uses the generated digital twin to simulate the user's investment and calculates the optimal investment option and its risk level (e.g., prompt: "Predict the target user's investment risk level for the next year using the target user's digital twin").

[0168] 4. The server provides this information to the user, who then confirms it via their device.

[0169] 5. The user decides on investment actions based on the advice provided and enters the results as feedback into the system (e.g., feedback content: "Investment performance: Stock A, Result: Growth rate 10%)."

[0170] In this way, the system according to the present invention assists in predicting user behavior and managing the risks involved, enabling safer and more efficient decision-making.

[0171] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0172] Step 1: Data entry

[0173] explanation:

[0174] Users enter personal information (e.g., name, age, gender, health information, purchase history, behavioral history, etc.) through the device.

[0175] Specific operation: The user fills in the information in the dedicated input form and presses the submit button.

[0176] Input: Personal information such as name, age, gender, and health information

[0177] Output: Well-formatted personal data

[0178] Step 2: Receiving and storing data

[0179] explanation:

[0180] The server receives the personal information sent from the terminal and checks the consistency of the data.

[0181] What happens: The server validates the received data, checking for formatting errors and incompleteness, and then stores it in a database (e.g., MySQL, PostgreSQL).

[0182] Input: Well-formatted personal data

[0183] Output: Personal information stored in a database

[0184] Step 3: Generate a prompt statement

[0185] explanation:

[0186] Based on the stored personal information, the server generates a prompt sentence to be passed to a generative AI model (e.g., OpenAI's GPT-4).

[0187] What it does: The server uses a script to generate a document such as "Create a digital twin based on the user's name, age, gender, and health information."

[0188] Input: Personal information data

[0189] Output: Prompt sentence for the generative AI model

[0190] Step 4: Create a digital twin

[0191] explanation:

[0192] The server inputs the generated prompt sentences into a generative AI model to generate a digital twin.

[0193] How it works: The server inputs a prompt statement to launch a generative AI model (e.g., GPT-4) and generates a digital twin of the user.

[0194] Input: prompt statement

[0195] Output: Data of the generated digital twin

[0196] Step 5: Behavioral simulation

[0197] explanation:

[0198] The server runs a behavioral simulation based on the generated digital twin and calculates behavioral predictions and risk levels.

[0199] Specific operation: Based on the digital twin, the server runs a simulation algorithm and generates predictive data for various scenarios (e.g., health risks, investment risks).

[0200] Input: Digital twin data

[0201] Output: Behavioral simulation results and risk level

[0202] Step 6: Generating and serving advice

[0203] explanation:

[0204] Based on the simulation results, the server generates advice for the user on the optimal behavioral options and their risk levels.

[0205] Specific operation: The server analyzes the simulation data, documents the optimal options and risk levels, and sends them to the terminal.

[0206] Input: Behavioral simulation results and risk level

[0207] Output: Advice document

[0208] Step 7: Viewing Advice

[0209] explanation:

[0210] The terminal displays the advice received from the server in an easy-to-read format for the user.

[0211] What it does: The device uses a UI such as a dashboard to visually display advice and risk levels.

[0212] Input: Advice document

[0213] Output: Advice information displayed on the screen

[0214] Step 8: Choose and take action

[0215] explanation:

[0216] The user reviews the advice provided via the device and selects and executes the most appropriate action.

[0217] Specific Action: The user reviews the advice and takes the suggested action (e.g., manage their health or invest).

[0218] Input: On-screen advice information

[0219] Output: User's choice

[0220] Step 9: Provide feedback

[0221] explanation:

[0222] Users provide feedback on their actual behavior and satisfaction.

[0223] Specific operation: The user enters the results of their actions and their evaluation in a dedicated feedback form and submits it.

[0224] Input: User action results and ratings

[0225] Output: Feedback data

[0226] Step 10: Receive feedback and improve the model

[0227] explanation:

[0228] The server receives the feedback data and uses it to improve the accuracy of the digital twin and refine the model.

[0229] Specific operation: The server analyzes the received feedback data and uses it as training data for the generative AI model.

[0230] Input: Feedback data

[0231] Output: Improved model data

[0232] (Application example 1)

[0233] 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."

[0234] Conventional food delivery systems are unable to provide optimal meal recommendations based on a user's eating habits, health status, or past order history, resulting in issues with user satisfaction and health management. Furthermore, delivery time predictions can be inaccurate, reducing user convenience. The present invention aims to solve these problems and provide users with more personalized meal recommendations and accurate delivery time predictions.

[0235] 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.

[0236] In this invention, the server includes means for inputting personal information of a user, means for receiving and storing the input personal information, means for generating a digital twin based on the received personal information, means for running a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and risk level, means for providing the calculation results to the user, means for inputting data on the user's eating habits and preferences, and means for making meal suggestions and predicting delivery times. This enables personalized meal suggestions and accurate predictions of delivery times based on the user's past eating history and health information.

[0237] "Means for inputting user information" refers to a device or software that provides an interface for users to input information about themselves.

[0238] "Means for receiving and storing information" refers to a device or software that has the function of receiving information entered by a user to a server and storing it in a database.

[0239] "Means for generating a digital twin" refers to algorithms and software for generating a virtual model based on user input.

[0240] "Means for performing behavioral simulation" refers to a device that provides algorithms and software for virtually predicting a user's behavior and choices using the generated digital twin.

[0241] "Means for predicting behavior and calculating risk levels" refers to a device or software that numerically analyzes and calculates a user's future behavior and the associated risks based on the results of a behavioral simulation.

[0242] "Means for providing calculation results" refers to a device or software with an interface for presenting the results of the simulation and risk calculation to a user.

[0243] "Means for inputting data relating to a user's dietary habits and preferences" means a device or software that provides an interface for a user to input information about their dietary habits and preferences.

[0244] "Means for meal suggestions and delivery time prediction" refers to algorithms and software that suggest optimal meal menus and predict accurate delivery times based on user input and digital twin information.

[0245] The present invention relates to a system for providing optimal meal suggestions and delivery time predictions based on a user's eating habits and preferences in a food delivery service. The specific configuration and operation of the system are described below.

[0246] 1. System Configuration

[0247] The system mainly comprises a server, a user terminal, and a generative AI model.

[0248] 1. Server:

[0249] Data Receipt and Storage:

[0250] The server receives personal information (such as name, age, gender, health information, dietary preferences, and past order history) sent from the user's device and stores it in a database. The software used is Python and Flask, and the database is managed by MySQL.

[0251] Digital Twin Generation:

[0252] Based on the stored data, a generative AI model (e.g., GPT-4) is used to generate a digital twin of the user, a virtual model of the user that mimics the user's characteristics and past dietary history.

[0253] Behavioral Simulation and Prediction:

[0254] The resulting digital twin is then used to simulate the user's next likely meal order and optimal delivery time, which then provides meal suggestions and calculates a risk level.

[0255] Advice provided:

[0256] Based on the simulation results, optimal meal suggestions and delivery time predictions are generated and sent to the terminal.

[0257] 2. User Device:

[0258] Information input interface:

[0259] It provides an interface where users can enter their personal information, food preferences, and past ordering history, which is then sent to a server.

[0260] Advice display:

[0261] The server sends meal suggestions and delivery time advice to the user in an easy-to-read format, which the user can use to make ordering decisions.

[0262] 3. Generative AI Model:

[0263] Digital twin generation and behavioral simulation:

[0264] Using OpenAI's GPT-4 model, a digital twin is generated based on the user's input data, and behavioral simulations are performed using that twin.

[0265] 2. Specific Hardware and Software Used

[0266] Server: High-performance server equipment, Python, Flask, MySQL, generative AI models (e.g., GPT-4)

[0267] User device: smartphone or tablet

[0268] Database: MySQL

[0269] Communication protocol: HTTPS

[0270] 3. Program processing explanation

[0271] The server first receives personal information entered by the user and stores it in a MySQL database. It then uses the data to generate a digital twin using a generative AI model (GPT-4) and performs a behavioral simulation. From the simulation results, it calculates optimal meal recommendations and predicted delivery times, and sends them to the user's device. The user's device then displays the received advice and assists the user in their ordering process.

[0272] 4. Specific Examples

[0273] For example, if a user has frequently ordered "salads" or "grilled chicken" in the past, a digital twin will be created based on those preferences and suggest a "salad chicken wrap" as the best menu item for the next order. Additionally, based on past delivery history, the system will predict the quickest delivery time for the user's preferred time slot.

[0274] 5. Examples of prompts

[0275] Here are some example prompts to input to the generative AI model:

[0276] Generate a digital twin for the following user data: name: "Username", age: "Age", gender: "Gender", health_info: "Health Information", dietary_restrictions: "Restricted Foods", meal_history: "Past Meal History"

[0277] Based on this digital twin, predict the next best meal for them.

[0278] As described above, the present invention provides a system that provides personalized meal suggestions and accurate delivery time predictions based on a user's eating habits and preferences.

[0279] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0280] Step 1:

[0281] The server receives the user's personal information (name, age, gender, health information, dietary preferences, past order history, etc.) sent from the device and stores it in a database. Specifically, it uses Flask to receive JSON-formatted data sent from the device and inserts that data into a MySQL database. The user's personal information is input, and the process of saving it to the database is completed as output.

[0282] Step 2:

[0283] The server generates a digital twin using a generative AI model (e.g., GPT-4) based on the stored personal information data. Specifically, a Python script is used to retrieve the necessary data from the database and generate a prompt to input it into the generative AI model. A digital twin is generated based on this prompt, and the generation result is returned to the server in JSON format. The user information in the database is used as input, and the generated digital twin model is obtained as output.

[0284] Step 3:

[0285] The server then uses the generated digital twin to perform a behavioral simulation. Specifically, the digital twin's data is re-input into the generative AI model, which simulates the next meal the user is likely to order and the optimal delivery time. The results are returned in JSON format as the simulation results. The digital twin is the input, and the simulation results are the output.

[0286] Step 4:

[0287] The server then makes meal suggestions and delivery time predictions based on the simulation results and provides them to the user. Specifically, it converts the simulation results into a format that is easy for humans to understand (text or graphics) and sends it back to the terminal. The simulation results are input, and advice information for the user is generated as output.

[0288] Step 5:

[0289] The user uses the device to check the displayed meal suggestions and delivery time estimates and decides on an order. Specifically, the user refers to the advice presented on the smartphone application and selects their next meal. The determined order details are then sent to the server. The input is the meal suggestions and delivery time estimates, and the order details are generated as the output.

[0290] Step 6:

[0291] Users provide feedback on the results of their actual orders and the service to the server via their devices. Specifically, after completing an order, information is entered through a satisfaction survey or feedback form, which is then sent to the server. This feedback is used to improve the accuracy of subsequent behavioral simulations and digital twins. The feedback information is the input, and the output is saved in a database.

[0292] 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.

[0293] The present invention relates to a system that generates a digital twin based on a user's personal information and emotional state, and provides predictions and advice through behavioral simulations using the digital twin. The specific configuration of the system and program processing are described below.

[0294] System Overview

[0295] This system consists of four elements: a server, a device, a user, and an emotion engine. The user provides personal and emotional information via the device, and the server receives, stores, and analyzes that information to generate a digital twin. The generated digital twin then performs a behavioral simulation, and the resulting behavioral predictions and advice are provided to the user via the device.

[0296] 1. Server Processing

[0297] Data reception and storage

[0298] The server receives personal information entered by the user and emotional information provided by the emotion engine, and stores it in a database. This data is used as the basis for generating digital twins and simulating their behavior.

[0299] Creating a digital twin

[0300] The server runs a generative AI model based on the stored personal and emotional information to generate a digital twin of the user. The generated digital twin is a virtual model that mimics the user's characteristics, behavioral patterns, and even emotional state. This model is used to simulate future behavior.

[0301] Behavioral simulation and prediction

[0302] The server simulates the behavior of the digital twin in response to the user's request, calculating predicted behavior and risk levels. This simulation utilizes past data, general behavioral patterns, and even the user's current emotional state to predict multiple options the user may choose.

[0303] Providing advice

[0304] Based on the simulation results, the server generates advice on the optimal options and risk levels for the user and sends this information to the device.

[0305] 2. Terminal Processing

[0306] User Data Input

[0307] The device provides an interface where users can input information about themselves and their families, and also works with an emotion engine to obtain user emotional information.

[0308] Sending data

[0309] The device executes protocol processing to transmit the input personal information and emotional information to the server. An encryption protocol is used for transmission to prevent information leakage.

[0310] Displaying Advice

[0311] The device displays the advice and risk level information sent from the server in an easy-to-read format for the user, who can then make decisions about their actions based on this information.

[0312] 3. User Actions

[0313] Entering information

[0314] Users access a dedicated personal page using their device and enter personal information, past behavior history, etc. Emotional information is also provided using an emotion engine.

[0315] Check the advice and choose your course of action

[0316] Users can review the advice provided via their device and choose the best course of action, taking into account the risk level and recommendations.

[0317] Providing Feedback

[0318] Users provide feedback on the results of their actions and their satisfaction with them, which is sent to a server and used to improve the accuracy of the digital twin's predictive model.

[0319] Specific examples

[0320] For example, if a user uses the system for health management purposes:

[0321] 1. The user uses a device to enter past health data and current lifestyle habits into their personal page. They also use the emotion engine to enter their current emotional state.

[0322] 2. The server receives this information and creates a digital twin.

[0323] 3. The server uses the generated digital twin to simulate the user's health behavior and calculate the optimal health strategy and its risk level.

[0324] 4. The server sends this information to the user's device.

[0325] 5. The device displays the health advice and risk level sent from the server.

[0326] 6. The user decides on health management actions based on the displayed advice and enters the results into the system as feedback.

[0327] Through these steps, the system predicts the user's behavior and takes their emotional state into account to help them make better decisions, allowing them to achieve their goals more safely and efficiently.

[0328] The processing flow will be explained below.

[0329] Program processing steps

[0330] 1. Server Processing

[0331] Step 1: Receiving and storing data

[0332] The server receives personal information entered by the user and emotional information provided by the emotion engine, including name, age, gender, health information, purchase history, and behavioral history.

[0333] The server processes the received data and stores it in a database in an appropriate format, including emotional information.

[0334] Step 2: Create a digital twin

[0335] The server runs a generative AI model based on the stored personal and emotional information, which combines the user's characteristics with real-time emotional data to generate a digital twin.

[0336] The server stores the generated digital twin profile and the results of the initial behavioral simulation based on it in a database.

[0337] Step 3: Behavioral simulation and prediction

[0338] Based on the user's request, the server runs a simulation of a specific behavior by the digital twin, where emotional information is used as part of the simulation to predict behavior based on the user's emotional state.

[0339] The server analyzes the simulation results and calculates behavior predictions and risk levels, and the results are prepared for delivery to the user.

[0340] Step 4: Providing advice

[0341] The server generates analysis results and advice, which includes specific action options and risk levels, and sends the data to the device.

[0342] 2. Terminal Processing

[0343] Step 1: User Data Input

[0344] The device provides an interface where users can input information about themselves and their families, and also works with an emotion engine to obtain user emotional information.

[0345] For example, the emotion engine recognizes the user's facial expressions and tone of voice and analyzes their emotions.

[0346] Step 2: Sending data

[0347] The device sends the entered personal information and acquired emotion information to the server, where the integrity of the data is verified and the data is transmitted securely using an encryption protocol.

[0348] Step 3: Receive and view advice

[0349] The device receives advice and risk level information sent from the server.

[0350] The device displays the received information in an easy-to-read format for the user, who can then make decisions about their actions based on this information.

[0351] 3. User Actions

[0352] Step 1: Enter your information

[0353] Users access their personal page using their device and enter their personal information and past behavioral history. The system also uses an emotion engine to provide information about their current emotions. For example, if a user is feeling stressed, that information is also entered into the system.

[0354] Step 2: Review the advice

[0355] Users can check the advice and risk levels displayed on their device and choose the best course of action. For example, the system may recommend low-risk options based on the user's current emotional state and past data.

[0356] Step 3: Provide feedback

[0357] Users provide feedback on their actual behavior and their satisfaction with it. This feedback is sent to the server via their device and used to improve the accuracy of the digital twin's predictive model. For example, a user can perform a recommended behavior and report the results to the system.

[0358] Specific examples

[0359] For example, if a user uses the system for health management purposes:

[0360] Step 1:

[0361] Users use their devices to input their past health data and current lifestyle habits into their personal page, and also input their current emotional state using the emotion engine.

[0362] Step 2:

[0363] The terminal transmits this information to the server.

[0364] Step 3:

[0365] The server uses this information to create a digital twin.

[0366] Step 4:

[0367] The server uses the created digital twin to simulate health behavior and calculate the optimal health strategy and its risk level.

[0368] Step 5:

[0369] The server sends the calculation results to the terminal.

[0370] Step 6:

[0371] The device displays health advice and risk levels sent from the server.

[0372] Step 7:

[0373] The user decides on health management actions based on the displayed advice and inputs the results into the system as feedback.

[0374] In this way, the system of the present invention predicts the user's behavior and takes their emotional state into account to help them make better decisions, allowing them to achieve their goals more safely and efficiently.

[0375] Example 2

[0376] 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."

[0377] In recent years, there has been an increasing need for systems that provide highly accurate behavioral predictions and advice that take into account an individual's emotional state and behavioral patterns. However, conventional systems lack an established method for integrating personal and emotional information to simulate user behavior. As a result, they have faced issues such as low prediction accuracy and failure to meet user expectations. Furthermore, they lacked a mechanism for improving the model based on feedback, making it difficult to improve user satisfaction.

[0378] 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 personal information and emotional information of a user, means for encrypting and transmitting the personal information and emotional information input by the user, means for storing the received personal information and emotional information in a database, means for generating a digital twin by a generative AI model using the stored personal information and emotional information, means for performing a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and a risk level, and means for providing the user with the behavioral prediction and risk level obtained as a result of the behavioral simulation. This makes it possible to provide highly accurate behavioral predictions and advice that take into account the individuality of the user.

[0379] "User" refers to an individual or organization that uses the system and provides personal and emotional information.

[0380] "Personal information" refers to information that can identify a specific individual, such as a user's name, address, age, and gender.

[0381] "Emotion information" is data that represents the user's current emotional state, and is information obtained using an emotion engine.

[0382] An "emotion engine" is a software or hardware system for analyzing a user's emotional state.

[0383] "Terminal" means a device that a user uses to input, send, or receive information, including a computer, smartphone, tablet, etc.

[0384] A "server" is a computer system that receives, stores, and analyzes data sent by users.

[0385] A "database" is a repository of information installed within a server, and is a system for systematically storing and managing personal and emotional information.

[0386] A "generative AI model" is an artificial intelligence algorithm or program that generates a digital twin based on input data.

[0387] A "digital twin" is a virtual model created based on a user's personal and emotional information, which mimics the user's characteristics and behavioral patterns.

[0388] "Behavioral simulation" is the process of predicting a user's future behavior using a generated digital twin.

[0389] "Behavior prediction" is predictive data about a user's future behavior obtained through behavior simulation.

[0390] The "risk level" is a numerical value or evaluation that indicates the degree of risk associated with a particular behavior, calculated based on behavior prediction.

[0391] "Advice" refers to optimal behavioral options or recommendations provided to users based on the results of behavioral simulations.

[0392] "Feedback" is information provided by users about the results of their actual actions and their satisfaction, and is used to improve the accuracy of the system's model.

[0393] "My Private Page" is a web page or application interface where users can input and manage personal information, emotional information, purchase history, behavioral history, family information, etc.

[0394] The present invention relates to a system that generates a digital twin based on a user's personal information and emotional information, and provides predictions and advice through behavioral simulations using the digital twin. The following describes specific embodiments of the invention.

[0395] System Configuration

[0396] This system consists of four elements: a server, a terminal, a user, and an emotion engine. Each of these elements functions as follows:

[0397] Server Roles

[0398] The server receives, stores, and analyzes the personal information and emotional information sent by the user. First, the data sent from the device is securely transmitted using an encryption protocol (e.g., HTTPS). The server then stores this data in a database.

[0399] The server then runs a generative AI model to generate a digital twin of the user, which analyzes the stored personal and emotional information to create a virtual model that mimics the user's characteristics and behavioral patterns.

[0400] Based on the generated digital twin, the server runs a behavioral simulation that utilizes past data, typical behavioral patterns, and the user's current emotional state to calculate behavioral predictions and risk levels for multiple options the user may choose.

[0401] Finally, the server generates advice based on the simulation results and sends this information to the device. The advice provided to the user includes predicted behavior and risk levels.

[0402] Device Role

[0403] The terminal is a device that allows users to input, send, and receive information. Users access their personal page and enter their personal information, past behavioral history, and emotional state. The terminal works in conjunction with the emotion engine to obtain the user's emotional information.

[0404] The device sends the entered data to a server using an encryption protocol, and the server then sends advice and risk level information that is displayed to the user in an easy-to-read format.

[0405] User Roles

[0406] Users access their personal page and enter their personal information, past behavioral history, and emotional state. This information is often obtained using an emotion engine. The user checks the advice provided by the server and selects the most appropriate action. They also provide feedback on the results of their actual actions and their satisfaction with them. This feedback is sent to the server and used to improve the accuracy of the digital twin's prediction model.

[0407] Specific examples

[0408] For example, the flow when a user uses the system for health management purposes will be explained.

[0409] 1. The user uses a device to enter past health data and current lifestyle habits into their personal page, and also inputs their current emotional state using the emotion engine.

[0410] 2. The server receives this information and creates a digital twin.

[0411] 3. The server uses the generated digital twin to simulate the user's health behavior and calculate the optimal health strategy and its risk level.

[0412] 4. The server sends this information to the user's device.

[0413] 5. The device displays the health advice and risk level sent from the server.

[0414] 6. The user decides on health management actions based on the displayed advice and enters the results into the system as feedback.

[0415] Prompt Sentence Examples

[0416] What specific data items do users enter for health management?

[0417] What predictive models does the server use to simulate behavior?

[0418] What exactly is the process for providing feedback?

[0419] Through these steps, the system predicts the user's behavior and takes their emotional state into account to help them make better decisions, allowing them to achieve their goals more safely and efficiently.

[0420] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0421] Step 1: Entering User Data

[0422] Users access their personal page using their own devices and enter a wide range of data, including personal information, past behavioral history, health data, and emotional state, such as name, age, weight, food records, and exercise volume.

[0423] Input: Personal information, past behavioral history, health data, emotional state

[0424] Output: Encrypted personal and emotional information

[0425] Specific behavior:

[0426] Users can open their personal page using a browser or mobile app.

[0427] Enter the required information using the text boxes and drop-down menus.

[0428] Activate the emotion engine and measure your emotional state.

[0429] Step 2: Sending data

[0430] The device encrypts the input data before sending it to the server. By using an encryption protocol (e.g. HTTPS), information leakage is prevented.

[0431] Input: Personal and emotional information entered by the user

[0432] Output: Data encrypted and sent to the server

[0433] Specific behavior:

[0434] The terminal encrypts the user's input data.

[0435] The encrypted data is sent to the server.

[0436] Step 3: Receiving and storing data

[0437] The server receives the data sent from the device and stores it in a database. It checks the integrity of the received data and stores it appropriately.

[0438] Input: Data sent encrypted from the device

[0439] Output: Personal and emotional information stored in a database

[0440] Specific behavior:

[0441] The server decrypts the received data and checks its integrity.

[0442] Store data appropriately in the database.

[0443] Step 4: Create a digital twin

[0444] The server then uses the stored personal and emotional information to run a generative AI model to create a digital twin, which virtually replicates the user's characteristics and behavioral patterns.

[0445] Input: Personal and emotional information stored in a database

[0446] Output: Generated digital twin

[0447] Specific behavior:

[0448] The server provides the data needed for the generative AI model.

[0449] The generative AI model generates a digital twin and returns the data to the server.

[0450] Step 5: Running a behavioral simulation

[0451] The server uses the created digital twin to simulate behavior under various conditions, taking into account past data, general behavioral patterns, and the user's current emotional state to make predictions.

[0452] Input: Generated digital twin, historical data, general behavioral patterns, current emotional state

[0453] Output: Behavioral prediction and risk level

[0454] Specific behavior:

[0455] The server runs the simulation engine and runs multiple scenarios.

[0456] Prediction data and risk levels are calculated from the results of each scenario.

[0457] Step 6: Generating and serving advice

[0458] The server generates advice for the user based on the results of the behavioral simulation, and the advice is sent to the terminal and provided to the user.

[0459] Input: Simulation results, predicted behavior and risk level

[0460] Output: Advice given to the user

[0461] Specific behavior:

[0462] The server creates customized advice based on the generated prediction data.

[0463] The created advice is sent to the device.

[0464] Step 7: User reviews advice and chooses action

[0465] The user uses the device to review the received advice, which includes action options and their risk levels. The user can then choose the best course of action based on the advice.

[0466] Input: Advice sent by the server

[0467] Output: User's action choice

[0468] Specific behavior:

[0469] The terminal displays the advice from the server.

[0470] Users decide what to do based on their situation and goals.

[0471] Step 8: Provide feedback

[0472] Users provide feedback on the results of their actions and their satisfaction with them, which is sent to the server and used to improve the accuracy of the model.

[0473] Input: User behavior results and satisfaction

[0474] Output: Feedback sent to the server

[0475] Specific behavior:

[0476] The user enters the results of their actual actions into the terminal.

[0477] The terminal transmits the feedback data to the server.

[0478] The server analyzes the received feedback and adjusts the parameters of the model.

[0479] (Application example 2)

[0480] 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."

[0481] While conventional systems were able to predict behavior and provide advice based on a user's personal and emotional information, they were unable to provide useful advice in real time in actual purchasing situations. When users are physically in a store, it is difficult to receive optimal advice or coupon information when selecting products, and there was a need to improve the purchasing experience.

[0482] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting a user's personal information and emotional state, means for receiving and storing the input personal information and emotional information, means for generating a digital twin based on the received personal information and emotional information, means for running a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and risk level, means for providing the calculation results to the user, and means for displaying purchasing advice in real time when the user is selecting a product in a physical store. This allows the user to receive optimal advice and coupon information in real time even in a physical store, improving the purchasing experience.

[0483] A "user" is an individual who uses this system and provides personal and emotional information.

[0484] "Personal information" refers to all data related to a user, such as basic information, purchasing history, behavioral history, and lifestyle habits.

[0485] "Emotional state" is information that indicates the user's current mood and psychological state.

[0486] A "digital twin" is a virtual model that mimics a user's characteristics and behavioral patterns, created based on the user's personal and emotional information.

[0487] "Behavioral simulation" is the process of using a generated digital twin to predict a user's behavioral patterns and calculate their behavioral options and risk levels.

[0488] The "risk level" indicates the degree of risk that a particular behavior may cause, as determined by the behavioral simulation.

[0489] "Means for displaying purchasing advice in real time" refers to a function that instantly displays optimal purchasing advice via a device such as a smartphone when a user is selecting a product in a physical store.

[0490] "Feedback" is information that provides the system with the results of a user's actual actions and their satisfaction.

[0491] Overall system overview

[0492] This invention is a system that generates a digital twin based on a user's personal information and emotional state, and provides predictions and advice through behavioral simulation using the digital twin. The main elements of this system include a server, a terminal, a user, and an emotion engine.

[0493] Server Roles

[0494] The server performs the following process.

[0495] 1. Data Receipt and Storage

[0496] The server receives personal and emotional information from users and stores it in a database, which is used to generate digital twins and simulate their behavior.

[0497] 2. Creating a digital twin

[0498] The server runs a generative AI model based on the stored personal and emotional information to create a digital twin of the user, a virtual model that mimics the user's characteristics, behavioral patterns, and emotional state.

[0499] 3. Behavioral Simulation and Prediction

[0500] The server uses a digital twin to simulate the user's behavior and calculates predicted behavior and risk levels, using historical data, general behavioral patterns, and current emotional state.

[0501] 4. Real-time purchasing advice

[0502] When users are selecting products in physical stores, the system provides optimal purchasing advice in real time, allowing users to make more effective in-store purchases.

[0503] Device Role

[0504] The terminal performs the following processing.

[0505] 1. User data input

[0506] The device provides an interface for users to input their personal and emotional information.

[0507] 2. Data transmission

[0508] The device sends the entered personal and emotional information to a server, using an encryption protocol to prevent information leakage.

[0509] 3. Displaying Advice

[0510] The device displays advice and risk level information sent from the server to the user, allowing the user to choose their course of action based on this information.

[0511] User Roles

[0512] The user performs the following process:

[0513] 1. Enter your information

[0514] Users use their devices to access their dedicated personal page and enter personal and emotional information.

[0515] 2. Check the advice and choose your course of action

[0516] Users can review the advice displayed on their device and choose the best course of action, taking into account risk levels and recommendations.

[0517] 3. Providing Feedback

[0518] After taking an action, users provide feedback about the results and their satisfaction, which is sent to the server and used to improve the accuracy of the digital twin's predictive model.

[0519] Hardware and software used

[0520] The following hardware and software are used to implement this system:

[0521] Server: The backend is built using Python and Flask, and Pandas, scikit-learn, and TensorFlow are used for data analysis.

[0522] Database: Use SQLite or Firebase for data storage.

[0523] Device: Develop a smartphone app that runs on iOS / Android and provide an interface to users.

[0524] Encryption Protocols: We use encryption technologies such as SSL / TLS to ensure data security.

[0525] Specific prompt examples

[0526] For example, the following prompt might be generated for a user looking to purchase a health drink:

[0527] User ID: 12345, Personal information: { Purchase history: ["health drinks", "supplements"]}, Emotion information: { Mood: "stressful"}

[0528] Generated advice: "I recommend a drink that's good for relieving stress. You can use this coupon."

[0529] Based on this prompt, users can select the most suitable product in the store, providing a better shopping experience.

[0530] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0531] Step 1:

[0532] The user uses the device to input personal and emotional information. The input information is retrieved through the device interface. This information includes the user's purchasing history, lifestyle habits, and current mood. The input data is converted into JSON format and sent to the server using an encryption protocol (e.g., SSL / TLS).

[0533] Step 2:

[0534] The server receives the personal information and emotional information sent from the device and stores it in a database. The received data is filtered and parsed to remove outliers and invalid data. The data is then stored in a database (e.g., SQLite or Firebase) and used as the basis for subsequent digital twin generation and simulation processing.

[0535] Step 3:

[0536] The server runs a generative AI model based on the stored personal and emotional information to generate a digital twin of the user. The generative AI model uses a machine learning framework (e.g., TensorFlow) to analyze the input data and create a virtual model that mimics the user's characteristics and behavioral patterns. The generated digital twin is then stored in a database.

[0537] Step 4:

[0538] The server then runs a behavioral simulation using the created digital twin. The behavioral simulation takes into account statistical data, general behavioral patterns, and the user's current emotional state to predict the user's purchasing behavior. The simulation results also calculate the risk level for multiple choices. This data is then saved in JSON format.

[0539] Step 5:

[0540] When a user selects a product in a physical store, the smartphone device uses the camera and GPS to identify the product and acquire its location. The identified product information and store information are immediately sent to the server. The transmitted data includes the product ID and location information.

[0541] Step 6:

[0542] The server generates optimal purchasing advice in real time based on the received product information and location information. Using a generative AI model, it compares past data and simulation results to determine the advice and coupon information that should be provided to the user. The generated advice is then sent to the device.

[0543] Step 7:

[0544] The device instantly displays the advice and coupon information sent from the server to the user on the smartphone's display, allowing the user to choose the best purchasing behavior based on this information.

[0545] Step 8:

[0546] Users provide feedback on the results of their purchases and their satisfaction. Feedback information is entered through the device interface and sent to the server. The submitted feedback is received and stored on the server, and used to improve the accuracy of the digital twin.

[0547] 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.

[0548] 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.

[0549] 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.

[0550] [Second embodiment]

[0551] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0552] 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.

[0553] 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).

[0554] 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.

[0555] 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.

[0556] 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).

[0557] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0558] 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.

[0559] 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.

[0560] 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.

[0561] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0562] 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."

[0563] The present invention relates to a system that creates a digital twin based on personal information provided by a user and provides predictions and advice through behavioral simulations using the digital twin. The specific configuration of the system and program processing are described below.

[0564] System Overview

[0565] The system consists of three elements: a server, a device, and the user. The user enters personal information via the device, and the server receives, stores, and analyzes that information to generate a digital twin. The generated digital twin then performs a behavioral simulation, and the resulting behavioral predictions and advice are provided to the user via the device.

[0566] 1. Server Processing

[0567] Data reception and storage

[0568] The server receives personal information (such as name, age, gender, health information, purchase history, and behavioral history) entered by the user and stores it in a database. This data is used as the basis for generating digital twins and simulating their behavior.

[0569] Creating a digital twin

[0570] The server runs a generative AI model based on the stored personal information to generate a digital twin of the user. The generated digital twin is a virtual model that mimics the user's characteristics and behavioral patterns. This model is used to simulate future behavior.

[0571] Behavioral simulation and prediction

[0572] The server simulates the behavior of the digital twin in response to the user's request, and calculates predicted behavior and risk levels. This simulation utilizes past data and general behavioral patterns to predict multiple options the user may choose.

[0573] Providing advice

[0574] Based on the simulation results, the server generates advice on the optimal options and risk levels for the user and sends this information to the device.

[0575] 2. Terminal Processing

[0576] User Data Input

[0577] The device provides an interface where users can enter information about themselves and their family members, and the data they enter is then sent to a server.

[0578] Sending data

[0579] The device checks the integrity of the personal information entered and then transmits it securely to the server using an encryption protocol to prevent information leaks.

[0580] Displaying Advice

[0581] The device displays the advice and risk level information sent from the server in an easy-to-read format for the user, who can then make decisions about their actions based on this information.

[0582] 3. User Actions

[0583] Entering information

[0584] Users access a dedicated personal page on their device and enter their personal information, past behavioral history, etc. This improves the accuracy of the digital twin and enables more reliable predictions.

[0585] Check the advice and choose your course of action

[0586] Users can review the advice provided via their device and choose the best course of action, taking into account the risk level and recommendations.

[0587] Providing Feedback

[0588] Users provide feedback on the results of their actions and their satisfaction with them, which is sent to a server and used to improve the accuracy of the digital twin's predictive model.

[0589] Specific examples

[0590] For example, if a user is considering investing in stocks:

[0591] 1. The user enters their past investment history and current asset status via the terminal.

[0592] 2. The server receives this information and creates a digital twin.

[0593] 3. The server uses the generated digital twin to simulate the user's investments and calculates the optimal investment options and their risk levels.

[0594] 4. The server provides this information to the user, who then confirms it via their device.

[0595] 5. The user decides on investment actions based on the advice provided and enters the results into the system as feedback.

[0596] In this way, the system according to the present invention assists in predicting user behavior and managing the risks involved, enabling safer and more efficient decision-making.

[0597] The processing flow will be explained below.

[0598] Program processing steps

[0599] 1. Server Processing Steps

[0600] Step 1: Receiving and storing data

[0601] The server receives personal information entered by the user, including name, age, gender, health information, purchase history, and behavioral history.

[0602] The server performs initial processing on the received data and stores it in a database in an appropriate format.

[0603] Step 2: Create a digital twin

[0604] The server runs a generative AI model based on the stored personal information.

[0605] The server stores the generated digital twin profile in a database.

[0606] Step 3: Behavioral simulation and prediction

[0607] The server performs a simulation of a specific behavior by the digital twin based on the user's request.

[0608] The server analyzes the simulation results and calculates behavior predictions and risk levels.

[0609] Step 4: Providing advice

[0610] The server generates optimal options and risk advice based on the analysis results and sends this information to the terminal.

[0611] 2. Terminal Processing Steps

[0612] Step 1: User Data Input

[0613] The device provides users with a dedicated personal page and displays an interface for entering personal information, family composition, purchasing history, behavioral history, etc.

[0614] Step 2: Sending data

[0615] The terminal executes protocol processing to transmit the input data to the server.

[0616] The terminal checks the integrity of the transmitted data and sends it to the server using an encryption protocol.

[0617] Step 3: Receive and view advice

[0618] The device receives advice and risk level information sent from the server.

[0619] The terminal displays the received information in a user-friendly format.

[0620] 3. User Processing Steps

[0621] Step 1: Enter your information

[0622] Users access their dedicated personal page via their terminal and enter personal information, past behavioral history, purchasing history, etc.

[0623] Step 2: Review the advice

[0624] The user checks the advice and risk level displayed on the device and chooses the most appropriate course of action.

[0625] Step 3: Provide feedback

[0626] The user provides feedback about the actual results of the action and the level of satisfaction to the server through the terminal.

[0627] Users contribute to improving the accuracy of the predictive model through feedback.

[0628] Specific examples

[0629] For example, if a user is considering investing in stocks:

[0630] Step 1:

[0631] The user enters past investment history and current asset status into their personal page via a terminal.

[0632] Step 2:

[0633] The terminal transmits this information to the server.

[0634] Step 3:

[0635] The server uses this information to create a digital twin.

[0636] Step 4:

[0637] The server uses the created digital twin to run a stock investment simulation and calculate the optimal investment options and their risk levels.

[0638] Step 5:

[0639] The server sends the calculation results to the terminal.

[0640] Step 6:

[0641] The terminal displays the investment advice and risk level sent from the server.

[0642] Step 7:

[0643] The user makes investment decisions based on the displayed advice.

[0644] Step 8:

[0645] Users provide feedback on their investment results and satisfaction via their terminals.

[0646] Through this series of steps, the system according to the present invention predicts user behavior and supports better decision-making.

[0647] Example 1

[0648] 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."

[0649] Conventional simulation systems have had limitations in accuracy and practicality when it comes to predicting user behavior and managing risks. Issues also exist regarding the handling of personal information and data security. Furthermore, they lack a mechanism for continuously improving the accuracy of the system through feedback, limiting the credibility and effectiveness of the advice users receive. The purpose of this invention is to solve these issues and provide users with more accurate predictions and useful advice.

[0650] 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.

[0651] In this invention, the server includes: means for inputting a user's personal information; means for receiving and storing the input personal information; means for generating a digital twin using a generative AI model based on the received personal information; means for generating prompts for generating the digital twin; means for performing a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and risk level; and means for providing the calculation results to the user. This enables a highly accurate digital twin to be generated based on the user's personal information, enabling reliable behavioral simulation and prediction. Furthermore, the accuracy of the system can be improved by utilizing continuous feedback, enabling more accurate advice to be provided to the user.

[0652] "User personal information" refers to individual data such as name, age, gender, health information, purchase history, and behavioral history that a user provides to the system.

[0653] A "generative AI model" is an artificial intelligence model that generates a digital twin based on a user's personal information.

[0654] A "digital twin" is a virtually generated model that mimics a user's characteristics and behavioral patterns.

[0655] A "prompt" is an instruction provided to a generative AI model to generate a digital twin.

[0656] "Behavioral simulation" is a virtual experiment that uses a generated digital twin to predict future user behavior.

[0657] "Behavioral prediction" is a prediction of a user's future behavior obtained through behavioral simulation.

[0658] The "risk level" is the degree of risk of the actions that the user should take, calculated based on the simulation results.

[0659] "Feedback" is information provided by users about the results of their actual actions and their satisfaction, and is used to improve the accuracy of the system.

[0660] The present invention relates to a system that creates a digital twin based on personal information provided by a user and provides predictions and advice through behavioral simulations using the digital twin. The specific configuration of the system and program processing are described below.

[0661] System Overview

[0662] The system consists of three elements: a server, a device, and the user. The user enters personal information via the device, and the server receives, stores, and analyzes that information to generate a digital twin. The generated digital twin then performs a behavioral simulation, and the resulting behavioral predictions and advice are provided to the user via the device.

[0663] Server Processing

[0664] Data reception and storage

[0665] The server receives personal information entered by the user (such as name, age, gender, health information, purchase history, and behavioral history) and stores it in a database. This data is used as the basis for generating digital twins and simulating their behavior. Suitable databases include MySQL and PostgreSQL.

[0666] Creating a digital twin

[0667] The server runs a generative AI model (e.g., OpenAI's GPT-4) based on the stored personal information to generate a digital twin for the user. Specifically, the generative AI model is run using the prompt, "Generate a digital twin based on the user's name, age, gender, and health information." The generated digital twin is a virtual model that mimics the user's characteristics and behavioral patterns. The generated digital twin is then stored back in the database.

[0668] Behavioral simulation and prediction

[0669] The server simulates the behavior of the digital twin in response to the user's request and calculates predicted behavior and risk levels. This simulation utilizes past data and general behavioral patterns. For example, a simulation can be performed using the prompt, "Predict the investment risk level for the next year using the target user's digital twin."

[0670] Providing advice

[0671] Based on the simulation results, the server generates advice on the user's optimal options and risk level and sends that information to the device, providing specific advice such as "regular exercise and a balanced diet will reduce risk by 30%."

[0672] Terminal handling

[0673] User Data Input

[0674] The device provides an interface where users can enter information about themselves and their family members, and the data they enter is then sent to a server.

[0675] Sending data

[0676] The device checks the integrity of the personal information entered and then securely transmits it to the server using encryption protocols such as SSL / TLS to prevent information leaks.

[0677] Displaying Advice

[0678] The terminal displays the advice and risk level information sent from the server in an easy-to-read format for the user, such as a dashboard displaying information like "Best investment option: Stock A, Estimated risk level: Low."

[0679] User Action

[0680] Entering information

[0681] Users access a dedicated personal page on their device and enter personal information, past activity history, etc. For example, by entering "past exercise habits: 30 minutes of jogging three times a week," the accuracy of the digital twin improves, enabling more reliable predictions.

[0682] Check the advice and choose your course of action

[0683] Users can review the advice provided via their device and choose the most appropriate course of action. They can make decisions based on their risk level and recommendations. For example, they can select "Perform recommended exercise habits" and actually start exercising.

[0684] Providing Feedback

[0685] Users provide feedback on the results of their actual actions and their satisfaction with them, for example, "After exercising for a month, my health condition has improved," and the server receives this feedback and uses it to improve the accuracy of the digital twin's prediction model.

[0686] Specific examples

[0687] For example, if a user is considering investing in stocks:

[0688] 1. The user enters their past investment history and current asset status via the terminal (e.g., "Investment history: past 3 years, current assets: 10 million yen").

[0689] 2. The server receives this information and generates a digital twin (e.g., prompt: "Generate a digital twin based on the user's investment history and current asset status").

[0690] 3. The server uses the generated digital twin to simulate the user's investment and calculates the optimal investment option and its risk level (e.g., prompt: "Predict the target user's investment risk level for the next year using the target user's digital twin").

[0691] 4. The server provides this information to the user, who then confirms it via their device.

[0692] 5. The user decides on investment actions based on the advice provided and enters the results as feedback into the system (e.g., feedback content: "Investment performance: Stock A, Result: Growth rate 10%)."

[0693] In this way, the system according to the present invention assists in predicting user behavior and managing the risks involved, enabling safer and more efficient decision-making.

[0694] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0695] Step 1: Data entry

[0696] explanation:

[0697] Users enter personal information (e.g., name, age, gender, health information, purchase history, behavioral history, etc.) through the device.

[0698] Specific operation: The user fills in the information in the dedicated input form and presses the submit button.

[0699] Input: Personal information such as name, age, gender, and health information

[0700] Output: Well-formatted personal data

[0701] Step 2: Receiving and storing data

[0702] explanation:

[0703] The server receives the personal information sent from the terminal and checks the consistency of the data.

[0704] What happens: The server validates the received data, checking for formatting errors and incompleteness, and then stores it in a database (e.g., MySQL, PostgreSQL).

[0705] Input: Well-formatted personal data

[0706] Output: Personal information stored in a database

[0707] Step 3: Generate a prompt statement

[0708] explanation:

[0709] Based on the stored personal information, the server generates a prompt sentence to be passed to a generative AI model (e.g., OpenAI's GPT-4).

[0710] What it does: The server uses a script to generate a document such as "Create a digital twin based on the user's name, age, gender, and health information."

[0711] Input: Personal information data

[0712] Output: Prompt sentence for the generative AI model

[0713] Step 4: Create a digital twin

[0714] explanation:

[0715] The server inputs the generated prompt sentences into a generative AI model to generate a digital twin.

[0716] How it works: The server inputs a prompt statement to launch a generative AI model (e.g., GPT-4) and generates a digital twin of the user.

[0717] Input: prompt statement

[0718] Output: Data of the generated digital twin

[0719] Step 5: Behavioral simulation

[0720] explanation:

[0721] The server runs a behavioral simulation based on the generated digital twin and calculates behavioral predictions and risk levels.

[0722] Specific operation: Based on the digital twin, the server runs a simulation algorithm and generates predictive data for various scenarios (e.g., health risks, investment risks).

[0723] Input: Digital twin data

[0724] Output: Behavioral simulation results and risk level

[0725] Step 6: Generating and serving advice

[0726] explanation:

[0727] Based on the simulation results, the server generates advice for the user on the optimal behavioral options and their risk levels.

[0728] Specific operation: The server analyzes the simulation data, documents the optimal options and risk levels, and sends them to the terminal.

[0729] Input: Behavioral simulation results and risk level

[0730] Output: Advice document

[0731] Step 7: Viewing Advice

[0732] explanation:

[0733] The terminal displays the advice received from the server in an easy-to-read format for the user.

[0734] What it does: The device uses a UI such as a dashboard to visually display advice and risk levels.

[0735] Input: Advice document

[0736] Output: Advice information displayed on the screen

[0737] Step 8: Choose and take action

[0738] explanation:

[0739] The user reviews the advice provided via the device and selects and executes the most appropriate action.

[0740] Specific Action: The user reviews the advice and takes the suggested action (e.g., manage their health or invest).

[0741] Input: On-screen advice information

[0742] Output: User's choice

[0743] Step 9: Provide feedback

[0744] explanation:

[0745] Users provide feedback on their actual behavior and satisfaction.

[0746] Specific operation: The user enters the results of their actions and their evaluation in a dedicated feedback form and submits it.

[0747] Input: User action results and ratings

[0748] Output: Feedback data

[0749] Step 10: Receive feedback and improve the model

[0750] explanation:

[0751] The server receives the feedback data and uses it to improve the accuracy of the digital twin and refine the model.

[0752] Specific operation: The server analyzes the received feedback data and uses it as training data for the generative AI model.

[0753] Input: Feedback data

[0754] Output: Improved model data

[0755] (Application example 1)

[0756] 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."

[0757] Conventional food delivery systems are unable to provide optimal meal recommendations based on a user's eating habits, health status, or past order history, resulting in issues with user satisfaction and health management. Furthermore, delivery time predictions can be inaccurate, reducing user convenience. The present invention aims to solve these problems and provide users with more personalized meal recommendations and accurate delivery time predictions.

[0758] 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.

[0759] In this invention, the server includes means for inputting personal information of a user, means for receiving and storing the input personal information, means for generating a digital twin based on the received personal information, means for running a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and risk level, means for providing the calculation results to the user, means for inputting data on the user's eating habits and preferences, and means for making meal suggestions and predicting delivery times. This enables personalized meal suggestions and accurate predictions of delivery times based on the user's past eating history and health information.

[0760] "Means for inputting user information" refers to a device or software that provides an interface for users to input information about themselves.

[0761] "Means for receiving and storing information" refers to a device or software that has the function of receiving information entered by a user to a server and storing it in a database.

[0762] "Means for generating a digital twin" refers to algorithms and software for generating a virtual model based on user input.

[0763] "Means for performing behavioral simulation" refers to a device that provides algorithms and software for virtually predicting a user's behavior and choices using the generated digital twin.

[0764] "Means for predicting behavior and calculating risk levels" refers to a device or software that numerically analyzes and calculates a user's future behavior and the associated risks based on the results of a behavioral simulation.

[0765] "Means for providing calculation results" refers to a device or software with an interface for presenting the results of the simulation and risk calculation to a user.

[0766] "Means for inputting data relating to a user's dietary habits and preferences" means a device or software that provides an interface for a user to input information about their dietary habits and preferences.

[0767] "Means for meal suggestions and delivery time prediction" refers to algorithms and software that suggest optimal meal menus and predict accurate delivery times based on user input and digital twin information.

[0768] The present invention relates to a system for providing optimal meal suggestions and delivery time predictions based on a user's eating habits and preferences in a food delivery service. The specific configuration and operation of the system are described below.

[0769] 1. System Configuration

[0770] The system mainly comprises a server, a user terminal, and a generative AI model.

[0771] 1. Server:

[0772] Data Receipt and Storage:

[0773] The server receives personal information (such as name, age, gender, health information, dietary preferences, and past order history) sent from the user's device and stores it in a database. The software used is Python and Flask, and the database is managed by MySQL.

[0774] Digital Twin Generation:

[0775] Based on the stored data, a generative AI model (e.g., GPT-4) is used to generate a digital twin of the user, a virtual model of the user that mimics the user's characteristics and past dietary history.

[0776] Behavioral Simulation and Prediction:

[0777] The resulting digital twin is then used to simulate the user's next likely meal order and optimal delivery time, which then provides meal suggestions and calculates a risk level.

[0778] Advice provided:

[0779] Based on the simulation results, optimal meal suggestions and delivery time predictions are generated and sent to the terminal.

[0780] 2. User Device:

[0781] Information input interface:

[0782] It provides an interface where users can enter their personal information, food preferences, and past ordering history, which is then sent to a server.

[0783] Advice display:

[0784] The server sends meal suggestions and delivery time advice to the user in an easy-to-read format, which the user can use to make ordering decisions.

[0785] 3. Generative AI Model:

[0786] Digital twin generation and behavioral simulation:

[0787] Using OpenAI's GPT-4 model, a digital twin is generated based on the user's input data, and behavioral simulations are performed using that twin.

[0788] 2. Specific Hardware and Software Used

[0789] Server: High-performance server equipment, Python, Flask, MySQL, generative AI models (e.g., GPT-4)

[0790] User device: smartphone or tablet

[0791] Database: MySQL

[0792] Communication protocol: HTTPS

[0793] 3. Program processing explanation

[0794] The server first receives personal information entered by the user and stores it in a MySQL database. It then uses the data to generate a digital twin using a generative AI model (GPT-4) and performs a behavioral simulation. From the simulation results, it calculates optimal meal recommendations and predicted delivery times, and sends them to the user's device. The user's device then displays the received advice and assists the user in their ordering process.

[0795] 4. Specific Examples

[0796] For example, if a user has frequently ordered "salads" or "grilled chicken" in the past, a digital twin will be created based on those preferences and suggest a "salad chicken wrap" as the best menu item for the next order. Additionally, based on past delivery history, the system will predict the quickest delivery time for the user's preferred time slot.

[0797] 5. Examples of prompts

[0798] Here are some example prompts to input to the generative AI model:

[0799] Generate a digital twin for the following user data: name: "Username", age: "Age", gender: "Gender", health_info: "Health Information", dietary_restrictions: "Restricted Foods", meal_history: "Past Meal History"

[0800] Based on this digital twin, predict the next best meal for them.

[0801] As described above, the present invention provides a system that provides personalized meal suggestions and accurate delivery time predictions based on a user's eating habits and preferences.

[0802] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0803] Step 1:

[0804] The server receives the user's personal information (name, age, gender, health information, dietary preferences, past order history, etc.) sent from the device and stores it in a database. Specifically, it uses Flask to receive JSON-formatted data sent from the device and inserts that data into a MySQL database. The user's personal information is input, and the process of saving it to the database is completed as output.

[0805] Step 2:

[0806] The server generates a digital twin using a generative AI model (e.g., GPT-4) based on the stored personal information data. Specifically, a Python script is used to retrieve the necessary data from the database and generate a prompt to input it into the generative AI model. A digital twin is generated based on this prompt, and the generation result is returned to the server in JSON format. The user information in the database is used as input, and the generated digital twin model is obtained as output.

[0807] Step 3:

[0808] The server then uses the generated digital twin to perform a behavioral simulation. Specifically, the digital twin's data is re-input into the generative AI model, which simulates the next meal the user is likely to order and the optimal delivery time. The results are returned in JSON format as the simulation results. The digital twin is the input, and the simulation results are the output.

[0809] Step 4:

[0810] The server then makes meal suggestions and delivery time predictions based on the simulation results and provides them to the user. Specifically, it converts the simulation results into a format that is easy for humans to understand (text or graphics) and sends it back to the terminal. The simulation results are input, and advice information for the user is generated as output.

[0811] Step 5:

[0812] The user uses the device to check the displayed meal suggestions and delivery time estimates and decides on an order. Specifically, the user refers to the advice presented on the smartphone application and selects their next meal. The determined order details are then sent to the server. The input is the meal suggestions and delivery time estimates, and the order details are generated as the output.

[0813] Step 6:

[0814] Users provide feedback on the results of their actual orders and the service to the server via their devices. Specifically, after completing an order, information is entered through a satisfaction survey or feedback form, which is then sent to the server. This feedback is used to improve the accuracy of subsequent behavioral simulations and digital twins. The feedback information is the input, and the output is saved in a database.

[0815] 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.

[0816] The present invention relates to a system that generates a digital twin based on a user's personal information and emotional state, and provides predictions and advice through behavioral simulations using the digital twin. The specific configuration of the system and program processing are described below.

[0817] System Overview

[0818] This system consists of four elements: a server, a device, a user, and an emotion engine. The user provides personal and emotional information via the device, and the server receives, stores, and analyzes that information to generate a digital twin. The generated digital twin then performs a behavioral simulation, and the resulting behavioral predictions and advice are provided to the user via the device.

[0819] 1. Server Processing

[0820] Data reception and storage

[0821] The server receives personal information entered by the user and emotional information provided by the emotion engine, and stores it in a database. This data is used as the basis for generating digital twins and simulating their behavior.

[0822] Creating a digital twin

[0823] The server runs a generative AI model based on the stored personal and emotional information to generate a digital twin of the user. The generated digital twin is a virtual model that mimics the user's characteristics, behavioral patterns, and even emotional state. This model is used to simulate future behavior.

[0824] Behavioral simulation and prediction

[0825] The server simulates the behavior of the digital twin in response to the user's request, calculating predicted behavior and risk levels. This simulation utilizes past data, general behavioral patterns, and even the user's current emotional state to predict multiple options the user may choose.

[0826] Providing advice

[0827] Based on the simulation results, the server generates advice on the optimal options and risk levels for the user and sends this information to the device.

[0828] 2. Terminal Processing

[0829] User Data Input

[0830] The device provides an interface where users can input information about themselves and their families, and also works with an emotion engine to obtain user emotional information.

[0831] Sending data

[0832] The device executes protocol processing to transmit the input personal information and emotional information to the server. An encryption protocol is used for transmission to prevent information leakage.

[0833] Displaying Advice

[0834] The device displays the advice and risk level information sent from the server in an easy-to-read format for the user, who can then make decisions about their actions based on this information.

[0835] 3. User Actions

[0836] Entering information

[0837] Users access a dedicated personal page using their device and enter personal information, past behavior history, etc. Emotional information is also provided using an emotion engine.

[0838] Check the advice and choose your course of action

[0839] Users can review the advice provided via their device and choose the best course of action, taking into account the risk level and recommendations.

[0840] Providing Feedback

[0841] Users provide feedback on the results of their actions and their satisfaction with them, which is sent to a server and used to improve the accuracy of the digital twin's predictive model.

[0842] Specific examples

[0843] For example, if a user uses the system for health management purposes:

[0844] 1. The user uses a device to enter past health data and current lifestyle habits into their personal page. They also use the emotion engine to enter their current emotional state.

[0845] 2. The server receives this information and creates a digital twin.

[0846] 3. The server uses the generated digital twin to simulate the user's health behavior and calculate the optimal health strategy and its risk level.

[0847] 4. The server sends this information to the user's device.

[0848] 5. The device displays the health advice and risk level sent from the server.

[0849] 6. The user decides on health management actions based on the displayed advice and enters the results into the system as feedback.

[0850] Through these steps, the system predicts the user's behavior and takes their emotional state into account to help them make better decisions, allowing them to achieve their goals more safely and efficiently.

[0851] The processing flow will be explained below.

[0852] Program processing steps

[0853] 1. Server Processing

[0854] Step 1: Receiving and storing data

[0855] The server receives personal information entered by the user and emotional information provided by the emotion engine, including name, age, gender, health information, purchase history, and behavioral history.

[0856] The server processes the received data and stores it in a database in an appropriate format, including emotional information.

[0857] Step 2: Create a digital twin

[0858] The server runs a generative AI model based on the stored personal and emotional information, which combines the user's characteristics with real-time emotional data to generate a digital twin.

[0859] The server stores the generated digital twin profile and the results of the initial behavioral simulation based on it in a database.

[0860] Step 3: Behavioral simulation and prediction

[0861] Based on the user's request, the server runs a simulation of a specific behavior by the digital twin, where emotional information is used as part of the simulation to predict behavior based on the user's emotional state.

[0862] The server analyzes the simulation results and calculates behavior predictions and risk levels, and the results are prepared for delivery to the user.

[0863] Step 4: Providing advice

[0864] The server generates analysis results and advice, which includes specific action options and risk levels, and sends the data to the device.

[0865] 2. Terminal Processing

[0866] Step 1: User Data Input

[0867] The device provides an interface where users can input information about themselves and their families, and also works with an emotion engine to obtain user emotional information.

[0868] For example, the emotion engine recognizes the user's facial expressions and tone of voice and analyzes their emotions.

[0869] Step 2: Sending data

[0870] The device sends the entered personal information and acquired emotion information to the server, where the integrity of the data is verified and the data is transmitted securely using an encryption protocol.

[0871] Step 3: Receive and view advice

[0872] The device receives advice and risk level information sent from the server.

[0873] The device displays the received information in an easy-to-read format for the user, who can then make decisions about their actions based on this information.

[0874] 3. User Actions

[0875] Step 1: Enter your information

[0876] Users access their personal page using their device and enter their personal information and past behavioral history. The system also uses an emotion engine to provide information about their current emotions. For example, if a user is feeling stressed, that information is also entered into the system.

[0877] Step 2: Review the advice

[0878] Users can check the advice and risk levels displayed on their device and choose the best course of action. For example, the system may recommend low-risk options based on the user's current emotional state and past data.

[0879] Step 3: Provide feedback

[0880] Users provide feedback on their actual behavior and their satisfaction with it. This feedback is sent to the server via their device and used to improve the accuracy of the digital twin's predictive model. For example, a user can perform a recommended behavior and report the results to the system.

[0881] Specific examples

[0882] For example, if a user uses the system for health management purposes:

[0883] Step 1:

[0884] Users use their devices to input their past health data and current lifestyle habits into their personal page, and also input their current emotional state using the emotion engine.

[0885] Step 2:

[0886] The terminal transmits this information to the server.

[0887] Step 3:

[0888] The server uses this information to create a digital twin.

[0889] Step 4:

[0890] The server uses the created digital twin to simulate health behavior and calculate the optimal health strategy and its risk level.

[0891] Step 5:

[0892] The server sends the calculation results to the terminal.

[0893] Step 6:

[0894] The device displays health advice and risk levels sent from the server.

[0895] Step 7:

[0896] The user decides on health management actions based on the displayed advice and inputs the results into the system as feedback.

[0897] In this way, the system of the present invention predicts the user's behavior and takes their emotional state into account to help them make better decisions, allowing them to achieve their goals more safely and efficiently.

[0898] Example 2

[0899] 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."

[0900] In recent years, there has been an increasing need for systems that provide highly accurate behavioral predictions and advice that take into account an individual's emotional state and behavioral patterns. However, conventional systems lack an established method for integrating personal and emotional information to simulate user behavior. As a result, they have faced issues such as low prediction accuracy and failure to meet user expectations. Furthermore, they lacked a mechanism for improving the model based on feedback, making it difficult to improve user satisfaction.

[0901] 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 personal information and emotional information of a user, means for encrypting and transmitting the personal information and emotional information input by the user, means for storing the received personal information and emotional information in a database, means for generating a digital twin by a generative AI model using the stored personal information and emotional information, means for performing a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and a risk level, and means for providing the user with the behavioral prediction and risk level obtained as a result of the behavioral simulation. This makes it possible to provide highly accurate behavioral predictions and advice that take into account the individuality of the user.

[0902] "User" refers to an individual or organization that uses the system and provides personal and emotional information.

[0903] "Personal information" refers to information that can identify a specific individual, such as a user's name, address, age, and gender.

[0904] "Emotion information" is data that represents the user's current emotional state, and is information obtained using an emotion engine.

[0905] An "emotion engine" is a software or hardware system for analyzing a user's emotional state.

[0906] "Terminal" means a device that a user uses to input, send, or receive information, including a computer, smartphone, tablet, etc.

[0907] A "server" is a computer system that receives, stores, and analyzes data sent by users.

[0908] A "database" is a repository of information installed within a server, and is a system for systematically storing and managing personal and emotional information.

[0909] A "generative AI model" is an artificial intelligence algorithm or program that generates a digital twin based on input data.

[0910] A "digital twin" is a virtual model created based on a user's personal and emotional information, which mimics the user's characteristics and behavioral patterns.

[0911] "Behavioral simulation" is the process of predicting a user's future behavior using a generated digital twin.

[0912] "Behavior prediction" is predictive data about a user's future behavior obtained through behavior simulation.

[0913] The "risk level" is a numerical value or evaluation that indicates the degree of risk associated with a particular behavior, calculated based on behavior prediction.

[0914] "Advice" refers to optimal behavioral options or recommendations provided to users based on the results of behavioral simulations.

[0915] "Feedback" is information provided by users about the results of their actual actions and their satisfaction, and is used to improve the accuracy of the system's model.

[0916] "My Private Page" is a web page or application interface where users can input and manage personal information, emotional information, purchase history, behavioral history, family information, etc.

[0917] The present invention relates to a system that generates a digital twin based on a user's personal information and emotional information, and provides predictions and advice through behavioral simulations using the digital twin. The following describes specific embodiments of the invention.

[0918] System Configuration

[0919] This system consists of four elements: a server, a terminal, a user, and an emotion engine. Each of these elements functions as follows:

[0920] Server Roles

[0921] The server receives, stores, and analyzes the personal information and emotional information sent by the user. First, the data sent from the device is securely transmitted using an encryption protocol (e.g., HTTPS). The server then stores this data in a database.

[0922] The server then runs a generative AI model to generate a digital twin of the user, which analyzes the stored personal and emotional information to create a virtual model that mimics the user's characteristics and behavioral patterns.

[0923] Based on the generated digital twin, the server runs a behavioral simulation that utilizes past data, typical behavioral patterns, and the user's current emotional state to calculate behavioral predictions and risk levels for multiple options the user may choose.

[0924] Finally, the server generates advice based on the simulation results and sends this information to the device. The advice provided to the user includes predicted behavior and risk levels.

[0925] Device Role

[0926] The terminal is a device that allows users to input, send, and receive information. Users access their personal page and enter their personal information, past behavioral history, and emotional state. The terminal works in conjunction with the emotion engine to obtain the user's emotional information.

[0927] The device sends the entered data to a server using an encryption protocol, and the server then sends advice and risk level information that is displayed to the user in an easy-to-read format.

[0928] User Roles

[0929] Users access their personal page and enter their personal information, past behavioral history, and emotional state. This information is often obtained using an emotion engine. The user checks the advice provided by the server and selects the most appropriate action. They also provide feedback on the results of their actual actions and their satisfaction with them. This feedback is sent to the server and used to improve the accuracy of the digital twin's prediction model.

[0930] Specific examples

[0931] For example, the flow when a user uses the system for health management purposes will be explained.

[0932] 1. The user uses a device to enter past health data and current lifestyle habits into their personal page, and also inputs their current emotional state using the emotion engine.

[0933] 2. The server receives this information and creates a digital twin.

[0934] 3. The server uses the generated digital twin to simulate the user's health behavior and calculate the optimal health strategy and its risk level.

[0935] 4. The server sends this information to the user's device.

[0936] 5. The device displays the health advice and risk level sent from the server.

[0937] 6. The user decides on health management actions based on the displayed advice and enters the results into the system as feedback.

[0938] Prompt Sentence Examples

[0939] What specific data items do users enter for health management?

[0940] What predictive models does the server use to simulate behavior?

[0941] What exactly is the process for providing feedback?

[0942] Through these steps, the system predicts the user's behavior and takes their emotional state into account to help them make better decisions, allowing them to achieve their goals more safely and efficiently.

[0943] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0944] Step 1: Entering User Data

[0945] Users access their personal page using their own devices and enter a wide range of data, including personal information, past behavioral history, health data, and emotional state, such as name, age, weight, food records, and exercise volume.

[0946] Input: Personal information, past behavioral history, health data, emotional state

[0947] Output: Encrypted personal and emotional information

[0948] Specific behavior:

[0949] Users can open their personal page using a browser or mobile app.

[0950] Enter the required information using the text boxes and drop-down menus.

[0951] Activate the emotion engine and measure your emotional state.

[0952] Step 2: Sending data

[0953] The device encrypts the input data before sending it to the server. By using an encryption protocol (e.g. HTTPS), information leakage is prevented.

[0954] Input: Personal and emotional information entered by the user

[0955] Output: Data encrypted and sent to the server

[0956] Specific behavior:

[0957] The terminal encrypts the user's input data.

[0958] The encrypted data is sent to the server.

[0959] Step 3: Receiving and storing data

[0960] The server receives the data sent from the device and stores it in a database. It checks the integrity of the received data and stores it appropriately.

[0961] Input: Data sent encrypted from the device

[0962] Output: Personal and emotional information stored in a database

[0963] Specific behavior:

[0964] The server decrypts the received data and checks its integrity.

[0965] Store data appropriately in the database.

[0966] Step 4: Create a digital twin

[0967] The server then uses the stored personal and emotional information to run a generative AI model to create a digital twin, which virtually replicates the user's characteristics and behavioral patterns.

[0968] Input: Personal and emotional information stored in a database

[0969] Output: Generated digital twin

[0970] Specific behavior:

[0971] The server provides the data needed for the generative AI model.

[0972] The generative AI model generates a digital twin and returns the data to the server.

[0973] Step 5: Running a behavioral simulation

[0974] The server uses the created digital twin to simulate behavior under various conditions, taking into account past data, general behavioral patterns, and the user's current emotional state to make predictions.

[0975] Input: Generated digital twin, historical data, general behavioral patterns, current emotional state

[0976] Output: Behavioral prediction and risk level

[0977] Specific behavior:

[0978] The server runs the simulation engine and runs multiple scenarios.

[0979] Prediction data and risk levels are calculated from the results of each scenario.

[0980] Step 6: Generating and serving advice

[0981] The server generates advice for the user based on the results of the behavioral simulation, and the advice is sent to the terminal and provided to the user.

[0982] Input: Simulation results, predicted behavior and risk level

[0983] Output: Advice given to the user

[0984] Specific behavior:

[0985] The server creates customized advice based on the generated prediction data.

[0986] The created advice is sent to the device.

[0987] Step 7: User reviews advice and chooses action

[0988] The user uses the device to review the received advice, which includes action options and their risk levels. The user can then choose the best course of action based on the advice.

[0989] Input: Advice sent by the server

[0990] Output: User's action choice

[0991] Specific behavior:

[0992] The terminal displays the advice from the server.

[0993] Users decide what to do based on their situation and goals.

[0994] Step 8: Provide feedback

[0995] Users provide feedback on the results of their actions and their satisfaction with them, which is sent to the server and used to improve the accuracy of the model.

[0996] Input: User behavior results and satisfaction

[0997] Output: Feedback sent to the server

[0998] Specific behavior:

[0999] The user enters the results of their actual actions into the terminal.

[1000] The terminal transmits the feedback data to the server.

[1001] The server analyzes the received feedback and adjusts the parameters of the model.

[1002] (Application example 2)

[1003] 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."

[1004] While conventional systems were able to predict behavior and provide advice based on a user's personal and emotional information, they were unable to provide useful advice in real time in actual purchasing situations. When users are physically in a store, it is difficult to receive optimal advice or coupon information when selecting products, and there was a need to improve the purchasing experience.

[1005] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting a user's personal information and emotional state, means for receiving and storing the input personal information and emotional information, means for generating a digital twin based on the received personal information and emotional information, means for running a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and risk level, means for providing the calculation results to the user, and means for displaying purchasing advice in real time when the user is selecting a product in a physical store. This allows the user to receive optimal advice and coupon information in real time even in a physical store, improving the purchasing experience.

[1006] A "user" is an individual who uses this system and provides personal and emotional information.

[1007] "Personal information" refers to all data related to a user, such as basic information, purchasing history, behavioral history, and lifestyle habits.

[1008] "Emotional state" is information that indicates the user's current mood and psychological state.

[1009] A "digital twin" is a virtual model that mimics a user's characteristics and behavioral patterns, created based on the user's personal and emotional information.

[1010] "Behavioral simulation" is the process of using a generated digital twin to predict a user's behavioral patterns and calculate their behavioral options and risk levels.

[1011] The "risk level" indicates the degree of risk that a particular behavior may cause, as determined by the behavioral simulation.

[1012] "Means for displaying purchasing advice in real time" refers to a function that instantly displays optimal purchasing advice via a device such as a smartphone when a user is selecting a product in a physical store.

[1013] "Feedback" is information that provides the system with the results of a user's actual actions and their satisfaction.

[1014] Overall system overview

[1015] This invention is a system that generates a digital twin based on a user's personal information and emotional state, and provides predictions and advice through behavioral simulation using the digital twin. The main elements of this system include a server, a terminal, a user, and an emotion engine.

[1016] Server Roles

[1017] The server performs the following process.

[1018] 1. Data Receipt and Storage

[1019] The server receives personal and emotional information from users and stores it in a database, which is used to generate digital twins and simulate their behavior.

[1020] 2. Creating a digital twin

[1021] The server runs a generative AI model based on the stored personal and emotional information to create a digital twin of the user, a virtual model that mimics the user's characteristics, behavioral patterns, and emotional state.

[1022] 3. Behavioral Simulation and Prediction

[1023] The server uses a digital twin to simulate the user's behavior and calculates predicted behavior and risk levels, using historical data, general behavioral patterns, and current emotional state.

[1024] 4. Real-time purchasing advice

[1025] When users are selecting products in physical stores, the system provides optimal purchasing advice in real time, allowing users to make more effective in-store purchases.

[1026] Device Role

[1027] The terminal performs the following processing.

[1028] 1. User data input

[1029] The device provides an interface for users to input their personal and emotional information.

[1030] 2. Data transmission

[1031] The device sends the entered personal and emotional information to a server, using an encryption protocol to prevent information leakage.

[1032] 3. Displaying Advice

[1033] The device displays advice and risk level information sent from the server to the user, allowing the user to choose their course of action based on this information.

[1034] User Roles

[1035] The user performs the following process:

[1036] 1. Enter your information

[1037] Users use their devices to access their dedicated personal page and enter personal and emotional information.

[1038] 2. Check the advice and choose your course of action

[1039] Users can review the advice displayed on their device and choose the best course of action, taking into account risk levels and recommendations.

[1040] 3. Providing Feedback

[1041] After taking an action, users provide feedback about the results and their satisfaction, which is sent to the server and used to improve the accuracy of the digital twin's predictive model.

[1042] Hardware and software used

[1043] The following hardware and software are used to implement this system:

[1044] Server: The backend is built using Python and Flask, and Pandas, scikit-learn, and TensorFlow are used for data analysis.

[1045] Database: Use SQLite or Firebase for data storage.

[1046] Device: Develop a smartphone app that runs on iOS / Android and provide an interface to users.

[1047] Encryption Protocols: We use encryption technologies such as SSL / TLS to ensure data security.

[1048] Specific prompt examples

[1049] For example, the following prompt might be generated for a user looking to purchase a health drink:

[1050] User ID: 12345, Personal information: { Purchase history: ["health drinks", "supplements"]}, Emotion information: { Mood: "stressful"}

[1051] Generated advice: "I recommend a drink that's good for relieving stress. You can use this coupon."

[1052] Based on this prompt, users can select the most suitable product in the store, providing a better shopping experience.

[1053] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1054] Step 1:

[1055] The user uses the device to input personal and emotional information. The input information is retrieved through the device interface. This information includes the user's purchasing history, lifestyle habits, and current mood. The input data is converted into JSON format and sent to the server using an encryption protocol (e.g., SSL / TLS).

[1056] Step 2:

[1057] The server receives the personal information and emotional information sent from the device and stores it in a database. The received data is filtered and parsed to remove outliers and invalid data. The data is then stored in a database (e.g., SQLite or Firebase) and used as the basis for subsequent digital twin generation and simulation processing.

[1058] Step 3:

[1059] The server runs a generative AI model based on the stored personal and emotional information to generate a digital twin of the user. The generative AI model uses a machine learning framework (e.g., TensorFlow) to analyze the input data and create a virtual model that mimics the user's characteristics and behavioral patterns. The generated digital twin is then stored in a database.

[1060] Step 4:

[1061] The server then runs a behavioral simulation using the created digital twin. The behavioral simulation takes into account statistical data, general behavioral patterns, and the user's current emotional state to predict the user's purchasing behavior. The simulation results also calculate the risk level for multiple choices. This data is then saved in JSON format.

[1062] Step 5:

[1063] When a user selects a product in a physical store, the smartphone device uses the camera and GPS to identify the product and acquire its location. The identified product information and store information are immediately sent to the server. The transmitted data includes the product ID and location information.

[1064] Step 6:

[1065] The server generates optimal purchasing advice in real time based on the received product information and location information. Using a generative AI model, it compares past data and simulation results to determine the advice and coupon information that should be provided to the user. The generated advice is then sent to the device.

[1066] Step 7:

[1067] The device instantly displays the advice and coupon information sent from the server to the user on the smartphone's display, allowing the user to choose the best purchasing behavior based on this information.

[1068] Step 8:

[1069] Users provide feedback on the results of their purchases and their satisfaction. Feedback information is entered through the device interface and sent to the server. The submitted feedback is received and stored on the server, and used to improve the accuracy of the digital twin.

[1070] 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.

[1071] 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.

[1072] 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.

[1073] [Third embodiment]

[1074] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1075] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1076] 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).

[1077] 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.

[1078] 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.

[1079] 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).

[1080] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1081] 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.

[1082] 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.

[1083] 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.

[1084] 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.

[1085] 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."

[1086] The present invention relates to a system that creates a digital twin based on personal information provided by a user and provides predictions and advice through behavioral simulations using the digital twin. The specific configuration of the system and program processing are described below.

[1087] System Overview

[1088] The system consists of three elements: a server, a device, and the user. The user enters personal information via the device, and the server receives, stores, and analyzes that information to generate a digital twin. The generated digital twin then performs a behavioral simulation, and the resulting behavioral predictions and advice are provided to the user via the device.

[1089] 1. Server Processing

[1090] Data reception and storage

[1091] The server receives personal information (such as name, age, gender, health information, purchase history, and behavioral history) entered by the user and stores it in a database. This data is used as the basis for generating digital twins and simulating their behavior.

[1092] Creating a digital twin

[1093] The server runs a generative AI model based on the stored personal information to generate a digital twin of the user. The generated digital twin is a virtual model that mimics the user's characteristics and behavioral patterns. This model is used to simulate future behavior.

[1094] Behavioral simulation and prediction

[1095] The server simulates the behavior of the digital twin in response to the user's request, and calculates predicted behavior and risk levels. This simulation utilizes past data and general behavioral patterns to predict multiple options the user may choose.

[1096] Providing advice

[1097] Based on the simulation results, the server generates advice on the optimal options and risk levels for the user and sends this information to the device.

[1098] 2. Terminal Processing

[1099] User Data Input

[1100] The device provides an interface where users can enter information about themselves and their family members, and the data they enter is then sent to a server.

[1101] Sending data

[1102] The device checks the integrity of the personal information entered and then transmits it securely to the server using an encryption protocol to prevent information leaks.

[1103] Displaying Advice

[1104] The device displays the advice and risk level information sent from the server in an easy-to-read format for the user, who can then make decisions about their actions based on this information.

[1105] 3. User Actions

[1106] Entering information

[1107] Users access a dedicated personal page on their device and enter their personal information, past behavioral history, etc. This improves the accuracy of the digital twin and enables more reliable predictions.

[1108] Check the advice and choose your course of action

[1109] Users can review the advice provided via their device and choose the best course of action, taking into account the risk level and recommendations.

[1110] Providing Feedback

[1111] Users provide feedback on the results of their actions and their satisfaction with them, which is sent to a server and used to improve the accuracy of the digital twin's predictive model.

[1112] Specific examples

[1113] For example, if a user is considering investing in stocks:

[1114] 1. The user enters their past investment history and current asset status via the terminal.

[1115] 2. The server receives this information and creates a digital twin.

[1116] 3. The server uses the generated digital twin to simulate the user's investments and calculates the optimal investment options and their risk levels.

[1117] 4. The server provides this information to the user, who then confirms it via their device.

[1118] 5. The user decides on investment actions based on the advice provided and enters the results into the system as feedback.

[1119] In this way, the system according to the present invention assists in predicting user behavior and managing the risks involved, enabling safer and more efficient decision-making.

[1120] The processing flow will be explained below.

[1121] Program processing steps

[1122] 1. Server Processing Steps

[1123] Step 1: Receiving and storing data

[1124] The server receives personal information entered by the user, including name, age, gender, health information, purchase history, and behavioral history.

[1125] The server performs initial processing on the received data and stores it in a database in an appropriate format.

[1126] Step 2: Create a digital twin

[1127] The server runs a generative AI model based on the stored personal information.

[1128] The server stores the generated digital twin profile in a database.

[1129] Step 3: Behavioral simulation and prediction

[1130] The server performs a simulation of a specific behavior by the digital twin based on the user's request.

[1131] The server analyzes the simulation results and calculates behavior predictions and risk levels.

[1132] Step 4: Providing advice

[1133] The server generates optimal options and risk advice based on the analysis results and sends this information to the terminal.

[1134] 2. Terminal Processing Steps

[1135] Step 1: User Data Input

[1136] The device provides users with a dedicated personal page and displays an interface for entering personal information, family composition, purchasing history, behavioral history, etc.

[1137] Step 2: Sending data

[1138] The terminal executes protocol processing to transmit the input data to the server.

[1139] The terminal checks the integrity of the transmitted data and sends it to the server using an encryption protocol.

[1140] Step 3: Receive and view advice

[1141] The device receives advice and risk level information sent from the server.

[1142] The terminal displays the received information in a user-friendly format.

[1143] 3. User Processing Steps

[1144] Step 1: Enter your information

[1145] Users access their dedicated personal page via their terminal and enter personal information, past behavioral history, purchasing history, etc.

[1146] Step 2: Review the advice

[1147] The user checks the advice and risk level displayed on the device and chooses the most appropriate course of action.

[1148] Step 3: Provide feedback

[1149] The user provides feedback about the actual results of the action and the level of satisfaction to the server through the terminal.

[1150] Users contribute to improving the accuracy of the predictive model through feedback.

[1151] Specific examples

[1152] For example, if a user is considering investing in stocks:

[1153] Step 1:

[1154] The user enters past investment history and current asset status into their personal page via a terminal.

[1155] Step 2:

[1156] The terminal transmits this information to the server.

[1157] Step 3:

[1158] The server uses this information to create a digital twin.

[1159] Step 4:

[1160] The server uses the created digital twin to run a stock investment simulation and calculate the optimal investment options and their risk levels.

[1161] Step 5:

[1162] The server sends the calculation results to the terminal.

[1163] Step 6:

[1164] The terminal displays the investment advice and risk level sent from the server.

[1165] Step 7:

[1166] The user makes investment decisions based on the displayed advice.

[1167] Step 8:

[1168] Users provide feedback on their investment results and satisfaction via their terminals.

[1169] Through this series of steps, the system according to the present invention predicts user behavior and supports better decision-making.

[1170] Example 1

[1171] 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."

[1172] Conventional simulation systems have had limitations in accuracy and practicality when it comes to predicting user behavior and managing risks. Issues also exist regarding the handling of personal information and data security. Furthermore, they lack a mechanism for continuously improving the accuracy of the system through feedback, limiting the credibility and effectiveness of the advice users receive. The purpose of this invention is to solve these issues and provide users with more accurate predictions and useful advice.

[1173] 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.

[1174] In this invention, the server includes: means for inputting a user's personal information; means for receiving and storing the input personal information; means for generating a digital twin using a generative AI model based on the received personal information; means for generating prompts for generating the digital twin; means for performing a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and risk level; and means for providing the calculation results to the user. This enables a highly accurate digital twin to be generated based on the user's personal information, enabling reliable behavioral simulation and prediction. Furthermore, the accuracy of the system can be improved by utilizing continuous feedback, enabling more accurate advice to be provided to the user.

[1175] "User personal information" refers to individual data such as name, age, gender, health information, purchase history, and behavioral history that a user provides to the system.

[1176] A "generative AI model" is an artificial intelligence model that generates a digital twin based on a user's personal information.

[1177] A "digital twin" is a virtually generated model that mimics a user's characteristics and behavioral patterns.

[1178] A "prompt" is an instruction provided to a generative AI model to generate a digital twin.

[1179] "Behavioral simulation" is a virtual experiment that uses a generated digital twin to predict future user behavior.

[1180] "Behavioral prediction" is a prediction of a user's future behavior obtained through behavioral simulation.

[1181] The "risk level" is the degree of risk of the actions that the user should take, calculated based on the simulation results.

[1182] "Feedback" is information provided by users about the results of their actual actions and their satisfaction, and is used to improve the accuracy of the system.

[1183] The present invention relates to a system that creates a digital twin based on personal information provided by a user and provides predictions and advice through behavioral simulations using the digital twin. The specific configuration of the system and program processing are described below.

[1184] System Overview

[1185] The system consists of three elements: a server, a device, and the user. The user enters personal information via the device, and the server receives, stores, and analyzes that information to generate a digital twin. The generated digital twin then performs a behavioral simulation, and the resulting behavioral predictions and advice are provided to the user via the device.

[1186] Server Processing

[1187] Data reception and storage

[1188] The server receives personal information entered by the user (such as name, age, gender, health information, purchase history, and behavioral history) and stores it in a database. This data is used as the basis for generating digital twins and simulating their behavior. Suitable databases include MySQL and PostgreSQL.

[1189] Creating a digital twin

[1190] The server runs a generative AI model (e.g., OpenAI's GPT-4) based on the stored personal information to generate a digital twin for the user. Specifically, the generative AI model is run using the prompt, "Generate a digital twin based on the user's name, age, gender, and health information." The generated digital twin is a virtual model that mimics the user's characteristics and behavioral patterns. The generated digital twin is then stored back in the database.

[1191] Behavioral simulation and prediction

[1192] The server simulates the behavior of the digital twin in response to the user's request and calculates predicted behavior and risk levels. This simulation utilizes past data and general behavioral patterns. For example, a simulation can be performed using the prompt, "Predict the investment risk level for the next year using the target user's digital twin."

[1193] Providing advice

[1194] Based on the simulation results, the server generates advice on the user's optimal options and risk level and sends that information to the device, providing specific advice such as "regular exercise and a balanced diet will reduce risk by 30%."

[1195] Terminal handling

[1196] User Data Input

[1197] The device provides an interface where users can enter information about themselves and their family members, and the data they enter is then sent to a server.

[1198] Sending data

[1199] The device checks the integrity of the personal information entered and then securely transmits it to the server using encryption protocols such as SSL / TLS to prevent information leaks.

[1200] Displaying Advice

[1201] The terminal displays the advice and risk level information sent from the server in an easy-to-read format for the user, such as a dashboard displaying information like "Best investment option: Stock A, Estimated risk level: Low."

[1202] User Action

[1203] Entering information

[1204] Users access a dedicated personal page on their device and enter personal information, past activity history, etc. For example, by entering "past exercise habits: 30 minutes of jogging three times a week," the accuracy of the digital twin improves, enabling more reliable predictions.

[1205] Check the advice and choose your course of action

[1206] Users can review the advice provided via their device and choose the most appropriate course of action. They can make decisions based on their risk level and recommendations. For example, they can select "Perform recommended exercise habits" and actually start exercising.

[1207] Providing Feedback

[1208] Users provide feedback on the results of their actual actions and their satisfaction with them, for example, "After exercising for a month, my health condition has improved," and the server receives this feedback and uses it to improve the accuracy of the digital twin's prediction model.

[1209] Specific examples

[1210] For example, if a user is considering investing in stocks:

[1211] 1. The user enters their past investment history and current asset status via the terminal (e.g., "Investment history: past 3 years, current assets: 10 million yen").

[1212] 2. The server receives this information and generates a digital twin (e.g., prompt: "Generate a digital twin based on the user's investment history and current asset status").

[1213] 3. The server uses the generated digital twin to simulate the user's investment and calculates the optimal investment option and its risk level (e.g., prompt: "Predict the target user's investment risk level for the next year using the target user's digital twin").

[1214] 4. The server provides this information to the user, who then confirms it via their device.

[1215] 5. The user decides on investment actions based on the advice provided and enters the results as feedback into the system (e.g., feedback content: "Investment performance: Stock A, Result: Growth rate 10%)."

[1216] In this way, the system according to the present invention assists in predicting user behavior and managing the risks involved, enabling safer and more efficient decision-making.

[1217] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1218] Step 1: Data entry

[1219] explanation:

[1220] Users enter personal information (e.g., name, age, gender, health information, purchase history, behavioral history, etc.) through the device.

[1221] Specific operation: The user fills in the information in the dedicated input form and presses the submit button.

[1222] Input: Personal information such as name, age, gender, and health information

[1223] Output: Well-formatted personal data

[1224] Step 2: Receiving and storing data

[1225] explanation:

[1226] The server receives the personal information sent from the terminal and checks the consistency of the data.

[1227] What happens: The server validates the received data, checking for formatting errors and incompleteness, and then stores it in a database (e.g., MySQL, PostgreSQL).

[1228] Input: Well-formatted personal data

[1229] Output: Personal information stored in a database

[1230] Step 3: Generate a prompt statement

[1231] explanation:

[1232] Based on the stored personal information, the server generates a prompt sentence to be passed to a generative AI model (e.g., OpenAI's GPT-4).

[1233] What it does: The server uses a script to generate a document such as "Create a digital twin based on the user's name, age, gender, and health information."

[1234] Input: Personal information data

[1235] Output: Prompt sentence for the generative AI model

[1236] Step 4: Create a digital twin

[1237] explanation:

[1238] The server inputs the generated prompt sentences into a generative AI model to generate a digital twin.

[1239] How it works: The server inputs a prompt statement to launch a generative AI model (e.g., GPT-4) and generates a digital twin of the user.

[1240] Input: prompt statement

[1241] Output: Data of the generated digital twin

[1242] Step 5: Behavioral simulation

[1243] explanation:

[1244] The server runs a behavioral simulation based on the generated digital twin and calculates behavioral predictions and risk levels.

[1245] Specific operation: Based on the digital twin, the server runs a simulation algorithm and generates predictive data for various scenarios (e.g., health risks, investment risks).

[1246] Input: Digital twin data

[1247] Output: Behavioral simulation results and risk level

[1248] Step 6: Generating and serving advice

[1249] explanation:

[1250] Based on the simulation results, the server generates advice for the user on the optimal behavioral options and their risk levels.

[1251] Specific operation: The server analyzes the simulation data, documents the optimal options and risk levels, and sends them to the terminal.

[1252] Input: Behavioral simulation results and risk level

[1253] Output: Advice document

[1254] Step 7: Viewing Advice

[1255] explanation:

[1256] The terminal displays the advice received from the server in an easy-to-read format for the user.

[1257] What it does: The device uses a UI such as a dashboard to visually display advice and risk levels.

[1258] Input: Advice document

[1259] Output: Advice information displayed on the screen

[1260] Step 8: Choose and take action

[1261] explanation:

[1262] The user reviews the advice provided via the device and selects and executes the most appropriate action.

[1263] Specific Action: The user reviews the advice and takes the suggested action (e.g., manage their health or invest).

[1264] Input: On-screen advice information

[1265] Output: User's choice

[1266] Step 9: Provide feedback

[1267] explanation:

[1268] Users provide feedback on their actual behavior and satisfaction.

[1269] Specific operation: The user enters the results of their actions and their evaluation in a dedicated feedback form and submits it.

[1270] Input: User action results and ratings

[1271] Output: Feedback data

[1272] Step 10: Receive feedback and improve the model

[1273] explanation:

[1274] The server receives the feedback data and uses it to improve the accuracy of the digital twin and refine the model.

[1275] Specific operation: The server analyzes the received feedback data and uses it as training data for the generative AI model.

[1276] Input: Feedback data

[1277] Output: Improved model data

[1278] (Application example 1)

[1279] 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."

[1280] Conventional food delivery systems are unable to provide optimal meal recommendations based on a user's eating habits, health status, or past order history, resulting in issues with user satisfaction and health management. Furthermore, delivery time predictions can be inaccurate, reducing user convenience. The present invention aims to solve these problems and provide users with more personalized meal recommendations and accurate delivery time predictions.

[1281] 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.

[1282] In this invention, the server includes means for inputting personal information of a user, means for receiving and storing the input personal information, means for generating a digital twin based on the received personal information, means for running a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and risk level, means for providing the calculation results to the user, means for inputting data on the user's eating habits and preferences, and means for making meal suggestions and predicting delivery times. This enables personalized meal suggestions and accurate predictions of delivery times based on the user's past eating history and health information.

[1283] "Means for inputting user information" refers to a device or software that provides an interface for users to input information about themselves.

[1284] "Means for receiving and storing information" refers to a device or software that has the function of receiving information entered by a user to a server and storing it in a database.

[1285] "Means for generating a digital twin" refers to algorithms and software for generating a virtual model based on user input.

[1286] "Means for performing behavioral simulation" refers to a device that provides algorithms and software for virtually predicting a user's behavior and choices using the generated digital twin.

[1287] "Means for predicting behavior and calculating risk levels" refers to a device or software that numerically analyzes and calculates a user's future behavior and the associated risks based on the results of a behavioral simulation.

[1288] "Means for providing calculation results" refers to a device or software with an interface for presenting the results of the simulation and risk calculation to a user.

[1289] "Means for inputting data relating to a user's dietary habits and preferences" means a device or software that provides an interface for a user to input information about their dietary habits and preferences.

[1290] "Means for meal suggestions and delivery time prediction" refers to algorithms and software that suggest optimal meal menus and predict accurate delivery times based on user input and digital twin information.

[1291] The present invention relates to a system for providing optimal meal suggestions and delivery time predictions based on a user's eating habits and preferences in a food delivery service. The specific configuration and operation of the system are described below.

[1292] 1. System Configuration

[1293] The system mainly comprises a server, a user terminal, and a generative AI model.

[1294] 1. Server:

[1295] Data Receipt and Storage:

[1296] The server receives personal information (such as name, age, gender, health information, dietary preferences, and past order history) sent from the user's device and stores it in a database. The software used is Python and Flask, and the database is managed by MySQL.

[1297] Digital Twin Generation:

[1298] Based on the stored data, a generative AI model (e.g., GPT-4) is used to generate a digital twin of the user, a virtual model of the user that mimics the user's characteristics and past dietary history.

[1299] Behavioral Simulation and Prediction:

[1300] The resulting digital twin is then used to simulate the user's next likely meal order and optimal delivery time, which then provides meal suggestions and calculates a risk level.

[1301] Advice provided:

[1302] Based on the simulation results, optimal meal suggestions and delivery time predictions are generated and sent to the terminal.

[1303] 2. User Device:

[1304] Information input interface:

[1305] It provides an interface where users can enter their personal information, food preferences, and past ordering history, which is then sent to a server.

[1306] Advice display:

[1307] The server sends meal suggestions and delivery time advice to the user in an easy-to-read format, which the user can use to make ordering decisions.

[1308] 3. Generative AI Model:

[1309] Digital twin generation and behavioral simulation:

[1310] Using OpenAI's GPT-4 model, a digital twin is generated based on the user's input data, and behavioral simulations are performed using that twin.

[1311] 2. Specific Hardware and Software Used

[1312] Server: High-performance server equipment, Python, Flask, MySQL, generative AI models (e.g., GPT-4)

[1313] User device: smartphone or tablet

[1314] Database: MySQL

[1315] Communication protocol: HTTPS

[1316] 3. Program processing explanation

[1317] The server first receives personal information entered by the user and stores it in a MySQL database. It then uses the data to generate a digital twin using a generative AI model (GPT-4) and performs a behavioral simulation. From the simulation results, it calculates optimal meal recommendations and predicted delivery times, and sends them to the user's device. The user's device then displays the received advice and assists the user in their ordering process.

[1318] 4. Specific Examples

[1319] For example, if a user has frequently ordered "salads" or "grilled chicken" in the past, a digital twin will be created based on those preferences and suggest a "salad chicken wrap" as the best menu item for the next order. Additionally, based on past delivery history, the system will predict the quickest delivery time for the user's preferred time slot.

[1320] 5. Examples of prompts

[1321] Here are some example prompts to input to the generative AI model:

[1322] Generate a digital twin for the following user data: name: "Username", age: "Age", gender: "Gender", health_info: "Health Information", dietary_restrictions: "Restricted Foods", meal_history: "Past Meal History"

[1323] Based on this digital twin, predict the next best meal for them.

[1324] As described above, the present invention provides a system that provides personalized meal suggestions and accurate delivery time predictions based on a user's eating habits and preferences.

[1325] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1326] Step 1:

[1327] The server receives the user's personal information (name, age, gender, health information, dietary preferences, past order history, etc.) sent from the device and stores it in a database. Specifically, it uses Flask to receive JSON-formatted data sent from the device and inserts that data into a MySQL database. The user's personal information is input, and the process of saving it to the database is completed as output.

[1328] Step 2:

[1329] The server generates a digital twin using a generative AI model (e.g., GPT-4) based on the stored personal information data. Specifically, a Python script is used to retrieve the necessary data from the database and generate a prompt to input it into the generative AI model. A digital twin is generated based on this prompt, and the generation result is returned to the server in JSON format. The user information in the database is used as input, and the generated digital twin model is obtained as output.

[1330] Step 3:

[1331] The server then uses the generated digital twin to perform a behavioral simulation. Specifically, the digital twin's data is re-input into the generative AI model, which simulates the next meal the user is likely to order and the optimal delivery time. The results are returned in JSON format as the simulation results. The digital twin is the input, and the simulation results are the output.

[1332] Step 4:

[1333] The server then makes meal suggestions and delivery time predictions based on the simulation results and provides them to the user. Specifically, it converts the simulation results into a format that is easy for humans to understand (text or graphics) and sends it back to the terminal. The simulation results are input, and advice information for the user is generated as output.

[1334] Step 5:

[1335] The user uses the device to check the displayed meal suggestions and delivery time estimates and decides on an order. Specifically, the user refers to the advice presented on the smartphone application and selects their next meal. The determined order details are then sent to the server. The input is the meal suggestions and delivery time estimates, and the order details are generated as the output.

[1336] Step 6:

[1337] Users provide feedback on the results of their actual orders and the service to the server via their devices. Specifically, after completing an order, information is entered through a satisfaction survey or feedback form, which is then sent to the server. This feedback is used to improve the accuracy of subsequent behavioral simulations and digital twins. The feedback information is the input, and the output is saved in a database.

[1338] 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.

[1339] The present invention relates to a system that generates a digital twin based on a user's personal information and emotional state, and provides predictions and advice through behavioral simulations using the digital twin. The specific configuration of the system and program processing are described below.

[1340] System Overview

[1341] This system consists of four elements: a server, a device, a user, and an emotion engine. The user provides personal and emotional information via the device, and the server receives, stores, and analyzes that information to generate a digital twin. The generated digital twin then performs a behavioral simulation, and the resulting behavioral predictions and advice are provided to the user via the device.

[1342] 1. Server Processing

[1343] Data reception and storage

[1344] The server receives personal information entered by the user and emotional information provided by the emotion engine, and stores it in a database. This data is used as the basis for generating digital twins and simulating their behavior.

[1345] Creating a digital twin

[1346] The server runs a generative AI model based on the stored personal and emotional information to generate a digital twin of the user. The generated digital twin is a virtual model that mimics the user's characteristics, behavioral patterns, and even emotional state. This model is used to simulate future behavior.

[1347] Behavioral simulation and prediction

[1348] The server simulates the behavior of the digital twin in response to the user's request, calculating predicted behavior and risk levels. This simulation utilizes past data, general behavioral patterns, and even the user's current emotional state to predict multiple options the user may choose.

[1349] Providing advice

[1350] Based on the simulation results, the server generates advice on the optimal options and risk levels for the user and sends this information to the device.

[1351] 2. Terminal Processing

[1352] User Data Input

[1353] The device provides an interface where users can input information about themselves and their families, and also works with an emotion engine to obtain user emotional information.

[1354] Sending data

[1355] The device executes protocol processing to transmit the input personal information and emotional information to the server. An encryption protocol is used for transmission to prevent information leakage.

[1356] Displaying Advice

[1357] The device displays the advice and risk level information sent from the server in an easy-to-read format for the user, who can then make decisions about their actions based on this information.

[1358] 3. User Actions

[1359] Entering information

[1360] Users access a dedicated personal page using their device and enter personal information, past behavior history, etc. Emotional information is also provided using an emotion engine.

[1361] Check the advice and choose your course of action

[1362] Users can review the advice provided via their device and choose the best course of action, taking into account the risk level and recommendations.

[1363] Providing Feedback

[1364] Users provide feedback on the results of their actions and their satisfaction with them, which is sent to a server and used to improve the accuracy of the digital twin's predictive model.

[1365] Specific examples

[1366] For example, if a user uses the system for health management purposes:

[1367] 1. The user uses a device to enter past health data and current lifestyle habits into their personal page. They also use the emotion engine to enter their current emotional state.

[1368] 2. The server receives this information and creates a digital twin.

[1369] 3. The server uses the generated digital twin to simulate the user's health behavior and calculate the optimal health strategy and its risk level.

[1370] 4. The server sends this information to the user's device.

[1371] 5. The device displays the health advice and risk level sent from the server.

[1372] 6. The user decides on health management actions based on the displayed advice and enters the results into the system as feedback.

[1373] Through these steps, the system predicts the user's behavior and takes their emotional state into account to help them make better decisions, allowing them to achieve their goals more safely and efficiently.

[1374] The processing flow will be explained below.

[1375] Program processing steps

[1376] 1. Server Processing

[1377] Step 1: Receiving and storing data

[1378] The server receives personal information entered by the user and emotional information provided by the emotion engine, including name, age, gender, health information, purchase history, and behavioral history.

[1379] The server processes the received data and stores it in a database in an appropriate format, including emotional information.

[1380] Step 2: Create a digital twin

[1381] The server runs a generative AI model based on the stored personal and emotional information, which combines the user's characteristics with real-time emotional data to generate a digital twin.

[1382] The server stores the generated digital twin profile and the results of the initial behavioral simulation based on it in a database.

[1383] Step 3: Behavioral simulation and prediction

[1384] Based on the user's request, the server runs a simulation of a specific behavior by the digital twin, where emotional information is used as part of the simulation to predict behavior based on the user's emotional state.

[1385] The server analyzes the simulation results and calculates behavior predictions and risk levels, and the results are prepared for delivery to the user.

[1386] Step 4: Providing advice

[1387] The server generates analysis results and advice, which includes specific action options and risk levels, and sends the data to the device.

[1388] 2. Terminal Processing

[1389] Step 1: User Data Input

[1390] The device provides an interface where users can input information about themselves and their families, and also works with an emotion engine to obtain user emotional information.

[1391] For example, the emotion engine recognizes the user's facial expressions and tone of voice and analyzes their emotions.

[1392] Step 2: Sending data

[1393] The device sends the entered personal information and acquired emotion information to the server, where the integrity of the data is verified and the data is transmitted securely using an encryption protocol.

[1394] Step 3: Receive and view advice

[1395] The device receives advice and risk level information sent from the server.

[1396] The device displays the received information in an easy-to-read format for the user, who can then make decisions about their actions based on this information.

[1397] 3. User Actions

[1398] Step 1: Enter your information

[1399] Users access their personal page using their device and enter their personal information and past behavioral history. The system also uses an emotion engine to provide information about their current emotions. For example, if a user is feeling stressed, that information is also entered into the system.

[1400] Step 2: Review the advice

[1401] Users can check the advice and risk levels displayed on their device and choose the best course of action. For example, the system may recommend low-risk options based on the user's current emotional state and past data.

[1402] Step 3: Provide feedback

[1403] Users provide feedback on their actual behavior and their satisfaction with it. This feedback is sent to the server via their device and used to improve the accuracy of the digital twin's predictive model. For example, a user can perform a recommended behavior and report the results to the system.

[1404] Specific examples

[1405] For example, if a user uses the system for health management purposes:

[1406] Step 1:

[1407] Users use their devices to input their past health data and current lifestyle habits into their personal page, and also input their current emotional state using the emotion engine.

[1408] Step 2:

[1409] The terminal transmits this information to the server.

[1410] Step 3:

[1411] The server uses this information to create a digital twin.

[1412] Step 4:

[1413] The server uses the created digital twin to simulate health behavior and calculate the optimal health strategy and its risk level.

[1414] Step 5:

[1415] The server sends the calculation results to the terminal.

[1416] Step 6:

[1417] The device displays health advice and risk levels sent from the server.

[1418] Step 7:

[1419] The user decides on health management actions based on the displayed advice and inputs the results into the system as feedback.

[1420] In this way, the system of the present invention predicts the user's behavior and takes their emotional state into account to help them make better decisions, allowing them to achieve their goals more safely and efficiently.

[1421] Example 2

[1422] 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."

[1423] In recent years, there has been an increasing need for systems that provide highly accurate behavioral predictions and advice that take into account an individual's emotional state and behavioral patterns. However, conventional systems lack an established method for integrating personal and emotional information to simulate user behavior. As a result, they have faced issues such as low prediction accuracy and failure to meet user expectations. Furthermore, they lacked a mechanism for improving the model based on feedback, making it difficult to improve user satisfaction.

[1424] 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 personal information and emotional information of a user, means for encrypting and transmitting the personal information and emotional information input by the user, means for storing the received personal information and emotional information in a database, means for generating a digital twin by a generative AI model using the stored personal information and emotional information, means for performing a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and a risk level, and means for providing the user with the behavioral prediction and risk level obtained as a result of the behavioral simulation. This makes it possible to provide highly accurate behavioral predictions and advice that take into account the individuality of the user.

[1425] "User" refers to an individual or organization that uses the system and provides personal and emotional information.

[1426] "Personal information" refers to information that can identify a specific individual, such as a user's name, address, age, and gender.

[1427] "Emotion information" is data that represents the user's current emotional state, and is information obtained using an emotion engine.

[1428] An "emotion engine" is a software or hardware system for analyzing a user's emotional state.

[1429] "Terminal" means a device that a user uses to input, send, or receive information, including a computer, smartphone, tablet, etc.

[1430] A "server" is a computer system that receives, stores, and analyzes data sent by users.

[1431] A "database" is a repository of information installed within a server, and is a system for systematically storing and managing personal and emotional information.

[1432] A "generative AI model" is an artificial intelligence algorithm or program that generates a digital twin based on input data.

[1433] A "digital twin" is a virtual model created based on a user's personal and emotional information, which mimics the user's characteristics and behavioral patterns.

[1434] "Behavioral simulation" is the process of predicting a user's future behavior using a generated digital twin.

[1435] "Behavior prediction" is predictive data about a user's future behavior obtained through behavior simulation.

[1436] The "risk level" is a numerical value or evaluation that indicates the degree of risk associated with a particular behavior, calculated based on behavior prediction.

[1437] "Advice" refers to optimal behavioral options or recommendations provided to users based on the results of behavioral simulations.

[1438] "Feedback" is information provided by users about the results of their actual actions and their satisfaction, and is used to improve the accuracy of the system's model.

[1439] "My Private Page" is a web page or application interface where users can input and manage personal information, emotional information, purchase history, behavioral history, family information, etc.

[1440] The present invention relates to a system that generates a digital twin based on a user's personal information and emotional information, and provides predictions and advice through behavioral simulations using the digital twin. The following describes specific embodiments of the invention.

[1441] System Configuration

[1442] This system consists of four elements: a server, a terminal, a user, and an emotion engine. Each of these elements functions as follows:

[1443] Server Roles

[1444] The server receives, stores, and analyzes the personal information and emotional information sent by the user. First, the data sent from the device is securely transmitted using an encryption protocol (e.g., HTTPS). The server then stores this data in a database.

[1445] The server then runs a generative AI model to generate a digital twin of the user, which analyzes the stored personal and emotional information to create a virtual model that mimics the user's characteristics and behavioral patterns.

[1446] Based on the generated digital twin, the server runs a behavioral simulation that utilizes past data, typical behavioral patterns, and the user's current emotional state to calculate behavioral predictions and risk levels for multiple options the user may choose.

[1447] Finally, the server generates advice based on the simulation results and sends this information to the device. The advice provided to the user includes predicted behavior and risk levels.

[1448] Device Role

[1449] The terminal is a device that allows users to input, send, and receive information. Users access their personal page and enter their personal information, past behavioral history, and emotional state. The terminal works in conjunction with the emotion engine to obtain the user's emotional information.

[1450] The device sends the entered data to a server using an encryption protocol, and the server then sends advice and risk level information that is displayed to the user in an easy-to-read format.

[1451] User Roles

[1452] Users access their personal page and enter their personal information, past behavioral history, and emotional state. This information is often obtained using an emotion engine. The user checks the advice provided by the server and selects the most appropriate action. They also provide feedback on the results of their actual actions and their satisfaction with them. This feedback is sent to the server and used to improve the accuracy of the digital twin's prediction model.

[1453] Specific examples

[1454] For example, the flow when a user uses the system for health management purposes will be explained.

[1455] 1. The user uses a device to enter past health data and current lifestyle habits into their personal page, and also inputs their current emotional state using the emotion engine.

[1456] 2. The server receives this information and creates a digital twin.

[1457] 3. The server uses the generated digital twin to simulate the user's health behavior and calculate the optimal health strategy and its risk level.

[1458] 4. The server sends this information to the user's device.

[1459] 5. The device displays the health advice and risk level sent from the server.

[1460] 6. The user decides on health management actions based on the displayed advice and enters the results into the system as feedback.

[1461] Prompt Sentence Examples

[1462] What specific data items do users enter for health management?

[1463] What predictive models does the server use to simulate behavior?

[1464] What exactly is the process for providing feedback?

[1465] Through these steps, the system predicts the user's behavior and takes their emotional state into account to help them make better decisions, allowing them to achieve their goals more safely and efficiently.

[1466] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1467] Step 1: Entering User Data

[1468] Users access their personal page using their own devices and enter a wide range of data, including personal information, past behavioral history, health data, and emotional state, such as name, age, weight, food records, and exercise volume.

[1469] Input: Personal information, past behavioral history, health data, emotional state

[1470] Output: Encrypted personal and emotional information

[1471] Specific behavior:

[1472] Users can open their personal page using a browser or mobile app.

[1473] Enter the required information using the text boxes and drop-down menus.

[1474] Activate the emotion engine and measure your emotional state.

[1475] Step 2: Sending data

[1476] The device encrypts the input data before sending it to the server. By using an encryption protocol (e.g. HTTPS), information leakage is prevented.

[1477] Input: Personal and emotional information entered by the user

[1478] Output: Data encrypted and sent to the server

[1479] Specific behavior:

[1480] The terminal encrypts the user's input data.

[1481] The encrypted data is sent to the server.

[1482] Step 3: Receiving and storing data

[1483] The server receives the data sent from the device and stores it in a database. It checks the integrity of the received data and stores it appropriately.

[1484] Input: Data sent encrypted from the device

[1485] Output: Personal and emotional information stored in a database

[1486] Specific behavior:

[1487] The server decrypts the received data and checks its integrity.

[1488] Store data appropriately in the database.

[1489] Step 4: Create a digital twin

[1490] The server then uses the stored personal and emotional information to run a generative AI model to create a digital twin, which virtually replicates the user's characteristics and behavioral patterns.

[1491] Input: Personal and emotional information stored in a database

[1492] Output: Generated digital twin

[1493] Specific behavior:

[1494] The server provides the data needed for the generative AI model.

[1495] The generative AI model generates a digital twin and returns the data to the server.

[1496] Step 5: Running a behavioral simulation

[1497] The server uses the created digital twin to simulate behavior under various conditions, taking into account past data, general behavioral patterns, and the user's current emotional state to make predictions.

[1498] Input: Generated digital twin, historical data, general behavioral patterns, current emotional state

[1499] Output: Behavioral prediction and risk level

[1500] Specific behavior:

[1501] The server runs the simulation engine and runs multiple scenarios.

[1502] Prediction data and risk levels are calculated from the results of each scenario.

[1503] Step 6: Generating and serving advice

[1504] The server generates advice for the user based on the results of the behavioral simulation, and the advice is sent to the terminal and provided to the user.

[1505] Input: Simulation results, predicted behavior and risk level

[1506] Output: Advice given to the user

[1507] Specific behavior:

[1508] The server creates customized advice based on the generated prediction data.

[1509] The created advice is sent to the device.

[1510] Step 7: User reviews advice and chooses action

[1511] The user uses the device to review the received advice, which includes action options and their risk levels. The user can then choose the best course of action based on the advice.

[1512] Input: Advice sent by the server

[1513] Output: User's action choice

[1514] Specific behavior:

[1515] The terminal displays the advice from the server.

[1516] Users decide what to do based on their situation and goals.

[1517] Step 8: Provide feedback

[1518] Users provide feedback on the results of their actions and their satisfaction with them, which is sent to the server and used to improve the accuracy of the model.

[1519] Input: User behavior results and satisfaction

[1520] Output: Feedback sent to the server

[1521] Specific behavior:

[1522] The user enters the results of their actual actions into the terminal.

[1523] The terminal transmits the feedback data to the server.

[1524] The server analyzes the received feedback and adjusts the parameters of the model.

[1525] (Application example 2)

[1526] 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."

[1527] While conventional systems were able to predict behavior and provide advice based on a user's personal and emotional information, they were unable to provide useful advice in real time in actual purchasing situations. When users are physically in a store, it is difficult to receive optimal advice or coupon information when selecting products, and there was a need to improve the purchasing experience.

[1528] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting a user's personal information and emotional state, means for receiving and storing the input personal information and emotional information, means for generating a digital twin based on the received personal information and emotional information, means for running a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and risk level, means for providing the calculation results to the user, and means for displaying purchasing advice in real time when the user is selecting a product in a physical store. This allows the user to receive optimal advice and coupon information in real time even in a physical store, improving the purchasing experience.

[1529] A "user" is an individual who uses this system and provides personal and emotional information.

[1530] "Personal information" refers to all data related to a user, such as basic information, purchasing history, behavioral history, and lifestyle habits.

[1531] "Emotional state" is information that indicates the user's current mood and psychological state.

[1532] A "digital twin" is a virtual model that mimics a user's characteristics and behavioral patterns, created based on the user's personal and emotional information.

[1533] "Behavioral simulation" is the process of using a generated digital twin to predict a user's behavioral patterns and calculate their behavioral options and risk levels.

[1534] The "risk level" indicates the degree of risk that a particular behavior may cause, as determined by the behavioral simulation.

[1535] "Means for displaying purchasing advice in real time" refers to a function that instantly displays optimal purchasing advice via a device such as a smartphone when a user is selecting a product in a physical store.

[1536] "Feedback" is information that provides the system with the results of a user's actual actions and their satisfaction.

[1537] Overall system overview

[1538] This invention is a system that generates a digital twin based on a user's personal information and emotional state, and provides predictions and advice through behavioral simulation using the digital twin. The main elements of this system include a server, a terminal, a user, and an emotion engine.

[1539] Server Roles

[1540] The server performs the following process.

[1541] 1. Data Receipt and Storage

[1542] The server receives personal and emotional information from users and stores it in a database, which is used to generate digital twins and simulate their behavior.

[1543] 2. Creating a digital twin

[1544] The server runs a generative AI model based on the stored personal and emotional information to create a digital twin of the user, a virtual model that mimics the user's characteristics, behavioral patterns, and emotional state.

[1545] 3. Behavioral Simulation and Prediction

[1546] The server uses a digital twin to simulate the user's behavior and calculates predicted behavior and risk levels, using historical data, general behavioral patterns, and current emotional state.

[1547] 4. Real-time purchasing advice

[1548] When users are selecting products in physical stores, the system provides optimal purchasing advice in real time, allowing users to make more effective in-store purchases.

[1549] Device Role

[1550] The terminal performs the following processing.

[1551] 1. User data input

[1552] The device provides an interface for users to input their personal and emotional information.

[1553] 2. Data transmission

[1554] The device sends the entered personal and emotional information to a server, using an encryption protocol to prevent information leakage.

[1555] 3. Displaying Advice

[1556] The device displays advice and risk level information sent from the server to the user, allowing the user to choose their course of action based on this information.

[1557] User Roles

[1558] The user performs the following process:

[1559] 1. Enter your information

[1560] Users use their devices to access their dedicated personal page and enter personal and emotional information.

[1561] 2. Check the advice and choose your course of action

[1562] Users can review the advice displayed on their device and choose the best course of action, taking into account risk levels and recommendations.

[1563] 3. Providing Feedback

[1564] After taking an action, users provide feedback about the results and their satisfaction, which is sent to the server and used to improve the accuracy of the digital twin's predictive model.

[1565] Hardware and software used

[1566] The following hardware and software are used to implement this system:

[1567] Server: The backend is built using Python and Flask, and Pandas, scikit-learn, and TensorFlow are used for data analysis.

[1568] Database: Use SQLite or Firebase for data storage.

[1569] Device: Develop a smartphone app that runs on iOS / Android and provide an interface to users.

[1570] Encryption Protocols: We use encryption technologies such as SSL / TLS to ensure data security.

[1571] Specific prompt examples

[1572] For example, the following prompt might be generated for a user looking to purchase a health drink:

[1573] User ID: 12345, Personal information: { Purchase history: ["health drinks", "supplements"]}, Emotion information: { Mood: "stressful"}

[1574] Generated advice: "I recommend a drink that's good for relieving stress. You can use this coupon."

[1575] Based on this prompt, users can select the most suitable product in the store, providing a better shopping experience.

[1576] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1577] Step 1:

[1578] The user uses the device to input personal and emotional information. The input information is retrieved through the device interface. This information includes the user's purchasing history, lifestyle habits, and current mood. The input data is converted into JSON format and sent to the server using an encryption protocol (e.g., SSL / TLS).

[1579] Step 2:

[1580] The server receives the personal information and emotional information sent from the device and stores it in a database. The received data is filtered and parsed to remove outliers and invalid data. The data is then stored in a database (e.g., SQLite or Firebase) and used as the basis for subsequent digital twin generation and simulation processing.

[1581] Step 3:

[1582] The server runs a generative AI model based on the stored personal and emotional information to generate a digital twin of the user. The generative AI model uses a machine learning framework (e.g., TensorFlow) to analyze the input data and create a virtual model that mimics the user's characteristics and behavioral patterns. The generated digital twin is then stored in a database.

[1583] Step 4:

[1584] The server then runs a behavioral simulation using the created digital twin. The behavioral simulation takes into account statistical data, general behavioral patterns, and the user's current emotional state to predict the user's purchasing behavior. The simulation results also calculate the risk level for multiple choices. This data is then saved in JSON format.

[1585] Step 5:

[1586] When a user selects a product in a physical store, the smartphone device uses the camera and GPS to identify the product and acquire its location. The identified product information and store information are immediately sent to the server. The transmitted data includes the product ID and location information.

[1587] Step 6:

[1588] The server generates optimal purchasing advice in real time based on the received product information and location information. Using a generative AI model, it compares past data and simulation results to determine the advice and coupon information that should be provided to the user. The generated advice is then sent to the device.

[1589] Step 7:

[1590] The device instantly displays the advice and coupon information sent from the server to the user on the smartphone's display, allowing the user to choose the best purchasing behavior based on this information.

[1591] Step 8:

[1592] Users provide feedback on the results of their purchases and their satisfaction. Feedback information is entered through the device interface and sent to the server. The submitted feedback is received and stored on the server, and used to improve the accuracy of the digital twin.

[1593] 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.

[1594] 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.

[1595] 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.

[1596] [Fourth embodiment]

[1597] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1598] 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.

[1599] 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).

[1600] 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.

[1601] 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.

[1602] 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).

[1603] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1604] 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.

[1605] 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.

[1606] 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.

[1607] 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.

[1608] 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.

[1609] 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."

[1610] The present invention relates to a system that creates a digital twin based on personal information provided by a user and provides predictions and advice through behavioral simulations using the digital twin. The specific configuration of the system and program processing are described below.

[1611] System Overview

[1612] The system consists of three elements: a server, a device, and the user. The user enters personal information via the device, and the server receives, stores, and analyzes that information to generate a digital twin. The generated digital twin then performs a behavioral simulation, and the resulting behavioral predictions and advice are provided to the user via the device.

[1613] 1. Server Processing

[1614] Data reception and storage

[1615] The server receives personal information (such as name, age, gender, health information, purchase history, and behavioral history) entered by the user and stores it in a database. This data is used as the basis for generating digital twins and simulating their behavior.

[1616] Creating a digital twin

[1617] The server runs a generative AI model based on the stored personal information to generate a digital twin of the user. The generated digital twin is a virtual model that mimics the user's characteristics and behavioral patterns. This model is used to simulate future behavior.

[1618] Behavioral simulation and prediction

[1619] The server simulates the behavior of the digital twin in response to the user's request, and calculates predicted behavior and risk levels. This simulation utilizes past data and general behavioral patterns to predict multiple options the user may choose.

[1620] Providing advice

[1621] Based on the simulation results, the server generates advice on the optimal options and risk levels for the user and sends this information to the device.

[1622] 2. Terminal Processing

[1623] User Data Input

[1624] The device provides an interface where users can enter information about themselves and their family members, and the data they enter is then sent to a server.

[1625] Sending data

[1626] The device checks the integrity of the personal information entered and then transmits it securely to the server using an encryption protocol to prevent information leaks.

[1627] Displaying Advice

[1628] The device displays the advice and risk level information sent from the server in an easy-to-read format for the user, who can then make decisions about their actions based on this information.

[1629] 3. User Actions

[1630] Entering information

[1631] Users access a dedicated personal page on their device and enter their personal information, past behavioral history, etc. This improves the accuracy of the digital twin and enables more reliable predictions.

[1632] Check the advice and choose your course of action

[1633] Users can review the advice provided via their device and choose the best course of action, taking into account the risk level and recommendations.

[1634] Providing Feedback

[1635] Users provide feedback on the results of their actions and their satisfaction with them, which is sent to a server and used to improve the accuracy of the digital twin's predictive model.

[1636] Specific examples

[1637] For example, if a user is considering investing in stocks:

[1638] 1. The user enters their past investment history and current asset status via the terminal.

[1639] 2. The server receives this information and creates a digital twin.

[1640] 3. The server uses the generated digital twin to simulate the user's investments and calculates the optimal investment options and their risk levels.

[1641] 4. The server provides this information to the user, who then confirms it via their device.

[1642] 5. The user decides on investment actions based on the advice provided and enters the results into the system as feedback.

[1643] In this way, the system according to the present invention assists in predicting user behavior and managing the risks involved, enabling safer and more efficient decision-making.

[1644] The processing flow will be explained below.

[1645] Program processing steps

[1646] 1. Server Processing Steps

[1647] Step 1: Receiving and storing data

[1648] The server receives personal information entered by the user, including name, age, gender, health information, purchase history, and behavioral history.

[1649] The server performs initial processing on the received data and stores it in a database in an appropriate format.

[1650] Step 2: Create a digital twin

[1651] The server runs a generative AI model based on the stored personal information.

[1652] The server stores the generated digital twin profile in a database.

[1653] Step 3: Behavioral simulation and prediction

[1654] The server performs a simulation of a specific behavior by the digital twin based on the user's request.

[1655] The server analyzes the simulation results and calculates behavior predictions and risk levels.

[1656] Step 4: Providing advice

[1657] The server generates optimal options and risk advice based on the analysis results and sends this information to the terminal.

[1658] 2. Terminal Processing Steps

[1659] Step 1: User Data Input

[1660] The device provides users with a dedicated personal page and displays an interface for entering personal information, family composition, purchasing history, behavioral history, etc.

[1661] Step 2: Sending data

[1662] The terminal executes protocol processing to transmit the input data to the server.

[1663] The terminal checks the integrity of the transmitted data and sends it to the server using an encryption protocol.

[1664] Step 3: Receive and view advice

[1665] The device receives advice and risk level information sent from the server.

[1666] The terminal displays the received information in a user-friendly format.

[1667] 3. User Processing Steps

[1668] Step 1: Enter your information

[1669] Users access their dedicated personal page via their terminal and enter personal information, past behavioral history, purchasing history, etc.

[1670] Step 2: Review the advice

[1671] The user checks the advice and risk level displayed on the device and chooses the most appropriate course of action.

[1672] Step 3: Provide feedback

[1673] The user provides feedback about the actual results of the action and the level of satisfaction to the server through the terminal.

[1674] Users contribute to improving the accuracy of the predictive model through feedback.

[1675] Specific examples

[1676] For example, if a user is considering investing in stocks:

[1677] Step 1:

[1678] The user enters past investment history and current asset status into their personal page via a terminal.

[1679] Step 2:

[1680] The terminal transmits this information to the server.

[1681] Step 3:

[1682] The server uses this information to create a digital twin.

[1683] Step 4:

[1684] The server uses the created digital twin to run a stock investment simulation and calculate the optimal investment options and their risk levels.

[1685] Step 5:

[1686] The server sends the calculation results to the terminal.

[1687] Step 6:

[1688] The terminal displays the investment advice and risk level sent from the server.

[1689] Step 7:

[1690] The user makes investment decisions based on the displayed advice.

[1691] Step 8:

[1692] Users provide feedback on their investment results and satisfaction via their terminals.

[1693] Through this series of steps, the system according to the present invention predicts user behavior and supports better decision-making.

[1694] Example 1

[1695] 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."

[1696] Conventional simulation systems have had limitations in accuracy and practicality when it comes to predicting user behavior and managing risks. Issues also exist regarding the handling of personal information and data security. Furthermore, they lack a mechanism for continuously improving the accuracy of the system through feedback, limiting the credibility and effectiveness of the advice users receive. The purpose of this invention is to solve these issues and provide users with more accurate predictions and useful advice.

[1697] 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.

[1698] In this invention, the server includes: means for inputting a user's personal information; means for receiving and storing the input personal information; means for generating a digital twin using a generative AI model based on the received personal information; means for generating prompts for generating the digital twin; means for performing a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and risk level; and means for providing the calculation results to the user. This enables a highly accurate digital twin to be generated based on the user's personal information, enabling reliable behavioral simulation and prediction. Furthermore, the accuracy of the system can be improved by utilizing continuous feedback, enabling more accurate advice to be provided to the user.

[1699] "User personal information" refers to individual data such as name, age, gender, health information, purchase history, and behavioral history that a user provides to the system.

[1700] A "generative AI model" is an artificial intelligence model that generates a digital twin based on a user's personal information.

[1701] A "digital twin" is a virtually generated model that mimics a user's characteristics and behavioral patterns.

[1702] A "prompt" is an instruction provided to a generative AI model to generate a digital twin.

[1703] "Behavioral simulation" is a virtual experiment that uses a generated digital twin to predict future user behavior.

[1704] "Behavioral prediction" is a prediction of a user's future behavior obtained through behavioral simulation.

[1705] The "risk level" is the degree of risk of the actions that the user should take, calculated based on the simulation results.

[1706] "Feedback" is information provided by users about the results of their actual actions and their satisfaction, and is used to improve the accuracy of the system.

[1707] The present invention relates to a system that creates a digital twin based on personal information provided by a user and provides predictions and advice through behavioral simulations using the digital twin. The specific configuration of the system and program processing are described below.

[1708] System Overview

[1709] The system consists of three elements: a server, a device, and the user. The user enters personal information via the device, and the server receives, stores, and analyzes that information to generate a digital twin. The generated digital twin then performs a behavioral simulation, and the resulting behavioral predictions and advice are provided to the user via the device.

[1710] Server Processing

[1711] Data reception and storage

[1712] The server receives personal information entered by the user (such as name, age, gender, health information, purchase history, and behavioral history) and stores it in a database. This data is used as the basis for generating digital twins and simulating their behavior. Suitable databases include MySQL and PostgreSQL.

[1713] Creating a digital twin

[1714] The server runs a generative AI model (e.g., OpenAI's GPT-4) based on the stored personal information to generate a digital twin for the user. Specifically, the generative AI model is run using the prompt, "Generate a digital twin based on the user's name, age, gender, and health information." The generated digital twin is a virtual model that mimics the user's characteristics and behavioral patterns. The generated digital twin is then stored back in the database.

[1715] Behavioral simulation and prediction

[1716] The server simulates the behavior of the digital twin in response to the user's request and calculates predicted behavior and risk levels. This simulation utilizes past data and general behavioral patterns. For example, a simulation can be performed using the prompt, "Predict the investment risk level for the next year using the target user's digital twin."

[1717] Providing advice

[1718] Based on the simulation results, the server generates advice on the user's optimal options and risk level and sends that information to the device, providing specific advice such as "regular exercise and a balanced diet will reduce risk by 30%."

[1719] Terminal handling

[1720] User Data Input

[1721] The device provides an interface where users can enter information about themselves and their family members, and the data they enter is then sent to a server.

[1722] Sending data

[1723] The device checks the integrity of the personal information entered and then securely transmits it to the server using encryption protocols such as SSL / TLS to prevent information leaks.

[1724] Displaying Advice

[1725] The terminal displays the advice and risk level information sent from the server in an easy-to-read format for the user, such as a dashboard displaying information like "Best investment option: Stock A, Estimated risk level: Low."

[1726] User Action

[1727] Entering information

[1728] Users access a dedicated personal page on their device and enter personal information, past activity history, etc. For example, by entering "past exercise habits: 30 minutes of jogging three times a week," the accuracy of the digital twin improves, enabling more reliable predictions.

[1729] Check the advice and choose your course of action

[1730] Users can review the advice provided via their device and choose the most appropriate course of action. They can make decisions based on their risk level and recommendations. For example, they can select "Perform recommended exercise habits" and actually start exercising.

[1731] Providing Feedback

[1732] Users provide feedback on the results of their actual actions and their satisfaction with them, for example, "After exercising for a month, my health condition has improved," and the server receives this feedback and uses it to improve the accuracy of the digital twin's prediction model.

[1733] Specific examples

[1734] For example, if a user is considering investing in stocks:

[1735] 1. The user enters their past investment history and current asset status via the terminal (e.g., "Investment history: past 3 years, current assets: 10 million yen").

[1736] 2. The server receives this information and generates a digital twin (e.g., prompt: "Generate a digital twin based on the user's investment history and current asset status").

[1737] 3. The server uses the generated digital twin to simulate the user's investment and calculates the optimal investment option and its risk level (e.g., prompt: "Predict the target user's investment risk level for the next year using the target user's digital twin").

[1738] 4. The server provides this information to the user, who then confirms it via their device.

[1739] 5. The user decides on investment actions based on the advice provided and enters the results as feedback into the system (e.g., feedback content: "Investment performance: Stock A, Result: Growth rate 10%)."

[1740] In this way, the system according to the present invention assists in predicting user behavior and managing the risks involved, enabling safer and more efficient decision-making.

[1741] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1742] Step 1: Data entry

[1743] explanation:

[1744] Users enter personal information (e.g., name, age, gender, health information, purchase history, behavioral history, etc.) through the device.

[1745] Specific operation: The user fills in the information in the dedicated input form and presses the submit button.

[1746] Input: Personal information such as name, age, gender, and health information

[1747] Output: Well-formatted personal data

[1748] Step 2: Receiving and storing data

[1749] explanation:

[1750] The server receives the personal information sent from the terminal and checks the consistency of the data.

[1751] What happens: The server validates the received data, checking for formatting errors and incompleteness, and then stores it in a database (e.g., MySQL, PostgreSQL).

[1752] Input: Well-formatted personal data

[1753] Output: Personal information stored in a database

[1754] Step 3: Generate a prompt statement

[1755] explanation:

[1756] Based on the stored personal information, the server generates a prompt sentence to be passed to a generative AI model (e.g., OpenAI's GPT-4).

[1757] What it does: The server uses a script to generate a document such as "Create a digital twin based on the user's name, age, gender, and health information."

[1758] Input: Personal information data

[1759] Output: Prompt sentence for the generative AI model

[1760] Step 4: Create a digital twin

[1761] explanation:

[1762] The server inputs the generated prompt sentences into a generative AI model to generate a digital twin.

[1763] How it works: The server inputs a prompt statement to launch a generative AI model (e.g., GPT-4) and generates a digital twin of the user.

[1764] Input: prompt statement

[1765] Output: Data of the generated digital twin

[1766] Step 5: Behavioral simulation

[1767] explanation:

[1768] The server runs a behavioral simulation based on the generated digital twin and calculates behavioral predictions and risk levels.

[1769] Specific operation: Based on the digital twin, the server runs a simulation algorithm and generates predictive data for various scenarios (e.g., health risks, investment risks).

[1770] Input: Digital twin data

[1771] Output: Behavioral simulation results and risk level

[1772] Step 6: Generating and serving advice

[1773] explanation:

[1774] Based on the simulation results, the server generates advice for the user on the optimal behavioral options and their risk levels.

[1775] Specific operation: The server analyzes the simulation data, documents the optimal options and risk levels, and sends them to the terminal.

[1776] Input: Behavioral simulation results and risk level

[1777] Output: Advice document

[1778] Step 7: Viewing Advice

[1779] explanation:

[1780] The terminal displays the advice received from the server in an easy-to-read format for the user.

[1781] What it does: The device uses a UI such as a dashboard to visually display advice and risk levels.

[1782] Input: Advice document

[1783] Output: Advice information displayed on the screen

[1784] Step 8: Choose and take action

[1785] explanation:

[1786] The user reviews the advice provided via the device and selects and executes the most appropriate action.

[1787] Specific Action: The user reviews the advice and takes the suggested action (e.g., manage their health or invest).

[1788] Input: On-screen advice information

[1789] Output: User's choice

[1790] Step 9: Provide feedback

[1791] explanation:

[1792] Users provide feedback on their actual behavior and satisfaction.

[1793] Specific operation: The user enters the results of their actions and their evaluation in a dedicated feedback form and submits it.

[1794] Input: User action results and ratings

[1795] Output: Feedback data

[1796] Step 10: Receive feedback and improve the model

[1797] explanation:

[1798] The server receives the feedback data and uses it to improve the accuracy of the digital twin and refine the model.

[1799] Specific operation: The server analyzes the received feedback data and uses it as training data for the generative AI model.

[1800] Input: Feedback data

[1801] Output: Improved model data

[1802] (Application example 1)

[1803] 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."

[1804] Conventional food delivery systems are unable to provide optimal meal recommendations based on a user's eating habits, health status, or past order history, resulting in issues with user satisfaction and health management. Furthermore, delivery time predictions can be inaccurate, reducing user convenience. The present invention aims to solve these problems and provide users with more personalized meal recommendations and accurate delivery time predictions.

[1805] 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.

[1806] In this invention, the server includes means for inputting personal information of a user, means for receiving and storing the input personal information, means for generating a digital twin based on the received personal information, means for running a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and risk level, means for providing the calculation results to the user, means for inputting data on the user's eating habits and preferences, and means for making meal suggestions and predicting delivery times. This enables personalized meal suggestions and accurate predictions of delivery times based on the user's past eating history and health information.

[1807] "Means for inputting user information" refers to a device or software that provides an interface for users to input information about themselves.

[1808] "Means for receiving and storing information" refers to a device or software that has the function of receiving information entered by a user to a server and storing it in a database.

[1809] "Means for generating a digital twin" refers to algorithms and software for generating a virtual model based on user input.

[1810] "Means for performing behavioral simulation" refers to a device that provides algorithms and software for virtually predicting a user's behavior and choices using the generated digital twin.

[1811] "Means for predicting behavior and calculating risk levels" refers to a device or software that numerically analyzes and calculates a user's future behavior and the associated risks based on the results of a behavioral simulation.

[1812] "Means for providing calculation results" refers to a device or software with an interface for presenting the results of the simulation and risk calculation to a user.

[1813] "Means for inputting data relating to a user's dietary habits and preferences" means a device or software that provides an interface for a user to input information about their dietary habits and preferences.

[1814] "Means for meal suggestions and delivery time prediction" refers to algorithms and software that suggest optimal meal menus and predict accurate delivery times based on user input and digital twin information.

[1815] The present invention relates to a system for providing optimal meal suggestions and delivery time predictions based on a user's eating habits and preferences in a food delivery service. The specific configuration and operation of the system are described below.

[1816] 1. System Configuration

[1817] The system mainly comprises a server, a user terminal, and a generative AI model.

[1818] 1. Server:

[1819] Data Receipt and Storage:

[1820] The server receives personal information (such as name, age, gender, health information, dietary preferences, and past order history) sent from the user's device and stores it in a database. The software used is Python and Flask, and the database is managed by MySQL.

[1821] Digital Twin Generation:

[1822] Based on the stored data, a generative AI model (e.g., GPT-4) is used to generate a digital twin of the user, a virtual model of the user that mimics the user's characteristics and past dietary history.

[1823] Behavioral Simulation and Prediction:

[1824] The resulting digital twin is then used to simulate the user's next likely meal order and optimal delivery time, which then provides meal suggestions and calculates a risk level.

[1825] Advice provided:

[1826] Based on the simulation results, optimal meal suggestions and delivery time predictions are generated and sent to the terminal.

[1827] 2. User Device:

[1828] Information input interface:

[1829] It provides an interface where users can enter their personal information, food preferences, and past ordering history, which is then sent to a server.

[1830] Advice display:

[1831] The server sends meal suggestions and delivery time advice to the user in an easy-to-read format, which the user can use to make ordering decisions.

[1832] 3. Generative AI Model:

[1833] Digital twin generation and behavioral simulation:

[1834] Using OpenAI's GPT-4 model, a digital twin is generated based on the user's input data, and behavioral simulations are performed using that twin.

[1835] 2. Specific Hardware and Software Used

[1836] Server: High-performance server equipment, Python, Flask, MySQL, generative AI models (e.g., GPT-4)

[1837] User device: smartphone or tablet

[1838] Database: MySQL

[1839] Communication protocol: HTTPS

[1840] 3. Program processing explanation

[1841] The server first receives personal information entered by the user and stores it in a MySQL database. It then uses the data to generate a digital twin using a generative AI model (GPT-4) and performs a behavioral simulation. From the simulation results, it calculates optimal meal recommendations and predicted delivery times, and sends them to the user's device. The user's device then displays the received advice and assists the user in their ordering process.

[1842] 4. Specific Examples

[1843] For example, if a user has frequently ordered "salads" or "grilled chicken" in the past, a digital twin will be created based on those preferences and suggest a "salad chicken wrap" as the best menu item for the next order. Additionally, based on past delivery history, the system will predict the quickest delivery time for the user's preferred time slot.

[1844] 5. Examples of prompts

[1845] Here are some example prompts to input to the generative AI model:

[1846] Generate a digital twin for the following user data: name: "Username", age: "Age", gender: "Gender", health_info: "Health Information", dietary_restrictions: "Restricted Foods", meal_history: "Past Meal History"

[1847] Based on this digital twin, predict the next best meal for them.

[1848] As described above, the present invention provides a system that provides personalized meal suggestions and accurate delivery time predictions based on a user's eating habits and preferences.

[1849] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1850] Step 1:

[1851] The server receives the user's personal information (name, age, gender, health information, dietary preferences, past order history, etc.) sent from the device and stores it in a database. Specifically, it uses Flask to receive JSON-formatted data sent from the device and inserts that data into a MySQL database. The user's personal information is input, and the process of saving it to the database is completed as output.

[1852] Step 2:

[1853] The server generates a digital twin using a generative AI model (e.g., GPT-4) based on the stored personal information data. Specifically, a Python script is used to retrieve the necessary data from the database and generate a prompt to input it into the generative AI model. A digital twin is generated based on this prompt, and the generation result is returned to the server in JSON format. The user information in the database is used as input, and the generated digital twin model is obtained as output.

[1854] Step 3:

[1855] The server then uses the generated digital twin to perform a behavioral simulation. Specifically, the digital twin's data is re-input into the generative AI model, which simulates the next meal the user is likely to order and the optimal delivery time. The results are returned in JSON format as the simulation results. The digital twin is the input, and the simulation results are the output.

[1856] Step 4:

[1857] The server then makes meal suggestions and delivery time predictions based on the simulation results and provides them to the user. Specifically, it converts the simulation results into a format that is easy for humans to understand (text or graphics) and sends it back to the terminal. The simulation results are input, and advice information for the user is generated as output.

[1858] Step 5:

[1859] The user uses the device to check the displayed meal suggestions and delivery time estimates and decides on an order. Specifically, the user refers to the advice presented on the smartphone application and selects their next meal. The determined order details are then sent to the server. The input is the meal suggestions and delivery time estimates, and the order details are generated as the output.

[1860] Step 6:

[1861] Users provide feedback on the results of their actual orders and the service to the server via their devices. Specifically, after completing an order, information is entered through a satisfaction survey or feedback form, which is then sent to the server. This feedback is used to improve the accuracy of subsequent behavioral simulations and digital twins. The feedback information is the input, and the output is saved in a database.

[1862] 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.

[1863] The present invention relates to a system that generates a digital twin based on a user's personal information and emotional state, and provides predictions and advice through behavioral simulations using the digital twin. The specific configuration of the system and program processing are described below.

[1864] System Overview

[1865] This system consists of four elements: a server, a device, a user, and an emotion engine. The user provides personal and emotional information via the device, and the server receives, stores, and analyzes that information to generate a digital twin. The generated digital twin then performs a behavioral simulation, and the resulting behavioral predictions and advice are provided to the user via the device.

[1866] 1. Server Processing

[1867] Data reception and storage

[1868] The server receives personal information entered by the user and emotional information provided by the emotion engine, and stores it in a database. This data is used as the basis for generating digital twins and simulating their behavior.

[1869] Creating a digital twin

[1870] The server runs a generative AI model based on the stored personal and emotional information to generate a digital twin of the user. The generated digital twin is a virtual model that mimics the user's characteristics, behavioral patterns, and even emotional state. This model is used to simulate future behavior.

[1871] Behavioral simulation and prediction

[1872] The server simulates the behavior of the digital twin in response to the user's request, calculating predicted behavior and risk levels. This simulation utilizes past data, general behavioral patterns, and even the user's current emotional state to predict multiple options the user may choose.

[1873] Providing advice

[1874] Based on the simulation results, the server generates advice on the optimal options and risk levels for the user and sends this information to the device.

[1875] 2. Terminal Processing

[1876] User Data Input

[1877] The device provides an interface where users can input information about themselves and their families, and also works with an emotion engine to obtain user emotional information.

[1878] Sending data

[1879] The device executes protocol processing to transmit the input personal information and emotional information to the server. An encryption protocol is used for transmission to prevent information leakage.

[1880] Displaying Advice

[1881] The device displays the advice and risk level information sent from the server in an easy-to-read format for the user, who can then make decisions about their actions based on this information.

[1882] 3. User Actions

[1883] Entering information

[1884] Users access a dedicated personal page using their device and enter personal information, past behavior history, etc. Emotional information is also provided using an emotion engine.

[1885] Check the advice and choose your course of action

[1886] Users can review the advice provided via their device and choose the best course of action, taking into account the risk level and recommendations.

[1887] Providing Feedback

[1888] Users provide feedback on the results of their actions and their satisfaction with them, which is sent to a server and used to improve the accuracy of the digital twin's predictive model.

[1889] Specific examples

[1890] For example, if a user uses the system for health management purposes:

[1891] 1. The user uses a device to enter past health data and current lifestyle habits into their personal page. They also use the emotion engine to enter their current emotional state.

[1892] 2. The server receives this information and creates a digital twin.

[1893] 3. The server uses the generated digital twin to simulate the user's health behavior and calculate the optimal health strategy and its risk level.

[1894] 4. The server sends this information to the user's device.

[1895] 5. The device displays the health advice and risk level sent from the server.

[1896] 6. The user decides on health management actions based on the displayed advice and enters the results into the system as feedback.

[1897] Through these steps, the system predicts the user's behavior and takes their emotional state into account to help them make better decisions, allowing them to achieve their goals more safely and efficiently.

[1898] The processing flow will be explained below.

[1899] Program processing steps

[1900] 1. Server Processing

[1901] Step 1: Receiving and storing data

[1902] The server receives personal information entered by the user and emotional information provided by the emotion engine, including name, age, gender, health information, purchase history, and behavioral history.

[1903] The server processes the received data and stores it in a database in an appropriate format, including emotional information.

[1904] Step 2: Create a digital twin

[1905] The server runs a generative AI model based on the stored personal and emotional information, which combines the user's characteristics with real-time emotional data to generate a digital twin.

[1906] The server stores the generated digital twin profile and the results of the initial behavioral simulation based on it in a database.

[1907] Step 3: Behavioral simulation and prediction

[1908] Based on the user's request, the server runs a simulation of a specific behavior by the digital twin, where emotional information is used as part of the simulation to predict behavior based on the user's emotional state.

[1909] The server analyzes the simulation results and calculates behavior predictions and risk levels, and the results are prepared for delivery to the user.

[1910] Step 4: Providing advice

[1911] The server generates analysis results and advice, which includes specific action options and risk levels, and sends the data to the device.

[1912] 2. Terminal Processing

[1913] Step 1: User Data Input

[1914] The device provides an interface where users can input information about themselves and their families, and also works with an emotion engine to obtain user emotional information.

[1915] For example, the emotion engine recognizes the user's facial expressions and tone of voice and analyzes their emotions.

[1916] Step 2: Sending data

[1917] The device sends the entered personal information and acquired emotion information to the server, where the integrity of the data is verified and the data is transmitted securely using an encryption protocol.

[1918] Step 3: Receive and view advice

[1919] The device receives advice and risk level information sent from the server.

[1920] The device displays the received information in an easy-to-read format for the user, who can then make decisions about their actions based on this information.

[1921] 3. User Actions

[1922] Step 1: Enter your information

[1923] Users access their personal page using their device and enter their personal information and past behavioral history. The system also uses an emotion engine to provide information about their current emotions. For example, if a user is feeling stressed, that information is also entered into the system.

[1924] Step 2: Review the advice

[1925] Users can check the advice and risk levels displayed on their device and choose the best course of action. For example, the system may recommend low-risk options based on the user's current emotional state and past data.

[1926] Step 3: Provide feedback

[1927] Users provide feedback on their actual behavior and their satisfaction with it. This feedback is sent to the server via their device and used to improve the accuracy of the digital twin's predictive model. For example, a user can perform a recommended behavior and report the results to the system.

[1928] Specific examples

[1929] For example, if a user uses the system for health management purposes:

[1930] Step 1:

[1931] Users use their devices to input their past health data and current lifestyle habits into their personal page, and also input their current emotional state using the emotion engine.

[1932] Step 2:

[1933] The terminal transmits this information to the server.

[1934] Step 3:

[1935] The server uses this information to create a digital twin.

[1936] Step 4:

[1937] The server uses the created digital twin to simulate health behavior and calculate the optimal health strategy and its risk level.

[1938] Step 5:

[1939] The server sends the calculation results to the terminal.

[1940] Step 6:

[1941] The device displays health advice and risk levels sent from the server.

[1942] Step 7:

[1943] The user decides on health management actions based on the displayed advice and inputs the results into the system as feedback.

[1944] In this way, the system of the present invention predicts the user's behavior and takes their emotional state into account to help them make better decisions, allowing them to achieve their goals more safely and efficiently.

[1945] Example 2

[1946] 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."

[1947] In recent years, there has been an increasing need for systems that provide highly accurate behavioral predictions and advice that take into account an individual's emotional state and behavioral patterns. However, conventional systems lack an established method for integrating personal and emotional information to simulate user behavior. As a result, they have faced issues such as low prediction accuracy and failure to meet user expectations. Furthermore, they lacked a mechanism for improving the model based on feedback, making it difficult to improve user satisfaction.

[1948] 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 personal information and emotional information of a user, means for encrypting and transmitting the personal information and emotional information input by the user, means for storing the received personal information and emotional information in a database, means for generating a digital twin by a generative AI model using the stored personal information and emotional information, means for performing a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and a risk level, and means for providing the user with the behavioral prediction and risk level obtained as a result of the behavioral simulation. This makes it possible to provide highly accurate behavioral predictions and advice that take into account the individuality of the user.

[1949] "User" refers to an individual or organization that uses the system and provides personal and emotional information.

[1950] "Personal information" refers to information that can identify a specific individual, such as a user's name, address, age, and gender.

[1951] "Emotion information" is data that represents the user's current emotional state, and is information obtained using an emotion engine.

[1952] An "emotion engine" is a software or hardware system for analyzing a user's emotional state.

[1953] "Terminal" means a device that a user uses to input, send, or receive information, including a computer, smartphone, tablet, etc.

[1954] A "server" is a computer system that receives, stores, and analyzes data sent by users.

[1955] A "database" is a repository of information installed within a server, and is a system for systematically storing and managing personal and emotional information.

[1956] A "generative AI model" is an artificial intelligence algorithm or program that generates a digital twin based on input data.

[1957] A "digital twin" is a virtual model created based on a user's personal and emotional information, which mimics the user's characteristics and behavioral patterns.

[1958] "Behavioral simulation" is the process of predicting a user's future behavior using a generated digital twin.

[1959] "Behavior prediction" is predictive data about a user's future behavior obtained through behavior simulation.

[1960] The "risk level" is a numerical value or evaluation that indicates the degree of risk associated with a particular behavior, calculated based on behavior prediction.

[1961] "Advice" refers to optimal behavioral options or recommendations provided to users based on the results of behavioral simulations.

[1962] "Feedback" is information provided by users about the results of their actual actions and their satisfaction, and is used to improve the accuracy of the system's model.

[1963] "My Private Page" is a web page or application interface where users can input and manage personal information, emotional information, purchase history, behavioral history, family information, etc.

[1964] The present invention relates to a system that generates a digital twin based on a user's personal information and emotional information, and provides predictions and advice through behavioral simulations using the digital twin. The following describes specific embodiments of the invention.

[1965] System Configuration

[1966] This system consists of four elements: a server, a terminal, a user, and an emotion engine. Each of these elements functions as follows:

[1967] Server Roles

[1968] The server receives, stores, and analyzes the personal information and emotional information sent by the user. First, the data sent from the device is securely transmitted using an encryption protocol (e.g., HTTPS). The server then stores this data in a database.

[1969] The server then runs a generative AI model to generate a digital twin of the user, which analyzes the stored personal and emotional information to create a virtual model that mimics the user's characteristics and behavioral patterns.

[1970] Based on the generated digital twin, the server runs a behavioral simulation that utilizes past data, typical behavioral patterns, and the user's current emotional state to calculate behavioral predictions and risk levels for multiple options the user may choose.

[1971] Finally, the server generates advice based on the simulation results and sends this information to the device. The advice provided to the user includes predicted behavior and risk levels.

[1972] Device Role

[1973] The terminal is a device that allows users to input, send, and receive information. Users access their personal page and enter their personal information, past behavioral history, and emotional state. The terminal works in conjunction with the emotion engine to obtain the user's emotional information.

[1974] The device sends the entered data to a server using an encryption protocol, and the server then sends advice and risk level information that is displayed to the user in an easy-to-read format.

[1975] User Roles

[1976] Users access their personal page and enter their personal information, past behavioral history, and emotional state. This information is often obtained using an emotion engine. The user checks the advice provided by the server and selects the most appropriate action. They also provide feedback on the results of their actual actions and their satisfaction with them. This feedback is sent to the server and used to improve the accuracy of the digital twin's prediction model.

[1977] Specific examples

[1978] For example, the flow when a user uses the system for health management purposes will be explained.

[1979] 1. The user uses a device to enter past health data and current lifestyle habits into their personal page, and also inputs their current emotional state using the emotion engine.

[1980] 2. The server receives this information and creates a digital twin.

[1981] 3. The server uses the generated digital twin to simulate the user's health behavior and calculate the optimal health strategy and its risk level.

[1982] 4. The server sends this information to the user's device.

[1983] 5. The device displays the health advice and risk level sent from the server.

[1984] 6. The user decides on health management actions based on the displayed advice and enters the results into the system as feedback.

[1985] Prompt Sentence Examples

[1986] What specific data items do users enter for health management?

[1987] What predictive models does the server use to simulate behavior?

[1988] What exactly is the process for providing feedback?

[1989] Through these steps, the system predicts the user's behavior and takes their emotional state into account to help them make better decisions, allowing them to achieve their goals more safely and efficiently.

[1990] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1991] Step 1: Entering User Data

[1992] Users access their personal page using their own devices and enter a wide range of data, including personal information, past behavioral history, health data, and emotional state, such as name, age, weight, food records, and exercise volume.

[1993] Input: Personal information, past behavioral history, health data, emotional state

[1994] Output: Encrypted personal and emotional information

[1995] Specific behavior:

[1996] Users can open their personal page using a browser or mobile app.

[1997] Enter the required information using the text boxes and drop-down menus.

[1998] Activate the emotion engine and measure your emotional state.

[1999] Step 2: Sending data

[2000] The device encrypts the input data before sending it to the server. By using an encryption protocol (e.g. HTTPS), information leakage is prevented.

[2001] Input: Personal and emotional information entered by the user

[2002] Output: Data encrypted and sent to the server

[2003] Specific behavior:

[2004] The terminal encrypts the user's input data.

[2005] The encrypted data is sent to the server.

[2006] Step 3: Receiving and storing data

[2007] The server receives the data sent from the device and stores it in a database. It checks the integrity of the received data and stores it appropriately.

[2008] Input: Data sent encrypted from the device

[2009] Output: Personal and emotional information stored in a database

[2010] Specific behavior:

[2011] The server decrypts the received data and checks its integrity.

[2012] Store data appropriately in the database.

[2013] Step 4: Create a digital twin

[2014] The server then uses the stored personal and emotional information to run a generative AI model to create a digital twin, which virtually replicates the user's characteristics and behavioral patterns.

[2015] Input: Personal and emotional information stored in a database

[2016] Output: Generated digital twin

[2017] Specific behavior:

[2018] The server provides the data needed for the generative AI model.

[2019] The generative AI model generates a digital twin and returns the data to the server.

[2020] Step 5: Running a behavioral simulation

[2021] The server uses the created digital twin to simulate behavior under various conditions, taking into account past data, general behavioral patterns, and the user's current emotional state to make predictions.

[2022] Input: Generated digital twin, historical data, general behavioral patterns, current emotional state

[2023] Output: Behavioral prediction and risk level

[2024] Specific behavior:

[2025] The server runs the simulation engine and runs multiple scenarios.

[2026] Prediction data and risk levels are calculated from the results of each scenario.

[2027] Step 6: Generating and serving advice

[2028] The server generates advice for the user based on the results of the behavioral simulation, and the advice is sent to the terminal and provided to the user.

[2029] Input: Simulation results, predicted behavior and risk level

[2030] Output: Advice given to the user

[2031] Specific behavior:

[2032] The server creates customized advice based on the generated prediction data.

[2033] The created advice is sent to the device.

[2034] Step 7: User reviews advice and chooses action

[2035] The user uses the device to review the received advice, which includes action options and their risk levels. The user can then choose the best course of action based on the advice.

[2036] Input: Advice sent by the server

[2037] Output: User's action choice

[2038] Specific behavior:

[2039] The terminal displays the advice from the server.

[2040] Users decide what to do based on their situation and goals.

[2041] Step 8: Provide feedback

[2042] Users provide feedback on the results of their actions and their satisfaction with them, which is sent to the server and used to improve the accuracy of the model.

[2043] Input: User behavior results and satisfaction

[2044] Output: Feedback sent to the server

[2045] Specific behavior:

[2046] The user enters the results of their actual actions into the terminal.

[2047] The terminal transmits the feedback data to the server.

[2048] The server analyzes the received feedback and adjusts the parameters of the model.

[2049] (Application example 2)

[2050] 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."

[2051] While conventional systems were able to predict behavior and provide advice based on a user's personal and emotional information, they were unable to provide useful advice in real time in actual purchasing situations. When users are physically in a store, it is difficult to receive optimal advice or coupon information when selecting products, and there was a need to improve the purchasing experience.

[2052] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting a user's personal information and emotional state, means for receiving and storing the input personal information and emotional information, means for generating a digital twin based on the received personal information and emotional information, means for running a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and risk level, means for providing the calculation results to the user, and means for displaying purchasing advice in real time when the user is selecting a product in a physical store. This allows the user to receive optimal advice and coupon information in real time even in a physical store, improving the purchasing experience.

[2053] A "user" is an individual who uses this system and provides personal and emotional information.

[2054] "Personal information" refers to all data related to a user, such as basic information, purchasing history, behavioral history, and lifestyle habits.

[2055] "Emotional state" is information that indicates the user's current mood and psychological state.

[2056] A "digital twin" is a virtual model that mimics a user's characteristics and behavioral patterns, created based on the user's personal and emotional information.

[2057] "Behavioral simulation" is the process of using a generated digital twin to predict a user's behavioral patterns and calculate their behavioral options and risk levels.

[2058] The "risk level" indicates the degree of risk that a particular behavior may cause, as determined by the behavioral simulation.

[2059] "Means for displaying purchasing advice in real time" refers to a function that instantly displays optimal purchasing advice via a device such as a smartphone when a user is selecting a product in a physical store.

[2060] "Feedback" is information that provides the system with the results of a user's actual actions and their satisfaction.

[2061] Overall system overview

[2062] This invention is a system that generates a digital twin based on a user's personal information and emotional state, and provides predictions and advice through behavioral simulation using the digital twin. The main elements of this system include a server, a terminal, a user, and an emotion engine.

[2063] Server Roles

[2064] The server performs the following process.

[2065] 1. Data Receipt and Storage

[2066] The server receives personal and emotional information from users and stores it in a database, which is used to generate digital twins and simulate their behavior.

[2067] 2. Creating a digital twin

[2068] The server runs a generative AI model based on the stored personal and emotional information to create a digital twin of the user, a virtual model that mimics the user's characteristics, behavioral patterns, and emotional state.

[2069] 3. Behavioral Simulation and Prediction

[2070] The server uses a digital twin to simulate the user's behavior and calculates predicted behavior and risk levels, using historical data, general behavioral patterns, and current emotional state.

[2071] 4. Real-time purchasing advice

[2072] When users are selecting products in physical stores, the system provides optimal purchasing advice in real time, allowing users to make more effective in-store purchases.

[2073] Device Role

[2074] The terminal performs the following processing.

[2075] 1. User data input

[2076] The device provides an interface for users to input their personal and emotional information.

[2077] 2. Data transmission

[2078] The device sends the entered personal and emotional information to a server, using an encryption protocol to prevent information leakage.

[2079] 3. Displaying Advice

[2080] The device displays advice and risk level information sent from the server to the user, allowing the user to choose their course of action based on this information.

[2081] User Roles

[2082] The user performs the following process:

[2083] 1. Enter your information

[2084] Users use their devices to access their dedicated personal page and enter personal and emotional information.

[2085] 2. Check the advice and choose your course of action

[2086] Users can review the advice displayed on their device and choose the best course of action, taking into account risk levels and recommendations.

[2087] 3. Providing Feedback

[2088] After taking an action, users provide feedback about the results and their satisfaction, which is sent to the server and used to improve the accuracy of the digital twin's predictive model.

[2089] Hardware and software used

[2090] The following hardware and software are used to implement this system:

[2091] Server: The backend is built using Python and Flask, and Pandas, scikit-learn, and TensorFlow are used for data analysis.

[2092] Database: Use SQLite or Firebase for data storage.

[2093] Device: Develop a smartphone app that runs on iOS / Android and provide an interface to users.

[2094] Encryption Protocols: We use encryption technologies such as SSL / TLS to ensure data security.

[2095] Specific prompt examples

[2096] For example, the following prompt might be generated for a user looking to purchase a health drink:

[2097] User ID: 12345, Personal information: { Purchase history: ["health drinks", "supplements"]}, Emotion information: { Mood: "stressful"}

[2098] Generated advice: "I recommend a drink that's good for relieving stress. You can use this coupon."

[2099] Based on this prompt, users can select the most suitable product in the store, providing a better shopping experience.

[2100] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2101] Step 1:

[2102] The user uses the device to input personal and emotional information. The input information is retrieved through the device interface. This information includes the user's purchasing history, lifestyle habits, and current mood. The input data is converted into JSON format and sent to the server using an encryption protocol (e.g., SSL / TLS).

[2103] Step 2:

[2104] The server receives the personal information and emotional information sent from the device and stores it in a database. The received data is filtered and parsed to remove outliers and invalid data. The data is then stored in a database (e.g., SQLite or Firebase) and used as the basis for subsequent digital twin generation and simulation processing.

[2105] Step 3:

[2106] The server runs a generative AI model based on the stored personal and emotional information to generate a digital twin of the user. The generative AI model uses a machine learning framework (e.g., TensorFlow) to analyze the input data and create a virtual model that mimics the user's characteristics and behavioral patterns. The generated digital twin is then stored in a database.

[2107] Step 4:

[2108] The server then runs a behavioral simulation using the created digital twin. The behavioral simulation takes into account statistical data, general behavioral patterns, and the user's current emotional state to predict the user's purchasing behavior. The simulation results also calculate the risk level for multiple choices. This data is then saved in JSON format.

[2109] Step 5:

[2110] When a user selects a product in a physical store, the smartphone device uses the camera and GPS to identify the product and acquire its location. The identified product information and store information are immediately sent to the server. The transmitted data includes the product ID and location information.

[2111] Step 6:

[2112] The server generates optimal purchasing advice in real time based on the received product information and location information. Using a generative AI model, it compares past data and simulation results to determine the advice and coupon information that should be provided to the user. The generated advice is then sent to the device.

[2113] Step 7:

[2114] The device instantly displays the advice and coupon information sent from the server to the user on the smartphone's display, allowing the user to choose the best purchasing behavior based on this information.

[2115] Step 8:

[2116] Users provide feedback on the results of their purchases and their satisfaction. Feedback information is entered through the device interface and sent to the server. The submitted feedback is received and stored on the server, and used to improve the accuracy of the digital twin.

[2117] 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.

[2118] 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.

[2119] 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 robot 414.

[2120] 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.

[2121] 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.

[2122] 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.

[2123] 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).

[2124] 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.

[2125] 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."

[2126] 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.

[2127] 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).

[2128] 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.

[2129] 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.

[2130] 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.

[2131] 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.

[2132] 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.

[2133] 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.

[2134] 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.

[2135] 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.

[2136] 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.

[2137] 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.

[2138] The following is further disclosed regarding the above embodiment.

[2139] (Claim 1)

[2140] a means for inputting the user's personal information;

[2141] A means for receiving and storing the personal information you enter;

[2142] a means for generating a digital twin based on the received personal information;

[2143] means for executing a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and a risk level;

[2144] a means for providing the results of the calculations to a user;

[2145] A system including:

[2146] (Claim 2)

[2147] 10. The system of claim 1, further comprising means for receiving feedback from a user and utilizing the feedback to improve the accuracy of the digital twin.

[2148] (Claim 3)

[2149] 2. The system according to claim 1, further comprising means for providing a dedicated personal page for inputting a user's purchase history, behavior history, and family information.

[2150] "Example 1"

[2151] (Claim 1)

[2152] a means for inputting the user's personal information;

[2153] A means for receiving and storing the personal information you enter;

[2154] a means for generating a digital twin using a generative AI model based on the received personal information;

[2155] means for generating a prompt sentence for generating a digital twin;

[2156] means for executing a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and a risk level;

[2157] a means for providing the results of the calculations to a user;

[2158] A system including:

[2159] (Claim 2)

[2160] 10. The system of claim 1, further comprising ...

Claims

1. a means for inputting the user's personal information; A means for receiving and storing the personal information you enter; a means for generating a digital twin based on the received personal information; means for executing a behavioral simulation based on the generated digital twin and calculating a behavioral prediction and a risk level; a means for providing the results of the calculations to a user; A system including:

2. 10. The system of claim 1, further comprising means for receiving feedback from a user and using the feedback to improve the accuracy of the digital twin.

3. The system according to claim 1, further comprising means for providing a dedicated personal page for inputting a user's purchase history, behavior history, and family information.

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