System

Abstract

A system is provided.SOLUTION: A system comprising: means for automatically generating ethical scenarios using a generated AI model; means for displaying the generated ethical scenarios on user devices; means for collecting user answers to ethical scenarios; means for evaluating the answers and scoring user ethical judgments; means for setting user ranks based on the scores; means for generating privilege information according to the user ranks; and means for displaying the privilege information on user devices.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] Modern society lacks systems that properly evaluate ethical behavior and judgments and provide fair treatment based on those evaluations. As a result, those who behave ethically inappropriately are not penalized, while those who behave ethically and fairly are not properly evaluated or treated, resulting in social injustice. The present invention aims to solve these problems by providing a system that accurately evaluates users' ethical behavior and values ​​and provides fair treatment based on those evaluations. [Means for solving the problem]

[0005] The present invention provides a system that automatically generates ethical scenarios using a generative AI model and collects and evaluates users' responses to the scenarios. Specifically, the system includes the following means: a means for automatically generating ethical scenarios using a generative AI model, a means for displaying the generated ethical scenarios on a user device, a means for collecting users' responses to the ethical scenarios, a means for evaluating the responses and scoring the users' ethical judgments, a means for setting the users' ranks based on the scores, a means for generating reward information according to the users' ranks, and a means for displaying the reward information on the user device. This makes it possible to effectively evaluate users' ethics, values, and judgment criteria and provide fair treatment appropriate to each individual.

[0006] A "generative AI model" refers to an artificial intelligence algorithm or program that automatically generates ethical scenarios.

[0007] An "ethical scenario" refers to a story or question that presents a situation or problem that requires the user to make an ethical decision.

[0008] "User device" refers to an electronic device used by a user, such as a computer, smartphone, or tablet.

[0009] "Means for displaying" refers to the function of visually presenting the generated ethical scenario and bonus information on the user device.

[0010] "Means for collecting answers" refers to the function of sending answers to ethical scenarios selected or entered by the user to the server.

[0011] "Means for evaluation" refers to the function of analyzing the user's answers and quantifying or scoring the ethical judgment based on the answers.

[0012] "Means for scoring" refers to the function of expressing a user's ethical choices and behavior as a score or rank based on the evaluation results.

[0013] "Means for setting a rank" refers to a function that determines a user's ethical status or grade based on the scoring results.

[0014] The "means for generating benefit information" refers to a function for creating the details of benefits and systems to be provided to users according to the set rank.

[0015] "Means for displaying benefit information" refers to a function that visually provides generated benefit or program information on a user device. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This system automatically generates ethical scenarios using a generative AI model and evaluates and ranks user responses. This system consists of three main components: a server, a terminal, and a user.

[0038] System Overview

[0039] Server Operation

[0040] The server first uses a generative AI model to generate ethical scenarios, which depict ethical issues and situations that users may face. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and prepared for transmission to user devices.

[0041] Device behavior

[0042] The user's device (computer, smartphone, tablet, etc.) displays an ethical scenario sent from the server. The user responds through the device's interface regarding their actions and opinions regarding the scenario. For example, in response to the question, "What would you do if you found a wallet on the street?", the user could select an option such as "Take it to the police station."

[0043] User Actions

[0044] The user selects their own ethical judgments and actions for the scenarios on the terminal and inputs their answers, which are then sent to the server via the terminal.

[0045] Server Ratings and Rankings

[0046] The server receives the user's answers sent from the device and evaluates them using a generative AI model. Based on the evaluation results, the user's ethical judgments and actions are scored. For example, if the choice of "deliver to the police station" is highly evaluated, the user will be given a high score.

[0047] Based on the score, the server determines the user's rank. For example, a user with a score of 90 or more is assigned a "high rank." After the rank is assigned, the server generates reward and program information appropriate for the user.

[0048] Terminal display

[0049] The generated benefit information is sent to the terminal again and displayed to the user. For example, information such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" is displayed.

[0050] Specific examples

[0051] 1. Server-side scenario generation: The generative AI model generates the scenario: "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away.

[0052] 2. Displaying scenarios on the terminal: The above scenarios and options are displayed to the user.

[0053] 3. User response: The user selects "Deliver to the police station."

[0054] 4. Server evaluation: The server analyzes the answer "deliver to the police station" and gives it a high evaluation score (for example, 90 points).

[0055] 5. Rank determination and reward generation: The server will set a "high rank" based on the score and generate rewards such as "tax reduction" or "priority support in the event of a disaster."

[0056] 6. Displaying rewards on the device: Display the generated reward information to the user.

[0057] This system makes it possible to accurately evaluate users' ethical behavior and provide fair treatment based on that evaluation.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The server runs the generative AI model to generate new ethical scenarios. The generated scenarios are stored in an internal database. For example, a scenario might be generated: "You found a wallet on the street. What would you do?"

[0061] Step 2:

[0062] The server prepares to send the generated scenario to the terminal. This scenario contains multiple options.

[0063] Step 3:

[0064] The terminal displays the scenario received from the server to the user. For example, a scenario such as "You found a wallet on the street. It has a lot of cash inside. What would you do?" is displayed.

[0065] Step 4:

[0066] The user reads the displayed scenario and selects an action from the options presented. For example, they can select "deliver to the police station."

[0067] Step 5:

[0068] The terminal records the user's selection and transmits the information to the server.

[0069] Step 6:

[0070] The server analyzes the responses received from users and uses a generative AI model to evaluate whether the responses are ethical, moral, and fair.

[0071] Step 7:

[0072] The server calculates the user's score based on the evaluation results. For example, the option "Deliver to the police station" is highly rated and is set to a score of 90.

[0073] Step 8:

[0074] The server assigns a rank to the user based on the calculated score. For example, if the user has a score of 90 or above, the server assigns a "high rank."

[0075] Step 9:

[0076] The server generates reward information according to the user's rank. High-ranking users are given "tax reductions" and "priority support in the event of a disaster."

[0077] Step 10:

[0078] The server transmits the generated bonus information to the terminal.

[0079] Step 11:

[0080] The terminal displays the benefit information received from the server to the user, for example, "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster."

[0081] This series of steps creates a system in which users' ethical behavior and judgments are evaluated and appropriate treatment is provided based on that evaluation.

[0082] Example 1

[0083] 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."

[0084] In modern society, the ethical judgment of individuals is highly valued, and there is a growing demand for systems that evaluate such judgments and provide appropriate social responses and rewards. However, there is a lack of means to objectively and efficiently evaluate users' ethical judgments. There is also a need for a system that can appropriately rank users based on the evaluation results and quickly provide reward information according to their rank.

[0085] 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.

[0086] In this invention, the server includes means for automatically generating an ethical scenario using a generative AI model, means for displaying the generated ethical scenario on a user terminal, means for collecting users' answers to the ethical scenario, means for evaluating the answers and scoring the users' ethical judgments, means for setting the users' ranks based on the scores, means for generating reward information according to the users' ranks, and means for displaying the reward information on the user terminal. This makes it possible to objectively evaluate the users' ethical judgments and provide appropriate responses and rewards based on the evaluation.

[0087] A "generative AI model" is an artificial intelligence model that uses advanced algorithms to generate text and automatically create ethical scenarios that users may encounter.

[0088] An "ethical scenario" is a scenario that specifically depicts moral and ethical issues or situations that users may face.

[0089] "User terminal" refers to an electronic device, such as a computer, smartphone, or tablet, that a user operates to input and display information.

[0090] "Means for collecting responses" refers to a system that allows users to select their opinions and actions regarding ethical scenarios, and then electronically captures and records them.

[0091] "Scoring means" refers to a system that evaluates answers provided by users and assigns them a score or rating based on pre-defined criteria.

[0092] "Means for setting a rank" refers to a system for determining the ranking or rating of a user based on the scoring results and assigning a rank.

[0093] The "means for generating benefit information" refers to a system that determines the content of the benefits and systems that will be applied depending on the user's rank and creates that information.

[0094] The "means for displaying special benefit information" refers to a system that transmits the generated special benefit information to a user terminal and displays it in a form that can be understood by the user.

[0095] This invention describes a system consisting of three main elements: a server, a terminal, and a user, which automatically generates ethical scenarios using a generative AI model and evaluates and ranks the user's answers.

[0096] Server Operation

[0097] The server first uses a generative AI model (e.g., OpenAI's GPT-4) to generate ethical scenarios that users may encounter. These scenarios are presented to users in the form of depictions of moral and ethical issues and situations. The generated scenarios are classified according to difficulty level (beginner, intermediate, advanced, etc.) and stored in a database on the server.

[0098] Specifically, it is generated using the following prompt:

[0099] “You find a wallet on the street and it has a lot of cash inside, what do you do?

[0100] A. Take it to the police station

[0101] B. Make it your own

[0102] C. Leave it there and walk away.”

[0103] Device behavior

[0104] The user's device (computer, smartphone, tablet, etc.) receives and displays the ethical scenario sent from the server. The user selects actions and opinions regarding this scenario through the device's interface. For example, the user can choose an action such as "deliver to the police station" from the options.

[0105] User Actions

[0106] The user makes their own ethical judgments about the scenarios displayed on the device and selects the most appropriate option from the options. The answer is then sent to the server via the device.

[0107] Server Ratings and Rankings

[0108] The server receives the user's answers sent from the device and evaluates them using a generative AI model. Based on the evaluation results, the user's ethical judgments and actions are scored, and for example, the option "deliver to the police station" is given a high score (e.g., 90 points). Based on this score, the server determines the user's rank. Specifically, users with a score above a certain level are assigned a "high rank." The server also generates reward information (e.g., tax reductions, priority support in the event of a disaster) according to the user's rank.

[0109] Terminal display

[0110] The generated reward information is sent back to the user's terminal and displayed to the user. For example, a message such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" is displayed to the user.

[0111] Specific examples

[0112] 1. Server-side scenario generation: The server uses a generative AI model to generate a scenario: "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away.

[0113] 2. Displaying scenarios on the terminal: The above scenarios and options are displayed to the user.

[0114] 3. User response: The user selects "Deliver to the police station."

[0115] 4. Server evaluation: The server analyzes the answer "deliver to the police station" and gives it a high evaluation score (for example, 90 points).

[0116] 5. Rank determination and reward generation: The server will set a "high rank" based on the score and generate rewards such as "tax reduction" or "priority support in the event of a disaster."

[0117] 6. Displaying rewards on the device: Display the generated reward information to the user.

[0118] This system makes it possible to fairly and efficiently evaluate users' ethical behavior and provide appropriate treatment and benefits based on that evaluation.

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

[0120] Step 1:

[0121] Server-based scenario generation

[0122] Input: Prompt sentence: "You find a wallet on the street. It has a large amount of cash inside. What do you do?"

[0123] The server uses a generative AI model to automatically generate ethical scenarios that users may encounter, which include multiple choices and are categorized according to difficulty level: beginner, intermediate, or advanced.

[0124] Output: Generated ethical scenario (e.g., "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away)

[0125] Specific operation: The server inputs the prompt sentence into the generative AI model and saves the generated scenario in the database.

[0126] Step 2:

[0127] Sending scenarios from the server to the device

[0128] Input: Ethical scenario stored on the server

[0129] The server sends the generated scenario to the user's terminal.

[0130] Output: Scenario data sent to the user's device

[0131] Specific operation: The server retrieves the scenario from the database and sends it to the user's terminal.

[0132] Step 3:

[0133] Displaying scenarios on a device

[0134] Input: Scenario data sent from the server

[0135] The user's terminal displays the ethical scenario received from the server.

[0136] Output: The scenario displayed on the terminal screen

[0137] Specific operation: The terminal analyzes the scenario data received from the server and displays it on the screen.

[0138] Step 4:

[0139] User selection and response submission

[0140] Input: Scenario and options displayed on the terminal

[0141] The user makes their own ethical judgment about the scenario displayed on the terminal, selects an option, and submits their answer.

[0142] Output: The answer selected by the user

[0143] Specific operation: The user selects an option such as "deliver to the police station" and sends the answer to the server via the terminal.

[0144] Step 5:

[0145] Server-based answer evaluation and scoring

[0146] Input: User-selected answer data

[0147] The server evaluates the user's answers received from the device and scores them using a generative AI model.

[0148] Output: User's rating score for ethical judgment

[0149] Specific operation: The server inputs the user's answer into the generative AI model, analyzes it, and stores the evaluation score (e.g., "Take it to the police station" gets 90 points) in the database.

[0150] Step 6:

[0151] Server determines rank and generates rewards

[0152] Input: Evaluation score data

[0153] The server determines the rank of the user based on the evaluation score and generates bonus information according to the rank.

[0154] Output: User rank and benefits information (e.g., high rank, tax reduction, priority support in case of disaster)

[0155] Specific operation: The server analyzes the score, sets the user as "high rank", and generates reward information.

[0156] Step 7:

[0157] Sending bonus information from the server to the device

[0158] Input: Bonus information generated on the server

[0159] The server transmits the generated bonus information to the user's terminal.

[0160] Output: Reward information data sent to the user device

[0161] Specific operation: The server sends the reward information to the user's terminal.

[0162] Step 8:

[0163] Displaying bonus information on your device

[0164] Input: Bonus information data sent from the server

[0165] The user's terminal displays the bonus information received from the server.

[0166] Output: Reward information displayed on the device screen

[0167] Specific operation: The terminal analyzes the bonus information data received from the server and displays it on the screen.

[0168] (Application example 1)

[0169] 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."

[0170] Conventional ethical education and assessment systems lack effective and enjoyable methods for helping users learn ethical judgment. Furthermore, conventional systems only present simple scenarios and collect answers, and do not sufficiently motivate users to take actual action. In other words, innovative methods are needed to encourage users to behave ethically and to learn in an enjoyable way.

[0171] 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.

[0172] In this invention, the server includes means for automatically generating an ethical scenario using a generative AI model, means for displaying the generated ethical scenario on a user device, means for collecting user responses to the ethical scenario, means for evaluating the responses and scoring the user's ethical judgment, means for setting the user's rank based on the score, means for generating reward information according to the user's rank, means for displaying the reward information on the user device, and means for providing an interface in a virtual store where users can respond based on the ethical scenario and automatically awarding rewards such as points or coupons according to the responses. This allows users to learn about ethics while having fun, and the rewards increase their motivation, which is expected to lead to a high level of learning effectiveness.

[0173] A "generative AI model" is an artificial intelligence technology that automatically generates ethical problems and situations that users may face.

[0174] An "ethical scenario" is a hypothetical scenario that depicts an ethical problem or situation that a user is asked to solve.

[0175] A "user device" is a terminal such as a computer, smartphone, or tablet that a user uses to access the system.

[0176] The "answer collection means" is a function that allows users to input and record answers to ethical scenarios on the device.

[0177] The "evaluation means" is a function that analyzes the user's responses and scores the accuracy and appropriateness of the ethical judgment based on the results.

[0178] The "scoring means" is a function that evaluates the user's answers analyzed by the evaluation means as numerical values ​​or grades.

[0179] The "rank setting means" is a function that determines the rank of a user based on the score calculated by the scoring means.

[0180] The "privilege information generating means" is a function that generates privilege information such as points and coupons according to the rank of the user.

[0181] A "virtual store" is an environment where users can virtually shop in a digital space.

[0182] The "interface providing means" is a function that provides a screen and an operation method for the user to input answers to the scenario.

[0183] System Overview

[0184] Server Operation

[0185] The server first uses a generative AI model to automatically generate ethical scenarios. The generated scenarios depict ethical issues and situations that users may encounter in their daily lives. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and prepared for transmission to the user's device.

[0186] Device behavior

[0187] The user's device (such as a smartphone or head-mounted display) displays an ethical scenario sent from the server. The user responds to the scenario through the device's interface, expressing their actions and opinions. For example, in response to the scenario, "You found a wallet at the cash register. What would you do?", the user would select an option such as "Take it to the clerk."

[0188] User Actions

[0189] The user selects their own ethical judgment and actions for the scenario displayed on the terminal and transmits their response via the terminal.

[0190] Server Ratings and Rankings

[0191] The server receives the user's answers sent from the device and evaluates them using a generative AI model. Based on the evaluation results, the user's ethical judgments and actions are scored. For example, if the choice of "deliver to the store clerk" is highly evaluated, the user is given a high score. Based on the score, the server determines the user's rank. For example, a user with a score of 90 or above is assigned a "high rank."

[0192] Generate and view rewards

[0193] The server generates reward information according to the rank and sends it to the user device. The reward information is displayed on the user device. For example, a notification such as "Your rank is high. You have been awarded a reward of 100 points" is displayed.

[0194] Specific examples

[0195] 1. Server-side scenario generation:

[0196] The generative AI model generates the following scenario: "You find a wallet on the street. It has a large amount of cash inside. What would you do?"

[0197] Option A: Give it to the store clerk

[0198] Option B: Make it your own

[0199] Option C: Leave it and walk away

[0200] 2. Display scenario on device:

[0201] The above scenario and options are displayed on the user's terminal.

[0202] 3. User Answer:

[0203] The user selects "deliver to store clerk."

[0204] 4. Server Rating:

[0205] The server analyzes the answer "deliver to the store clerk" and gives a high evaluation score (for example, 90 points).

[0206] 5. Rank determination and reward generation:

[0207] The server will set a "high rank" based on the score and generate "100 points" as a reward.

[0208] 6. Displaying the benefits on your device:

[0209] The generated benefit information is displayed to the user.

[0210] Prompt Sentence Examples

[0211] You found a wallet on the street. What would you do?

[0212] 1. Report it to the police station

[0213] 2. Make it your own

[0214] 3. Leave it there and walk away

[0215] This system allows users to learn ethically while having fun, and the rewards it provides increase motivation, leading to the expectation of high learning outcomes.

[0216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0217] Step 1:

[0218] The server automatically generates ethical scenarios using a generative AI model. The server inputs the prompt "Generate ethical issues that users may face" into the generative AI model to obtain ethical scenarios. The generated scenarios are classified into beginner, intermediate, and advanced levels of difficulty.

[0219] Input: prompt statement

[0220] Output: Ethical scenarios (with difficulty classification)

[0221] Step 2:

[0222] The server sends the generated ethical scenario to the user's device, where it is displayed on the user's connected smartphone or head-mounted display.

[0223] Input: Ethical scenario

[0224] Output: Scenario display on user terminal

[0225] Step 3:

[0226] The user inputs their response to the scenario through the interface on the device. As a specific action, the user selects the appropriate answer from the presented options. For example, in response to the question, "You found a wallet on the street. What would you do?", the user selects "Take it to the police station."

[0227] Input: Scenario choices

[0228] Output: User's answer

[0229] Step 4:

[0230] The device sends the user's answer to the server, which analyzes the received answer and evaluates it using a generative AI model. The evaluation process includes checking against a surrounding knowledge base and comparing it with similar answers from the past.

[0231] Input: User's answer

[0232] Output: Evaluation result (score)

[0233] Step 5:

[0234] The server calculates the user's score based on the evaluation results and assigns a rank according to that score. For example, if the score is 90 or above, it will be assigned a "high rank."

[0235] Input: Evaluation result (score)

[0236] Output: User's rank

[0237] Step 6:

[0238] The server generates reward information (points, coupons, etc.) according to the user's rank. The type and amount of reward information varies depending on the rank.

[0239] Input: User rank

[0240] Output: Bonus information

[0241] Step 7:

[0242] The server transmits the generated reward information to the user's terminal, which then displays it to the user. The displayed reward is visualized in a concrete form such as points or coupons.

[0243] Input: Bonus information

[0244] Output: Display rewards on user device

[0245] 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.

[0246] This invention is a system that automatically generates ethical scenarios using a generative AI model, evaluates and ranks user responses to those scenarios, and recognizes the user's emotions and reflects them in the evaluation and ranking. This system consists of four main components: a server, a terminal (user device), a user, and an emotion engine.

[0247] System Overview

[0248] Server Operation

[0249] The server first uses a generative AI model to generate ethical scenarios, which depict ethical issues and situations that users may face. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and prepared for transmission to user devices.

[0250] Device behavior

[0251] The user's device (computer, smartphone, tablet, etc.) displays an ethical scenario sent from the server. The user responds through the device's interface regarding their actions and opinions regarding the scenario. For example, in response to the question, "What would you do if you found a wallet on the street?", the user could select an option such as "Take it to the police station."

[0252] User Actions

[0253] The user selects their own ethical judgment and behavior for the scenario on the device and inputs their response. The response is sent to the server via the device. When the user responds, the emotion engine recognizes the user's emotions from their facial expression, tone of voice, input speed, etc.

[0254] Emotion Engine Operation

[0255] The emotion engine analyzes the emotions of users as they answer ethical scenarios. This engine analyzes the user's facial expressions, tone of voice, typing speed, etc. in real time to identify the user's emotional state. For example, it can recognize whether the user is nervous or calm when answering. The emotion engine sends this information to the server.

[0256] Server Ratings and Rankings

[0257] The server analyzes the user's answers received from the device and the user's emotional data received from the emotion engine. Using a generative AI model, the server evaluates whether the answers and emotional data are ethical, moral, and fair. For example, the choice "Take it to the police station" is highly evaluated and assigned a high score. Furthermore, if the server recognizes that the user responded calmly, the score may be even higher.

[0258] Based on the score, the server determines the user's rank. For example, a user with a score of 90 or more is assigned a "high rank." After the rank is assigned, the server generates reward and program information appropriate for the user.

[0259] Terminal display

[0260] The generated benefit information is sent to the terminal again and displayed to the user. For example, information such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" is displayed.

[0261] Specific examples

[0262] 1. Server-side scenario generation: The generative AI model generates the scenario: "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away.

[0263] 2. Displaying scenarios on the terminal: The above scenarios and options are displayed to the user.

[0264] 3. User response and emotion recognition: The user selects "Deliver to the police station." At the same time, the emotion engine recognizes the user's calm emotion from their facial expression and tone of voice.

[0265] 4. Server evaluation: The server analyzes the answer "Take it to the police station" and the emotional data that the user answered calmly, and gives a high evaluation score (for example, 90 points).

[0266] 5. Rank determination and reward generation: The server will set a "high rank" based on the score and generate rewards such as "tax reduction" or "priority support in the event of a disaster."

[0267] 6. Displaying rewards on the device: Display the generated reward information to the user.

[0268] This system makes it possible to accurately assess users' ethical behavior and emotional state and provide fair treatment based on that assessment.

[0269] The processing flow will be explained below.

[0270] Step 1:

[0271] The server runs the generative AI model to generate new ethical scenarios. The generated scenarios are stored in an internal database. For example, a scenario might be generated: "You found a wallet on the street. What would you do?"

[0272] Step 2:

[0273] The server prepares to send the generated scenario to the terminal. This scenario contains multiple options.

[0274] Step 3:

[0275] The terminal displays the scenario received from the server to the user. For example, a scenario such as "You found a wallet on the street. It has a lot of cash inside. What would you do?" is displayed.

[0276] Step 4:

[0277] The user reads the displayed scenario and selects an action from the options presented. For example, they select "deliver to the police station." At this time, the emotion engine recognizes the user's emotions in real time from their facial expressions, tone of voice, typing speed, etc.

[0278] Step 5:

[0279] The terminal transmits the user's selection together with the emotion data recognized by the emotion engine to the server.

[0280] Step 6:

[0281] The server analyzes the response data and sentiment data received from users and uses a generative AI model to evaluate whether the content and sentiment of the responses are ethical, moral, and fair.

[0282] Step 7:

[0283] The server calculates the user's score based on the evaluation results. For example, if the choice "deliver to the police station" is highly rated, the user will be given a high score. If the user answers calmly, the score may be even higher.

[0284] Step 8:

[0285] The server assigns a rank to the user based on the calculated score. For example, if the user has a score of 90 or above, the server assigns a "high rank."

[0286] Step 9:

[0287] The server generates reward information according to the user's rank. High-ranking users are given "tax reductions" and "priority support in the event of a disaster."

[0288] Step 10:

[0289] The server transmits the generated bonus information to the terminal.

[0290] Step 11:

[0291] The terminal displays the benefit information received from the server to the user, for example, "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster."

[0292] This series of steps creates a system in which users' ethical behavior and judgments are evaluated and appropriate treatment is provided based on that evaluation.

[0293] Example 2

[0294] 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."

[0295] Conventional systems lack fairness and accuracy because they do not consider emotional data when evaluating users' ethical judgments. Another issue is the lack of systems that not only simply score ethical judgments but also provide appropriate feedback and rewards based on those scores. Furthermore, if the difficulty of the generated scenarios is not appropriate for the user's ability, this could affect the reliability of the evaluation.

[0296] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0297] In this invention, the server includes means for automatically generating ethical situations using a generative AI model, means for presenting the generated ethical situations on a user interface, means for collecting responses and emotional data to users' ethical judgments, means for analyzing the responses and emotional data and evaluating and scoring the users' ethical judgments, means for setting the users' ranks based on the scores, means for generating reward information according to the users' ranks, and means for displaying the reward information on the user interface. This improves the accuracy and fairness of evaluations based on the users' ethical judgments, and makes it possible to provide users with feedback and rewards appropriate to them.

[0298] A "generative AI model" is a machine learning algorithm that performs human-like natural language generation based on input prompts.

[0299] An "ethical situation" is a situation or occasion that a user may encounter that requires a moral or ethical decision.

[0300] A "user interface" refers to the interaction that includes a screen and input devices that allow a user to interact with a device or system.

[0301] An "answer" is an option or opinion that a user selects and enters in response to a presented ethical situation.

[0302] "Emotional data" is information about the emotional state of the user, such as facial expressions, tone of voice, and input speed, that is collected when the user answers.

[0303] "Analysis" is the process of statistically or computationally analyzing collected data to extract meaningful information.

[0304] "Scoring" is the process of assigning a score according to specific evaluation criteria based on the user's answers and emotional data.

[0305] "Rank" refers to the evaluation level of a user that is determined based on the score.

[0306] "Reward information" is information about benefits and profits provided according to the user's rank.

[0307] This invention is a system that automatically generates ethical situations using a generative AI model, evaluates and scores users' responses to those situations, and generates and presents reward information. The system consists of four main components: a server, a terminal (user device), a user, and an emotion engine.

[0308] The server first generates ethical situations using a generative AI model. Examples of such models include ChatGPT. Situations are generated by inputting prompts into the model. For example, a prompt such as "Imagine a situation where a user finds a wallet on the street" can be used. Scenarios generated by this model are classified according to difficulty level (beginner, intermediate, or advanced) and saved on the server.

[0309] The server then sends the generated situation to the device, which then displays it on its user interface. For example, the device displays a situation such as "You found a wallet on the street. What would you do?" along with options such as "Take it to the police station," "Keep it," and "Ignore it and walk away."

[0310] The user selects and inputs their own response to a given situation on the device. At this time, the emotion engine analyzes the user's facial expression, tone of voice, input speed, etc. in real time to collect emotional data. This emotion engine uses, for example, emotion recognition software and sensor devices.

[0311] The device sends the user's selected answer and emotional data to the server. The server then statistically or computationally analyzes this data and uses a generative AI model to evaluate and score the user's ethical judgment. For example, the option "Take it to the police station" will receive a high score, and a calm emotional state will receive additional points.

[0312] The server determines the user's rank based on this score and generates reward information accordingly. For example, a user with a score of 90 or more will be assigned a "high rank" and be given special benefits such as "tax reductions" and "priority support in the event of a disaster." This reward information is then sent back to the terminal.

[0313] Finally, the terminal displays the generated reward information to the user on a user interface, such as a message saying, "Your rank is high. You will receive reduced taxes and priority assistance in the event of a disaster."

[0314] In this way, the server, terminal, user, and emotion engine work together to realize a system in which evaluations are made based on the user's ethical judgment and fair rewards are provided.

[0315] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0316] Step 1:

[0317] The server uses a generative AI model to generate ethical situations. The server inputs the prompt "Imagine a situation where a user finds a wallet on the street" into the generative AI model. Based on this input, the generative AI model outputs the situation "You found a wallet on the street. What would you do?". Furthermore, the server adds options to this output situation, such as "Take it to the police station," "Keep it for yourself," and "Ignore it and walk away."

[0318] Step 2:

[0319] The server sends the generated situation and options to the terminal. The server then sends the generated situation and options in packet format to a specific user ID within the system. This operation completes data transmission from the server to the terminal.

[0320] Step 3:

[0321] The device displays the received situation and options to the user. The device displays the situation "You found a wallet on the street. What would you do?" on the user interface along with options such as "Take it to the police station," "Keep it," and "Ignore it and walk away." This display allows the user to visually see the options for the situation.

[0322] Step 4:

[0323] The user inputs an answer to the situation. The user selects an option on the terminal, such as "deliver to the police station," and inputs that selection on the interface. This input records the user's selected answer on the terminal.

[0324] Step 5:

[0325] The device sends the user's answer and emotional data to the server. The device then sends the answer selected by the user and emotional data collected by the emotion engine, such as facial expressions, tone of voice, and input speed, in packet format to the server. This transmitted data includes information such as "User ID 12345: Option A (report to the police station), Emotion: Calm."

[0326] Step 6:

[0327] The server analyzes the user's responses and emotional data and assigns a score. The server analyzes the received data and evaluates the user's ethical judgment using a generative AI model. For example, the option "Take it to the police station" is given 50 points, and a calm emotional state is given 40 points, for a total score of 90 points.

[0328] Step 7:

[0329] The server determines the user's rank based on the score and generates reward information. The server assigns a "high rank" to users with a score of 90 or more, and generates reward information such as "tax reduction" or "priority support in the event of a disaster" as appropriate benefits. This reward information is stored in the user ID.

[0330] Step 8:

[0331] The server sends the generated reward information to the terminal. The server sends the reward information corresponding to the specific user ID to the terminal in packet format. This operation completes the data transmission from the server to the terminal.

[0332] Step 9:

[0333] The terminal displays the reward information to the user. The terminal displays reward information such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" on the user interface. This allows the user to check their own evaluation results and reward information.

[0334] (Application example 2)

[0335] 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."

[0336] Conventional ethical judgment evaluation systems calculate scores based solely on the user's responses, ignoring the user's emotional state and the psychological background of the situation. This makes it difficult to achieve truly fair and balanced evaluations. Furthermore, because reward information is determined solely based on the score, it cannot be said to be appropriate and reflects the user's actual behavior and emotions. In such cases, particularly in the field of security services, where immediate response and calmness on the spot are required, conventional systems are unable to provide sufficient evaluations.

[0337] The identification process by the identification 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 automatically generating an ethical scenario using a generative AI model, means for displaying the generated ethical scenario on a user device, means for collecting user responses to the ethical scenario, means for evaluating the responses and scoring the user's ethical judgment, means for analyzing the user's emotional state using an emotion recognition engine, means for setting the user's rank based on the score and emotional data, means for generating reward information according to the user's rank, and means for displaying the reward information on the user device. This makes it possible to comprehensively evaluate the user's ethical behavior and the emotional state at the time and provide more fair and appropriate reward information.

[0338] A "generative AI model" is a type of algorithm that automatically generates specific scenarios or sentences based on artificial intelligence.

[0339] An "ethical scenario" is a scenario that depicts a moral or ethical issue or situation that a user may face.

[0340] A "user device" is a terminal device operated by a user, and includes a computer, a smartphone, a tablet, smart glasses, a head-mounted display, etc.

[0341] A "means for collecting responses" is a mechanism for receiving user input via a user device and storing it in a database.

[0342] "Scoring means" is a system that evaluates the user's answers and assigns them points or evaluation values.

[0343] An "emotion recognition engine" is an algorithm that analyzes data such as a user's facial expression, tone of voice, and input speed to identify the user's emotional state.

[0344] The "means for setting a rank" is a mechanism for determining and classifying a user's evaluation rank based on the user's score and emotional data.

[0345] "Benefit information" is information about rewards and systems provided according to the user's rank.

[0346] This invention is a system that automatically generates ethical scenarios using a generative AI model. The system consists of a server, a terminal (user device), and an emotion recognition engine that recognizes the user's emotions. This system evaluates the user's ethical judgment and emotional state, and generates and displays reward information based on the results.

[0347] System Program

[0348] The server uses a generative AI model to generate ethical scenarios that depict moral or ethical issues or situations that users may encounter. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and then sent to the user's device.

[0349] The device (smartphone, smart glasses, head-mounted display, etc.) displays an ethical scenario sent from the server. The user responds to the scenario through the device's interface, expressing their actions and opinions. For example, in response to the question, "What would you do if you found a wallet on the street?", the user can select an option such as "Take it to the police station."

[0350] When a user answers a scenario, the emotion recognition engine analyzes the user's facial expressions, tone of voice, typing speed, etc. in real time to identify the user's emotional state. This information is sent to the server and evaluated along with the answer data. For example, if a user answers "deliver it to the police station" in a calm state, a high score will be assigned.

[0351] The server analyzes the user's answers received from the device and the emotional data received from the emotion engine. Using a generative AI model, it evaluates whether the answers and emotional data are ethical, moral, and fair. Based on the evaluation score, the server determines the user's rank. Users with high scores are given a "high rank" and are given rewards such as "tax reductions" and "priority support in the event of a disaster."

[0352] The generated reward information is retransmitted to the terminal and displayed to the user, thereby enabling the user's ethical behavior and emotional state to be accurately evaluated and reward information to be provided to the user based on the evaluation.

[0353] Hardware and software used

[0354] Hardware: Smartphone, smart glasses, head-mounted display, camera (for emotion recognition)

[0355] Software: OpenAI API (generative AI model), Emotion Recognition Library (emotion recognition), OpenCV (image processing)

[0356] Specific examples

[0357] 1. Example of a prompt for scenario generation

[0358] "Generate an ethical scenario of a situation a security guard might face. The scenario should include a situation in which a suspicious individual attempts to enter a staff area while on patrol."

[0359] 2. Example Scenario

[0360] "While on patrol, you notice an unknown individual attempting to enter a staff area. How would you respond?"

[0361] 3. Example user responses

[0362] "Stop them immediately and ask them to show their identification."

[0363] This system will help security guards make more ethical and fair decisions, improving their ability to respond immediately when dealing with problems.

[0364] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0365] Step 1:

[0366] The server uses a generative AI model to generate an ethical scenario. The server is given the prompt, "Generate an ethical scenario for a situation a security guard might face. Include a situation in which a suspicious person is attempting to enter a staff area while on patrol." The generated scenario might be text like, "While on patrol, you notice an unknown person attempting to enter a staff area. How would you respond?" The generated scenario is stored on the server and prepared for transmission to the terminal.

[0367] Step 2:

[0368] The device receives the ethical scenario sent from the server and displays it to the user. The user responds to the scenario through the device's interface, expressing their actions and opinions. The input response data is obtained in a format such as "Stop the person immediately and ask for identification" and is sent from the device to the server.

[0369] Step 3:

[0370] As the user answers the scenario, the emotion recognition engine uses a camera connected to the device to analyze data such as the user's facial expressions, tone of voice, and typing speed in real time. This allows the user's emotional state (e.g., calm or nervous) to be identified. This emotional data is then sent from the device to the server.

[0371] Step 4:

[0372] The server analyzes the user's response data received from the device and the emotion data from the emotion recognition engine. Utilizing a generative AI model, it evaluates whether the response is ethical and fair and assigns a score (e.g., 85 points for the content of the response, 90 points for the emotional state). Based on this, it calculates an overall evaluation score. This score data is used in subsequent processing.

[0373] Step 5:

[0374] The server determines the user's rank based on the evaluation score. For example, if the score is 80 or more, it sets the user as "high rank." This rank data is used to generate rewards.

[0375] Step 6:

[0376] The server generates special benefit information according to the user's rank. For example, special benefit information such as "Your rank is high. You will receive reduced taxes and priority assistance in the event of a disaster" is generated. This special benefit information is stored in the server and transmitted to the terminal.

[0377] Step 7:

[0378] The terminal receives the reward information sent from the server and displays it to the user, allowing the user to receive evaluations and rewards based on their own behavior and emotional state.

[0379] 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.

[0380] 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.

[0381] 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.

[0382] [Second embodiment]

[0383] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0384] 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.

[0385] 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).

[0386] 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.

[0387] 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.

[0388] 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).

[0389] 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.

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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."

[0395] This system automatically generates ethical scenarios using a generative AI model and evaluates and ranks user responses. This system consists of three main components: a server, a terminal, and a user.

[0396] System Overview

[0397] Server Operation

[0398] The server first uses a generative AI model to generate ethical scenarios, which depict ethical issues and situations that users may face. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and prepared for transmission to user devices.

[0399] Device behavior

[0400] The user's device (computer, smartphone, tablet, etc.) displays an ethical scenario sent from the server. The user responds through the device's interface regarding their actions and opinions regarding the scenario. For example, in response to the question, "What would you do if you found a wallet on the street?", the user could select an option such as "Take it to the police station."

[0401] User Actions

[0402] The user selects their own ethical judgments and actions for the scenarios on the terminal and inputs their answers, which are then sent to the server via the terminal.

[0403] Server Ratings and Rankings

[0404] The server receives the user's answers sent from the device and evaluates them using a generative AI model. Based on the evaluation results, the user's ethical judgments and actions are scored. For example, if the choice of "deliver to the police station" is highly evaluated, the user will be given a high score.

[0405] Based on the score, the server determines the user's rank. For example, a user with a score of 90 or more is assigned a "high rank." After the rank is assigned, the server generates reward and program information appropriate for the user.

[0406] Terminal display

[0407] The generated benefit information is sent to the terminal again and displayed to the user. For example, information such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" is displayed.

[0408] Specific examples

[0409] 1. Server-side scenario generation: The generative AI model generates the scenario: "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away.

[0410] 2. Displaying scenarios on the terminal: The above scenarios and options are displayed to the user.

[0411] 3. User response: The user selects "Deliver to the police station."

[0412] 4. Server evaluation: The server analyzes the answer "deliver to the police station" and gives it a high evaluation score (for example, 90 points).

[0413] 5. Rank determination and reward generation: The server will set a "high rank" based on the score and generate rewards such as "tax reduction" or "priority support in the event of a disaster."

[0414] 6. Displaying rewards on the device: Display the generated reward information to the user.

[0415] This system makes it possible to accurately evaluate users' ethical behavior and provide fair treatment based on that evaluation.

[0416] The processing flow will be explained below.

[0417] Step 1:

[0418] The server runs the generative AI model to generate new ethical scenarios. The generated scenarios are stored in an internal database. For example, a scenario might be generated: "You found a wallet on the street. What would you do?"

[0419] Step 2:

[0420] The server prepares to send the generated scenario to the terminal. This scenario contains multiple options.

[0421] Step 3:

[0422] The terminal displays the scenario received from the server to the user. For example, a scenario such as "You found a wallet on the street. It has a lot of cash inside. What would you do?" is displayed.

[0423] Step 4:

[0424] The user reads the displayed scenario and selects an action from the options presented. For example, they can select "deliver to the police station."

[0425] Step 5:

[0426] The terminal records the user's selection and transmits the information to the server.

[0427] Step 6:

[0428] The server analyzes the responses received from users and uses a generative AI model to evaluate whether the responses are ethical, moral, and fair.

[0429] Step 7:

[0430] The server calculates the user's score based on the evaluation results. For example, the option "Deliver to the police station" is highly rated and is set to a score of 90.

[0431] Step 8:

[0432] The server assigns a rank to the user based on the calculated score. For example, if the user has a score of 90 or above, the server assigns a "high rank."

[0433] Step 9:

[0434] The server generates reward information according to the user's rank. High-ranking users are given "tax reductions" and "priority support in the event of a disaster."

[0435] Step 10:

[0436] The server transmits the generated bonus information to the terminal.

[0437] Step 11:

[0438] The terminal displays the benefit information received from the server to the user, for example, "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster."

[0439] This series of steps creates a system in which users' ethical behavior and judgments are evaluated and appropriate treatment is provided based on that evaluation.

[0440] Example 1

[0441] 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."

[0442] In modern society, the ethical judgment of individuals is highly valued, and there is a growing demand for systems that evaluate such judgments and provide appropriate social responses and rewards. However, there is a lack of means to objectively and efficiently evaluate users' ethical judgments. There is also a need for a system that can appropriately rank users based on the evaluation results and quickly provide reward information according to their rank.

[0443] 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.

[0444] In this invention, the server includes means for automatically generating an ethical scenario using a generative AI model, means for displaying the generated ethical scenario on a user terminal, means for collecting users' answers to the ethical scenario, means for evaluating the answers and scoring the users' ethical judgments, means for setting the users' ranks based on the scores, means for generating reward information according to the users' ranks, and means for displaying the reward information on the user terminal. This makes it possible to objectively evaluate the users' ethical judgments and provide appropriate responses and rewards based on the evaluation.

[0445] A "generative AI model" is an artificial intelligence model that uses advanced algorithms to generate text and automatically create ethical scenarios that users may encounter.

[0446] An "ethical scenario" is a scenario that specifically depicts moral and ethical issues or situations that users may face.

[0447] "User terminal" refers to an electronic device, such as a computer, smartphone, or tablet, that a user operates to input and display information.

[0448] "Means for collecting responses" refers to a system that allows users to select their opinions and actions regarding ethical scenarios, and then electronically captures and records them.

[0449] "Scoring means" refers to a system that evaluates answers provided by users and assigns them a score or rating based on pre-defined criteria.

[0450] "Means for setting a rank" refers to a system for determining the ranking or rating of a user based on the scoring results and assigning a rank.

[0451] The "means for generating benefit information" refers to a system that determines the content of the benefits and systems that will be applied depending on the user's rank and creates that information.

[0452] The "means for displaying special benefit information" refers to a system that transmits the generated special benefit information to a user terminal and displays it in a form that can be understood by the user.

[0453] This invention describes a system consisting of three main elements: a server, a terminal, and a user, which automatically generates ethical scenarios using a generative AI model and evaluates and ranks the user's answers.

[0454] Server Operation

[0455] The server first uses a generative AI model (e.g., OpenAI's GPT-4) to generate ethical scenarios that users may encounter. These scenarios are presented to users in the form of depictions of moral and ethical issues and situations. The generated scenarios are classified according to difficulty level (beginner, intermediate, advanced, etc.) and stored in a database on the server.

[0456] Specifically, it is generated using the following prompt:

[0457] “You find a wallet on the street and it has a lot of cash inside, what do you do?

[0458] A. Take it to the police station

[0459] B. Make it your own

[0460] C. Leave it there and walk away.”

[0461] Device behavior

[0462] The user's device (computer, smartphone, tablet, etc.) receives and displays the ethical scenario sent from the server. The user selects actions and opinions regarding this scenario through the device's interface. For example, the user can choose an action such as "deliver to the police station" from the options.

[0463] User Actions

[0464] The user makes their own ethical judgments about the scenarios displayed on the device and selects the most appropriate option from the options. The answer is then sent to the server via the device.

[0465] Server Ratings and Rankings

[0466] The server receives the user's answers sent from the device and evaluates them using a generative AI model. Based on the evaluation results, the user's ethical judgments and actions are scored, and for example, the option "deliver to the police station" is given a high score (e.g., 90 points). Based on this score, the server determines the user's rank. Specifically, users with a score above a certain level are assigned a "high rank." The server also generates reward information (e.g., tax reductions, priority support in the event of a disaster) according to the user's rank.

[0467] Terminal display

[0468] The generated reward information is sent back to the user's terminal and displayed to the user. For example, a message such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" is displayed to the user.

[0469] Specific examples

[0470] 1. Server-side scenario generation: The server uses a generative AI model to generate a scenario: "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away.

[0471] 2. Displaying scenarios on the terminal: The above scenarios and options are displayed to the user.

[0472] 3. User response: The user selects "Deliver to the police station."

[0473] 4. Server evaluation: The server analyzes the answer "deliver to the police station" and gives it a high evaluation score (for example, 90 points).

[0474] 5. Rank determination and reward generation: The server will set a "high rank" based on the score and generate rewards such as "tax reduction" or "priority support in the event of a disaster."

[0475] 6. Displaying rewards on the device: Display the generated reward information to the user.

[0476] This system makes it possible to fairly and efficiently evaluate users' ethical behavior and provide appropriate treatment and benefits based on that evaluation.

[0477] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0478] Step 1:

[0479] Server-based scenario generation

[0480] Input: Prompt sentence: "You find a wallet on the street. It has a large amount of cash inside. What do you do?"

[0481] The server uses a generative AI model to automatically generate ethical scenarios that users may encounter, which include multiple choices and are categorized according to difficulty level: beginner, intermediate, or advanced.

[0482] Output: Generated ethical scenario (e.g., "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away)

[0483] Specific operation: The server inputs the prompt sentence into the generative AI model and saves the generated scenario in the database.

[0484] Step 2:

[0485] Sending scenarios from the server to the device

[0486] Input: Ethical scenario stored on the server

[0487] The server sends the generated scenario to the user's terminal.

[0488] Output: Scenario data sent to the user's device

[0489] Specific operation: The server retrieves the scenario from the database and sends it to the user's terminal.

[0490] Step 3:

[0491] Displaying scenarios on a device

[0492] Input: Scenario data sent from the server

[0493] The user's terminal displays the ethical scenario received from the server.

[0494] Output: The scenario displayed on the terminal screen

[0495] Specific operation: The terminal analyzes the scenario data received from the server and displays it on the screen.

[0496] Step 4:

[0497] User selection and response submission

[0498] Input: Scenario and options displayed on the terminal

[0499] The user makes their own ethical judgment about the scenario displayed on the terminal, selects an option, and submits their answer.

[0500] Output: The answer selected by the user

[0501] Specific operation: The user selects an option such as "deliver to the police station" and sends the answer to the server via the terminal.

[0502] Step 5:

[0503] Server-based answer evaluation and scoring

[0504] Input: User-selected answer data

[0505] The server evaluates the user's answers received from the device and scores them using a generative AI model.

[0506] Output: User's rating score for ethical judgment

[0507] Specific operation: The server inputs the user's answer into the generative AI model, analyzes it, and stores the evaluation score (e.g., "Take it to the police station" gets 90 points) in the database.

[0508] Step 6:

[0509] Server determines rank and generates rewards

[0510] Input: Evaluation score data

[0511] The server determines the rank of the user based on the evaluation score and generates bonus information according to the rank.

[0512] Output: User rank and benefits information (e.g., high rank, tax reduction, priority support in case of disaster)

[0513] Specific operation: The server analyzes the score, sets the user as "high rank", and generates reward information.

[0514] Step 7:

[0515] Sending bonus information from the server to the device

[0516] Input: Bonus information generated on the server

[0517] The server transmits the generated bonus information to the user's terminal.

[0518] Output: Reward information data sent to the user device

[0519] Specific operation: The server sends the reward information to the user's terminal.

[0520] Step 8:

[0521] Displaying bonus information on your device

[0522] Input: Bonus information data sent from the server

[0523] The user's terminal displays the bonus information received from the server.

[0524] Output: Reward information displayed on the device screen

[0525] Specific operation: The terminal analyzes the bonus information data received from the server and displays it on the screen.

[0526] (Application example 1)

[0527] 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."

[0528] Conventional ethical education and assessment systems lack effective and enjoyable methods for helping users learn ethical judgment. Furthermore, conventional systems only present simple scenarios and collect answers, and do not sufficiently motivate users to take actual action. In other words, innovative methods are needed to encourage users to behave ethically and to learn in an enjoyable way.

[0529] 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.

[0530] In this invention, the server includes means for automatically generating an ethical scenario using a generative AI model, means for displaying the generated ethical scenario on a user device, means for collecting user responses to the ethical scenario, means for evaluating the responses and scoring the user's ethical judgment, means for setting the user's rank based on the score, means for generating reward information according to the user's rank, means for displaying the reward information on the user device, and means for providing an interface in a virtual store where users can respond based on the ethical scenario and automatically awarding rewards such as points or coupons according to the responses. This allows users to learn about ethics while having fun, and the rewards increase their motivation, which is expected to lead to a high level of learning effectiveness.

[0531] A "generative AI model" is an artificial intelligence technology that automatically generates ethical problems and situations that users may face.

[0532] An "ethical scenario" is a hypothetical scenario that depicts an ethical problem or situation that a user is asked to solve.

[0533] A "user device" is a terminal such as a computer, smartphone, or tablet that a user uses to access the system.

[0534] The "answer collection means" is a function that allows users to input and record answers to ethical scenarios on the device.

[0535] The "evaluation means" is a function that analyzes the user's responses and scores the accuracy and appropriateness of the ethical judgment based on the results.

[0536] The "scoring means" is a function that evaluates the user's answers analyzed by the evaluation means as numerical values ​​or grades.

[0537] The "rank setting means" is a function that determines the rank of a user based on the score calculated by the scoring means.

[0538] The "privilege information generating means" is a function that generates privilege information such as points and coupons according to the rank of the user.

[0539] A "virtual store" is an environment where users can virtually shop in a digital space.

[0540] The "interface providing means" is a function that provides a screen and an operation method for the user to input answers to the scenario.

[0541] System Overview

[0542] Server Operation

[0543] The server first uses a generative AI model to automatically generate ethical scenarios. The generated scenarios depict ethical issues and situations that users may encounter in their daily lives. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and prepared for transmission to the user's device.

[0544] Device behavior

[0545] The user's device (such as a smartphone or head-mounted display) displays an ethical scenario sent from the server. The user responds to the scenario through the device's interface, expressing their actions and opinions. For example, in response to the scenario, "You found a wallet at the cash register. What would you do?", the user would select an option such as "Take it to the clerk."

[0546] User Actions

[0547] The user selects their own ethical judgment and actions for the scenario displayed on the terminal and transmits their response via the terminal.

[0548] Server Ratings and Rankings

[0549] The server receives the user's answers sent from the device and evaluates them using a generative AI model. Based on the evaluation results, the user's ethical judgments and actions are scored. For example, if the choice of "deliver to the store clerk" is highly evaluated, the user is given a high score. Based on the score, the server determines the user's rank. For example, a user with a score of 90 or above is assigned a "high rank."

[0550] Generate and view rewards

[0551] The server generates reward information according to the rank and sends it to the user device. The reward information is displayed on the user device. For example, a notification such as "Your rank is high. You have been awarded a reward of 100 points" is displayed.

[0552] Specific examples

[0553] 1. Server-side scenario generation:

[0554] The generative AI model generates the following scenario: "You find a wallet on the street. It has a large amount of cash inside. What would you do?"

[0555] Option A: Give it to the store clerk

[0556] Option B: Make it your own

[0557] Option C: Leave it and walk away

[0558] 2. Display scenario on device:

[0559] The above scenario and options are displayed on the user's terminal.

[0560] 3. User Answer:

[0561] The user selects "deliver to store clerk."

[0562] 4. Server Rating:

[0563] The server analyzes the answer "deliver to the store clerk" and gives a high evaluation score (for example, 90 points).

[0564] 5. Rank determination and reward generation:

[0565] The server will set a "high rank" based on the score and generate "100 points" as a reward.

[0566] 6. Displaying the benefits on your device:

[0567] The generated benefit information is displayed to the user.

[0568] Prompt Sentence Examples

[0569] You found a wallet on the street. What would you do?

[0570] 1. Report it to the police station

[0571] 2. Make it your own

[0572] 3. Leave it there and walk away

[0573] This system allows users to learn ethically while having fun, and the rewards it provides increase motivation, leading to the expectation of high learning outcomes.

[0574] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0575] Step 1:

[0576] The server automatically generates ethical scenarios using a generative AI model. The server inputs the prompt "Generate ethical issues that users may face" into the generative AI model to obtain ethical scenarios. The generated scenarios are classified into beginner, intermediate, and advanced levels of difficulty.

[0577] Input: prompt statement

[0578] Output: Ethical scenarios (with difficulty classification)

[0579] Step 2:

[0580] The server sends the generated ethical scenario to the user's device, where it is displayed on the user's connected smartphone or head-mounted display.

[0581] Input: Ethical scenario

[0582] Output: Scenario display on user terminal

[0583] Step 3:

[0584] The user inputs their response to the scenario through the interface on the device. As a specific action, the user selects the appropriate answer from the presented options. For example, in response to the question, "You found a wallet on the street. What would you do?", the user selects "Take it to the police station."

[0585] Input: Scenario choices

[0586] Output: User's answer

[0587] Step 4:

[0588] The device sends the user's answer to the server, which analyzes the received answer and evaluates it using a generative AI model. The evaluation process includes checking against a surrounding knowledge base and comparing it with similar answers from the past.

[0589] Input: User's answer

[0590] Output: Evaluation result (score)

[0591] Step 5:

[0592] The server calculates the user's score based on the evaluation results and assigns a rank according to that score. For example, if the score is 90 or above, it will be assigned a "high rank."

[0593] Input: Evaluation result (score)

[0594] Output: User's rank

[0595] Step 6:

[0596] The server generates reward information (points, coupons, etc.) according to the user's rank. The type and amount of reward information varies depending on the rank.

[0597] Input: User rank

[0598] Output: Bonus information

[0599] Step 7:

[0600] The server transmits the generated reward information to the user's terminal, which then displays it to the user. The displayed reward is visualized in a concrete form such as points or coupons.

[0601] Input: Bonus information

[0602] Output: Display rewards on user device

[0603] 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.

[0604] This invention is a system that automatically generates ethical scenarios using a generative AI model, evaluates and ranks user responses to those scenarios, and recognizes the user's emotions and reflects them in the evaluation and ranking. This system consists of four main components: a server, a terminal (user device), a user, and an emotion engine.

[0605] System Overview

[0606] Server Operation

[0607] The server first uses a generative AI model to generate ethical scenarios, which depict ethical issues and situations that users may face. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and prepared for transmission to user devices.

[0608] Device behavior

[0609] The user's device (computer, smartphone, tablet, etc.) displays an ethical scenario sent from the server. The user responds through the device's interface regarding their actions and opinions regarding the scenario. For example, in response to the question, "What would you do if you found a wallet on the street?", the user could select an option such as "Take it to the police station."

[0610] User Actions

[0611] The user selects their own ethical judgment and behavior for the scenario on the device and inputs their response. The response is sent to the server via the device. When the user responds, the emotion engine recognizes the user's emotions from their facial expression, tone of voice, input speed, etc.

[0612] Emotion Engine Operation

[0613] The emotion engine analyzes the emotions of users as they answer ethical scenarios. This engine analyzes the user's facial expressions, tone of voice, typing speed, etc. in real time to identify the user's emotional state. For example, it can recognize whether the user is nervous or calm when answering. The emotion engine sends this information to the server.

[0614] Server Ratings and Rankings

[0615] The server analyzes the user's answers received from the device and the user's emotional data received from the emotion engine. Using a generative AI model, the server evaluates whether the answers and emotional data are ethical, moral, and fair. For example, the choice "Take it to the police station" is highly evaluated and assigned a high score. Furthermore, if the server recognizes that the user responded calmly, the score may be even higher.

[0616] Based on the score, the server determines the user's rank. For example, a user with a score of 90 or more is assigned a "high rank." After the rank is assigned, the server generates reward and program information appropriate for the user.

[0617] Terminal display

[0618] The generated benefit information is sent to the terminal again and displayed to the user. For example, information such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" is displayed.

[0619] Specific examples

[0620] 1. Server-side scenario generation: The generative AI model generates the scenario: "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away.

[0621] 2. Displaying scenarios on the terminal: The above scenarios and options are displayed to the user.

[0622] 3. User response and emotion recognition: The user selects "Deliver to the police station." At the same time, the emotion engine recognizes the user's calm emotion from their facial expression and tone of voice.

[0623] 4. Server evaluation: The server analyzes the answer "Take it to the police station" and the emotional data that the user answered calmly, and gives a high evaluation score (for example, 90 points).

[0624] 5. Rank determination and reward generation: The server will set a "high rank" based on the score and generate rewards such as "tax reduction" or "priority support in the event of a disaster."

[0625] 6. Displaying rewards on the device: Display the generated reward information to the user.

[0626] This system makes it possible to accurately assess users' ethical behavior and emotional state and provide fair treatment based on that assessment.

[0627] The processing flow will be explained below.

[0628] Step 1:

[0629] The server runs the generative AI model to generate new ethical scenarios. The generated scenarios are stored in an internal database. For example, a scenario might be generated: "You found a wallet on the street. What would you do?"

[0630] Step 2:

[0631] The server prepares to send the generated scenario to the terminal. This scenario contains multiple options.

[0632] Step 3:

[0633] The terminal displays the scenario received from the server to the user. For example, a scenario such as "You found a wallet on the street. It has a lot of cash inside. What would you do?" is displayed.

[0634] Step 4:

[0635] The user reads the displayed scenario and selects an action from the options presented. For example, they select "deliver to the police station." At this time, the emotion engine recognizes the user's emotions in real time from their facial expressions, tone of voice, typing speed, etc.

[0636] Step 5:

[0637] The terminal transmits the user's selection together with the emotion data recognized by the emotion engine to the server.

[0638] Step 6:

[0639] The server analyzes the response data and sentiment data received from users and uses a generative AI model to evaluate whether the content and sentiment of the responses are ethical, moral, and fair.

[0640] Step 7:

[0641] The server calculates the user's score based on the evaluation results. For example, if the choice "deliver to the police station" is highly rated, the user will be given a high score. If the user answers calmly, the score may be even higher.

[0642] Step 8:

[0643] The server assigns a rank to the user based on the calculated score. For example, if the user has a score of 90 or above, the server assigns a "high rank."

[0644] Step 9:

[0645] The server generates reward information according to the user's rank. High-ranking users are given "tax reductions" and "priority support in the event of a disaster."

[0646] Step 10:

[0647] The server transmits the generated bonus information to the terminal.

[0648] Step 11:

[0649] The terminal displays the benefit information received from the server to the user, for example, "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster."

[0650] This series of steps creates a system in which users' ethical behavior and judgments are evaluated and appropriate treatment is provided based on that evaluation.

[0651] Example 2

[0652] 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."

[0653] Conventional systems lack fairness and accuracy because they do not consider emotional data when evaluating users' ethical judgments. Another issue is the lack of systems that not only simply score ethical judgments but also provide appropriate feedback and rewards based on those scores. Furthermore, if the difficulty of the generated scenarios is not appropriate for the user's ability, this could affect the reliability of the evaluation.

[0654] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0655] In this invention, the server includes means for automatically generating ethical situations using a generative AI model, means for presenting the generated ethical situations on a user interface, means for collecting responses and emotional data to users' ethical judgments, means for analyzing the responses and emotional data and evaluating and scoring the users' ethical judgments, means for setting the users' ranks based on the scores, means for generating reward information according to the users' ranks, and means for displaying the reward information on the user interface. This improves the accuracy and fairness of evaluations based on the users' ethical judgments, and makes it possible to provide users with feedback and rewards appropriate to them.

[0656] A "generative AI model" is a machine learning algorithm that performs human-like natural language generation based on input prompts.

[0657] An "ethical situation" is a situation or occasion that a user may encounter that requires a moral or ethical decision.

[0658] A "user interface" refers to the interaction that includes a screen and input devices that allow a user to interact with a device or system.

[0659] An "answer" is an option or opinion that a user selects and enters in response to a presented ethical situation.

[0660] "Emotional data" is information about the emotional state of the user, such as facial expressions, tone of voice, and input speed, that is collected when the user answers.

[0661] "Analysis" is the process of statistically or computationally analyzing collected data to extract meaningful information.

[0662] "Scoring" is the process of assigning a score according to specific evaluation criteria based on the user's answers and emotional data.

[0663] "Rank" refers to the evaluation level of a user that is determined based on the score.

[0664] "Reward information" is information about benefits and profits provided according to the user's rank.

[0665] This invention is a system that automatically generates ethical situations using a generative AI model, evaluates and scores users' responses to those situations, and generates and presents reward information. The system consists of four main components: a server, a terminal (user device), a user, and an emotion engine.

[0666] The server first generates ethical situations using a generative AI model. Examples of such models include ChatGPT. Situations are generated by inputting prompts into the model. For example, a prompt such as "Imagine a situation where a user finds a wallet on the street" can be used. Scenarios generated by this model are classified according to difficulty level (beginner, intermediate, or advanced) and saved on the server.

[0667] The server then sends the generated situation to the device, which then displays it on its user interface. For example, the device displays a situation such as "You found a wallet on the street. What would you do?" along with options such as "Take it to the police station," "Keep it," and "Ignore it and walk away."

[0668] The user selects and inputs their own response to a given situation on the device. At this time, the emotion engine analyzes the user's facial expression, tone of voice, input speed, etc. in real time to collect emotional data. This emotion engine uses, for example, emotion recognition software and sensor devices.

[0669] The device sends the user's selected answer and emotional data to the server. The server then statistically or computationally analyzes this data and uses a generative AI model to evaluate and score the user's ethical judgment. For example, the option "Take it to the police station" will receive a high score, and a calm emotional state will receive additional points.

[0670] The server determines the user's rank based on this score and generates reward information accordingly. For example, a user with a score of 90 or more will be assigned a "high rank" and be given special benefits such as "tax reductions" and "priority support in the event of a disaster." This reward information is then sent back to the terminal.

[0671] Finally, the terminal displays the generated reward information to the user on a user interface, such as a message saying, "Your rank is high. You will receive reduced taxes and priority assistance in the event of a disaster."

[0672] In this way, the server, terminal, user, and emotion engine work together to realize a system in which evaluations are made based on the user's ethical judgment and fair rewards are provided.

[0673] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0674] Step 1:

[0675] The server uses a generative AI model to generate ethical situations. The server inputs the prompt "Imagine a situation where a user finds a wallet on the street" into the generative AI model. Based on this input, the generative AI model outputs the situation "You found a wallet on the street. What would you do?". Furthermore, the server adds options to this output situation, such as "Take it to the police station," "Keep it for yourself," and "Ignore it and walk away."

[0676] Step 2:

[0677] The server sends the generated situation and options to the terminal. The server then sends the generated situation and options in packet format to a specific user ID within the system. This operation completes data transmission from the server to the terminal.

[0678] Step 3:

[0679] The device displays the received situation and options to the user. The device displays the situation "You found a wallet on the street. What would you do?" on the user interface along with options such as "Take it to the police station," "Keep it," and "Ignore it and walk away." This display allows the user to visually see the options for the situation.

[0680] Step 4:

[0681] The user inputs an answer to the situation. The user selects an option on the terminal, such as "deliver to the police station," and inputs that selection on the interface. This input records the user's selected answer on the terminal.

[0682] Step 5:

[0683] The device sends the user's answer and emotional data to the server. The device then sends the answer selected by the user and emotional data collected by the emotion engine, such as facial expressions, tone of voice, and input speed, in packet format to the server. This transmitted data includes information such as "User ID 12345: Option A (report to the police station), Emotion: Calm."

[0684] Step 6:

[0685] The server analyzes the user's responses and emotional data and assigns a score. The server analyzes the received data and evaluates the user's ethical judgment using a generative AI model. For example, the option "Take it to the police station" is given 50 points, and a calm emotional state is given 40 points, for a total score of 90 points.

[0686] Step 7:

[0687] The server determines the user's rank based on the score and generates reward information. The server assigns a "high rank" to users with a score of 90 or more, and generates reward information such as "tax reduction" or "priority support in the event of a disaster" as appropriate benefits. This reward information is stored in the user ID.

[0688] Step 8:

[0689] The server sends the generated reward information to the terminal. The server sends the reward information corresponding to the specific user ID to the terminal in packet format. This operation completes the data transmission from the server to the terminal.

[0690] Step 9:

[0691] The terminal displays the reward information to the user. The terminal displays reward information such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" on the user interface. This allows the user to check their own evaluation results and reward information.

[0692] (Application example 2)

[0693] 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."

[0694] Conventional ethical judgment evaluation systems calculate scores based solely on the user's responses, ignoring the user's emotional state and the psychological background of the situation. This makes it difficult to achieve truly fair and balanced evaluations. Furthermore, because reward information is determined solely based on the score, it cannot be said to be appropriate and reflects the user's actual behavior and emotions. In such cases, particularly in the field of security services, where immediate response and calmness on the spot are required, conventional systems are unable to provide sufficient evaluations.

[0695] The identification process by the identification 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 automatically generating an ethical scenario using a generative AI model, means for displaying the generated ethical scenario on a user device, means for collecting user responses to the ethical scenario, means for evaluating the responses and scoring the user's ethical judgment, means for analyzing the user's emotional state using an emotion recognition engine, means for setting the user's rank based on the score and emotional data, means for generating reward information according to the user's rank, and means for displaying the reward information on the user device. This makes it possible to comprehensively evaluate the user's ethical behavior and the emotional state at the time and provide more fair and appropriate reward information.

[0696] A "generative AI model" is a type of algorithm that automatically generates specific scenarios or sentences based on artificial intelligence.

[0697] An "ethical scenario" is a scenario that depicts a moral or ethical issue or situation that a user may face.

[0698] A "user device" is a terminal device operated by a user, and includes a computer, a smartphone, a tablet, smart glasses, a head-mounted display, etc.

[0699] A "means for collecting responses" is a mechanism for receiving user input via a user device and storing it in a database.

[0700] "Scoring means" is a system that evaluates the user's answers and assigns them points or evaluation values.

[0701] An "emotion recognition engine" is an algorithm that analyzes data such as a user's facial expression, tone of voice, and input speed to identify the user's emotional state.

[0702] The "means for setting a rank" is a mechanism for determining and classifying a user's evaluation rank based on the user's score and emotional data.

[0703] "Benefit information" is information about rewards and systems provided according to the user's rank.

[0704] This invention is a system that automatically generates ethical scenarios using a generative AI model. The system consists of a server, a terminal (user device), and an emotion recognition engine that recognizes the user's emotions. This system evaluates the user's ethical judgment and emotional state, and generates and displays reward information based on the results.

[0705] System Program

[0706] The server uses a generative AI model to generate ethical scenarios that depict moral or ethical issues or situations that users may encounter. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and then sent to the user's device.

[0707] The device (smartphone, smart glasses, head-mounted display, etc.) displays an ethical scenario sent from the server. The user responds to the scenario through the device's interface, expressing their actions and opinions. For example, in response to the question, "What would you do if you found a wallet on the street?", the user can select an option such as "Take it to the police station."

[0708] When a user answers a scenario, the emotion recognition engine analyzes the user's facial expressions, tone of voice, typing speed, etc. in real time to identify the user's emotional state. This information is sent to the server and evaluated along with the answer data. For example, if a user answers "deliver it to the police station" in a calm state, a high score will be assigned.

[0709] The server analyzes the user's answers received from the device and the emotional data received from the emotion engine. Using a generative AI model, it evaluates whether the answers and emotional data are ethical, moral, and fair. Based on the evaluation score, the server determines the user's rank. Users with high scores are given a "high rank" and are given rewards such as "tax reductions" and "priority support in the event of a disaster."

[0710] The generated reward information is retransmitted to the terminal and displayed to the user, thereby enabling the user's ethical behavior and emotional state to be accurately evaluated and reward information to be provided to the user based on the evaluation.

[0711] Hardware and software used

[0712] Hardware: Smartphone, smart glasses, head-mounted display, camera (for emotion recognition)

[0713] Software: OpenAI API (generative AI model), Emotion Recognition Library (emotion recognition), OpenCV (image processing)

[0714] Specific examples

[0715] 1. Example of a prompt for scenario generation

[0716] "Generate an ethical scenario of a situation a security guard might face. The scenario should include a situation in which a suspicious individual attempts to enter a staff area while on patrol."

[0717] 2. Example Scenario

[0718] "While on patrol, you notice an unknown individual attempting to enter a staff area. How would you respond?"

[0719] 3. Example user responses

[0720] "Stop them immediately and ask them to show their identification."

[0721] This system will help security guards make more ethical and fair decisions, improving their ability to respond immediately when dealing with problems.

[0722] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0723] Step 1:

[0724] The server uses a generative AI model to generate an ethical scenario. The server is given the prompt, "Generate an ethical scenario for a situation a security guard might face. Include a situation in which a suspicious person is attempting to enter a staff area while on patrol." The generated scenario might be text like, "While on patrol, you notice an unknown person attempting to enter a staff area. How would you respond?" The generated scenario is stored on the server and prepared for transmission to the terminal.

[0725] Step 2:

[0726] The device receives the ethical scenario sent from the server and displays it to the user. The user responds to the scenario through the device's interface, expressing their actions and opinions. The input response data is obtained in a format such as "Stop the person immediately and ask for identification" and is sent from the device to the server.

[0727] Step 3:

[0728] As the user answers the scenario, the emotion recognition engine uses a camera connected to the device to analyze data such as the user's facial expressions, tone of voice, and typing speed in real time. This allows the user's emotional state (e.g., calm or nervous) to be identified. This emotional data is then sent from the device to the server.

[0729] Step 4:

[0730] The server analyzes the user's response data received from the device and the emotion data from the emotion recognition engine. Utilizing a generative AI model, it evaluates whether the response is ethical and fair and assigns a score (e.g., 85 points for the content of the response, 90 points for the emotional state). Based on this, it calculates an overall evaluation score. This score data is used in subsequent processing.

[0731] Step 5:

[0732] The server determines the user's rank based on the evaluation score. For example, if the score is 80 or more, it sets the user as "high rank." This rank data is used to generate rewards.

[0733] Step 6:

[0734] The server generates special benefit information according to the user's rank. For example, special benefit information such as "Your rank is high. You will receive reduced taxes and priority assistance in the event of a disaster" is generated. This special benefit information is stored in the server and transmitted to the terminal.

[0735] Step 7:

[0736] The terminal receives the reward information sent from the server and displays it to the user, allowing the user to receive evaluations and rewards based on their own behavior and emotional state.

[0737] 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.

[0738] 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.

[0739] 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.

[0740] [Third embodiment]

[0741] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0742] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0743] 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).

[0744] 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.

[0745] 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.

[0746] 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).

[0747] 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.

[0748] 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.

[0749] 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.

[0750] 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.

[0751] 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.

[0752] 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."

[0753] This system automatically generates ethical scenarios using a generative AI model and evaluates and ranks user responses. This system consists of three main components: a server, a terminal, and a user.

[0754] System Overview

[0755] Server Operation

[0756] The server first uses a generative AI model to generate ethical scenarios, which depict ethical issues and situations that users may face. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and prepared for transmission to user devices.

[0757] Device behavior

[0758] The user's device (computer, smartphone, tablet, etc.) displays an ethical scenario sent from the server. The user responds through the device's interface regarding their actions and opinions regarding the scenario. For example, in response to the question, "What would you do if you found a wallet on the street?", the user could select an option such as "Take it to the police station."

[0759] User Actions

[0760] The user selects their own ethical judgments and actions for the scenarios on the terminal and inputs their answers, which are then sent to the server via the terminal.

[0761] Server Ratings and Rankings

[0762] The server receives the user's answers sent from the device and evaluates them using a generative AI model. Based on the evaluation results, the user's ethical judgments and actions are scored. For example, if the choice of "deliver to the police station" is highly evaluated, the user will be given a high score.

[0763] Based on the score, the server determines the user's rank. For example, a user with a score of 90 or more is assigned a "high rank." After the rank is assigned, the server generates reward and program information appropriate for the user.

[0764] Terminal display

[0765] The generated benefit information is sent to the terminal again and displayed to the user. For example, information such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" is displayed.

[0766] Specific examples

[0767] 1. Server-side scenario generation: The generative AI model generates the scenario: "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away.

[0768] 2. Displaying scenarios on the terminal: The above scenarios and options are displayed to the user.

[0769] 3. User response: The user selects "Deliver to the police station."

[0770] 4. Server evaluation: The server analyzes the answer "deliver to the police station" and gives it a high evaluation score (for example, 90 points).

[0771] 5. Rank determination and reward generation: The server will set a "high rank" based on the score and generate rewards such as "tax reduction" or "priority support in the event of a disaster."

[0772] 6. Displaying rewards on the device: Display the generated reward information to the user.

[0773] This system makes it possible to accurately evaluate users' ethical behavior and provide fair treatment based on that evaluation.

[0774] The processing flow will be explained below.

[0775] Step 1:

[0776] The server runs the generative AI model to generate new ethical scenarios. The generated scenarios are stored in an internal database. For example, a scenario might be generated: "You found a wallet on the street. What would you do?"

[0777] Step 2:

[0778] The server prepares to send the generated scenario to the terminal. This scenario contains multiple options.

[0779] Step 3:

[0780] The terminal displays the scenario received from the server to the user. For example, a scenario such as "You found a wallet on the street. It has a lot of cash inside. What would you do?" is displayed.

[0781] Step 4:

[0782] The user reads the displayed scenario and selects an action from the options presented. For example, they can select "deliver to the police station."

[0783] Step 5:

[0784] The terminal records the user's selection and transmits the information to the server.

[0785] Step 6:

[0786] The server analyzes the responses received from users and uses a generative AI model to evaluate whether the responses are ethical, moral, and fair.

[0787] Step 7:

[0788] The server calculates the user's score based on the evaluation results. For example, the option "Deliver to the police station" is highly rated and is set to a score of 90.

[0789] Step 8:

[0790] The server assigns a rank to the user based on the calculated score. For example, if the user has a score of 90 or above, the server assigns a "high rank."

[0791] Step 9:

[0792] The server generates reward information according to the user's rank. High-ranking users are given "tax reductions" and "priority support in the event of a disaster."

[0793] Step 10:

[0794] The server transmits the generated bonus information to the terminal.

[0795] Step 11:

[0796] The terminal displays the benefit information received from the server to the user, for example, "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster."

[0797] This series of steps creates a system in which users' ethical behavior and judgments are evaluated and appropriate treatment is provided based on that evaluation.

[0798] Example 1

[0799] 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."

[0800] In modern society, the ethical judgment of individuals is highly valued, and there is a growing demand for systems that evaluate such judgments and provide appropriate social responses and rewards. However, there is a lack of means to objectively and efficiently evaluate users' ethical judgments. There is also a need for a system that can appropriately rank users based on the evaluation results and quickly provide reward information according to their rank.

[0801] 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.

[0802] In this invention, the server includes means for automatically generating an ethical scenario using a generative AI model, means for displaying the generated ethical scenario on a user terminal, means for collecting users' answers to the ethical scenario, means for evaluating the answers and scoring the users' ethical judgments, means for setting the users' ranks based on the scores, means for generating reward information according to the users' ranks, and means for displaying the reward information on the user terminal. This makes it possible to objectively evaluate the users' ethical judgments and provide appropriate responses and rewards based on the evaluation.

[0803] A "generative AI model" is an artificial intelligence model that uses advanced algorithms to generate text and automatically create ethical scenarios that users may encounter.

[0804] An "ethical scenario" is a scenario that specifically depicts moral and ethical issues or situations that users may face.

[0805] "User terminal" refers to an electronic device, such as a computer, smartphone, or tablet, that a user operates to input and display information.

[0806] "Means for collecting responses" refers to a system that allows users to select their opinions and actions regarding ethical scenarios, and then electronically captures and records them.

[0807] "Scoring means" refers to a system that evaluates answers provided by users and assigns them a score or rating based on pre-defined criteria.

[0808] "Means for setting a rank" refers to a system for determining the ranking or rating of a user based on the scoring results and assigning a rank.

[0809] The "means for generating benefit information" refers to a system that determines the content of the benefits and systems that will be applied depending on the user's rank and creates that information.

[0810] The "means for displaying special benefit information" refers to a system that transmits the generated special benefit information to a user terminal and displays it in a form that can be understood by the user.

[0811] This invention describes a system consisting of three main elements: a server, a terminal, and a user, which automatically generates ethical scenarios using a generative AI model and evaluates and ranks the user's answers.

[0812] Server Operation

[0813] The server first uses a generative AI model (e.g., OpenAI's GPT-4) to generate ethical scenarios that users may encounter. These scenarios are presented to users in the form of depictions of moral and ethical issues and situations. The generated scenarios are classified according to difficulty level (beginner, intermediate, advanced, etc.) and stored in a database on the server.

[0814] Specifically, it is generated using the following prompt:

[0815] “You find a wallet on the street and it has a lot of cash inside, what do you do?

[0816] A. Take it to the police station

[0817] B. Make it your own

[0818] C. Leave it there and walk away.”

[0819] Device behavior

[0820] The user's device (computer, smartphone, tablet, etc.) receives and displays the ethical scenario sent from the server. The user selects actions and opinions regarding this scenario through the device's interface. For example, the user can choose an action such as "deliver to the police station" from the options.

[0821] User Actions

[0822] The user makes their own ethical judgments about the scenarios displayed on the device and selects the most appropriate option from the options. The answer is then sent to the server via the device.

[0823] Server Ratings and Rankings

[0824] The server receives the user's answers sent from the device and evaluates them using a generative AI model. Based on the evaluation results, the user's ethical judgments and actions are scored, and for example, the option "deliver to the police station" is given a high score (e.g., 90 points). Based on this score, the server determines the user's rank. Specifically, users with a score above a certain level are assigned a "high rank." The server also generates reward information (e.g., tax reductions, priority support in the event of a disaster) according to the user's rank.

[0825] Terminal display

[0826] The generated reward information is sent back to the user's terminal and displayed to the user. For example, a message such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" is displayed to the user.

[0827] Specific examples

[0828] 1. Server-side scenario generation: The server uses a generative AI model to generate a scenario: "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away.

[0829] 2. Displaying scenarios on the terminal: The above scenarios and options are displayed to the user.

[0830] 3. User response: The user selects "Deliver to the police station."

[0831] 4. Server evaluation: The server analyzes the answer "deliver to the police station" and gives it a high evaluation score (for example, 90 points).

[0832] 5. Rank determination and reward generation: The server will set a "high rank" based on the score and generate rewards such as "tax reduction" or "priority support in the event of a disaster."

[0833] 6. Displaying rewards on the device: Display the generated reward information to the user.

[0834] This system makes it possible to fairly and efficiently evaluate users' ethical behavior and provide appropriate treatment and benefits based on that evaluation.

[0835] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0836] Step 1:

[0837] Server-based scenario generation

[0838] Input: Prompt sentence: "You find a wallet on the street. It has a large amount of cash inside. What do you do?"

[0839] The server uses a generative AI model to automatically generate ethical scenarios that users may encounter, which include multiple choices and are categorized according to difficulty level: beginner, intermediate, or advanced.

[0840] Output: Generated ethical scenario (e.g., "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away)

[0841] Specific operation: The server inputs the prompt sentence into the generative AI model and saves the generated scenario in the database.

[0842] Step 2:

[0843] Sending scenarios from the server to the device

[0844] Input: Ethical scenario stored on the server

[0845] The server sends the generated scenario to the user's terminal.

[0846] Output: Scenario data sent to the user's device

[0847] Specific operation: The server retrieves the scenario from the database and sends it to the user's terminal.

[0848] Step 3:

[0849] Displaying scenarios on a device

[0850] Input: Scenario data sent from the server

[0851] The user's terminal displays the ethical scenario received from the server.

[0852] Output: The scenario displayed on the terminal screen

[0853] Specific operation: The terminal analyzes the scenario data received from the server and displays it on the screen.

[0854] Step 4:

[0855] User selection and response submission

[0856] Input: Scenario and options displayed on the terminal

[0857] The user makes their own ethical judgment about the scenario displayed on the terminal, selects an option, and submits their answer.

[0858] Output: The answer selected by the user

[0859] Specific operation: The user selects an option such as "deliver to the police station" and sends the answer to the server via the terminal.

[0860] Step 5:

[0861] Server-based answer evaluation and scoring

[0862] Input: User-selected answer data

[0863] The server evaluates the user's answers received from the device and scores them using a generative AI model.

[0864] Output: User's rating score for ethical judgment

[0865] Specific operation: The server inputs the user's answer into the generative AI model, analyzes it, and stores the evaluation score (e.g., "Take it to the police station" gets 90 points) in the database.

[0866] Step 6:

[0867] Server determines rank and generates rewards

[0868] Input: Evaluation score data

[0869] The server determines the rank of the user based on the evaluation score and generates bonus information according to the rank.

[0870] Output: User rank and benefits information (e.g., high rank, tax reduction, priority support in case of disaster)

[0871] Specific operation: The server analyzes the score, sets the user as "high rank", and generates reward information.

[0872] Step 7:

[0873] Sending bonus information from the server to the device

[0874] Input: Bonus information generated on the server

[0875] The server transmits the generated bonus information to the user's terminal.

[0876] Output: Reward information data sent to the user device

[0877] Specific operation: The server sends the reward information to the user's terminal.

[0878] Step 8:

[0879] Displaying bonus information on your device

[0880] Input: Bonus information data sent from the server

[0881] The user's terminal displays the bonus information received from the server.

[0882] Output: Reward information displayed on the device screen

[0883] Specific operation: The terminal analyzes the bonus information data received from the server and displays it on the screen.

[0884] (Application example 1)

[0885] 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."

[0886] Conventional ethical education and assessment systems lack effective and enjoyable methods for helping users learn ethical judgment. Furthermore, conventional systems only present simple scenarios and collect answers, and do not sufficiently motivate users to take actual action. In other words, innovative methods are needed to encourage users to behave ethically and to learn in an enjoyable way.

[0887] 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.

[0888] In this invention, the server includes means for automatically generating an ethical scenario using a generative AI model, means for displaying the generated ethical scenario on a user device, means for collecting user responses to the ethical scenario, means for evaluating the responses and scoring the user's ethical judgment, means for setting the user's rank based on the score, means for generating reward information according to the user's rank, means for displaying the reward information on the user device, and means for providing an interface in a virtual store where users can respond based on the ethical scenario and automatically awarding rewards such as points or coupons according to the responses. This allows users to learn about ethics while having fun, and the rewards increase their motivation, which is expected to lead to a high level of learning effectiveness.

[0889] A "generative AI model" is an artificial intelligence technology that automatically generates ethical problems and situations that users may face.

[0890] An "ethical scenario" is a hypothetical scenario that depicts an ethical problem or situation that a user is asked to solve.

[0891] A "user device" is a terminal such as a computer, smartphone, or tablet that a user uses to access the system.

[0892] The "answer collection means" is a function that allows users to input and record answers to ethical scenarios on the device.

[0893] The "evaluation means" is a function that analyzes the user's responses and scores the accuracy and appropriateness of the ethical judgment based on the results.

[0894] The "scoring means" is a function that evaluates the user's answers analyzed by the evaluation means as numerical values ​​or grades.

[0895] The "rank setting means" is a function that determines the rank of a user based on the score calculated by the scoring means.

[0896] The "privilege information generating means" is a function that generates privilege information such as points and coupons according to the rank of the user.

[0897] A "virtual store" is an environment where users can virtually shop in a digital space.

[0898] The "interface providing means" is a function that provides a screen and an operation method for the user to input answers to the scenario.

[0899] System Overview

[0900] Server Operation

[0901] The server first uses a generative AI model to automatically generate ethical scenarios. The generated scenarios depict ethical issues and situations that users may encounter in their daily lives. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and prepared for transmission to the user's device.

[0902] Device behavior

[0903] The user's device (such as a smartphone or head-mounted display) displays an ethical scenario sent from the server. The user responds to the scenario through the device's interface, expressing their actions and opinions. For example, in response to the scenario, "You found a wallet at the cash register. What would you do?", the user would select an option such as "Take it to the clerk."

[0904] User Actions

[0905] The user selects their own ethical judgment and actions for the scenario displayed on the terminal and transmits their response via the terminal.

[0906] Server Ratings and Rankings

[0907] The server receives the user's answers sent from the device and evaluates them using a generative AI model. Based on the evaluation results, the user's ethical judgments and actions are scored. For example, if the choice of "deliver to the store clerk" is highly evaluated, the user is given a high score. Based on the score, the server determines the user's rank. For example, a user with a score of 90 or above is assigned a "high rank."

[0908] Generate and view rewards

[0909] The server generates reward information according to the rank and sends it to the user device. The reward information is displayed on the user device. For example, a notification such as "Your rank is high. You have been awarded a reward of 100 points" is displayed.

[0910] Specific examples

[0911] 1. Server-side scenario generation:

[0912] The generative AI model generates the following scenario: "You find a wallet on the street. It has a large amount of cash inside. What would you do?"

[0913] Option A: Give it to the store clerk

[0914] Option B: Make it your own

[0915] Option C: Leave it and walk away

[0916] 2. Display scenario on device:

[0917] The above scenario and options are displayed on the user's terminal.

[0918] 3. User Answer:

[0919] The user selects "deliver to store clerk."

[0920] 4. Server Rating:

[0921] The server analyzes the answer "deliver to the store clerk" and gives a high evaluation score (for example, 90 points).

[0922] 5. Rank determination and reward generation:

[0923] The server will set a "high rank" based on the score and generate "100 points" as a reward.

[0924] 6. Displaying the benefits on your device:

[0925] The generated benefit information is displayed to the user.

[0926] Prompt Sentence Examples

[0927] You found a wallet on the street. What would you do?

[0928] 1. Report it to the police station

[0929] 2. Make it your own

[0930] 3. Leave it there and walk away

[0931] This system allows users to learn ethically while having fun, and the rewards it provides increase motivation, leading to the expectation of high learning outcomes.

[0932] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0933] Step 1:

[0934] The server automatically generates ethical scenarios using a generative AI model. The server inputs the prompt "Generate ethical issues that users may face" into the generative AI model to obtain ethical scenarios. The generated scenarios are classified into beginner, intermediate, and advanced levels of difficulty.

[0935] Input: prompt statement

[0936] Output: Ethical scenarios (with difficulty classification)

[0937] Step 2:

[0938] The server sends the generated ethical scenario to the user's device, where it is displayed on the user's connected smartphone or head-mounted display.

[0939] Input: Ethical scenario

[0940] Output: Scenario display on user terminal

[0941] Step 3:

[0942] The user inputs their response to the scenario through the interface on the device. As a specific action, the user selects the appropriate answer from the presented options. For example, in response to the question, "You found a wallet on the street. What would you do?", the user selects "Take it to the police station."

[0943] Input: Scenario choices

[0944] Output: User's answer

[0945] Step 4:

[0946] The device sends the user's answer to the server, which analyzes the received answer and evaluates it using a generative AI model. The evaluation process includes checking against a surrounding knowledge base and comparing it with similar answers from the past.

[0947] Input: User's answer

[0948] Output: Evaluation result (score)

[0949] Step 5:

[0950] The server calculates the user's score based on the evaluation results and assigns a rank according to that score. For example, if the score is 90 or above, it will be assigned a "high rank."

[0951] Input: Evaluation result (score)

[0952] Output: User's rank

[0953] Step 6:

[0954] The server generates reward information (points, coupons, etc.) according to the user's rank. The type and amount of reward information varies depending on the rank.

[0955] Input: User rank

[0956] Output: Bonus information

[0957] Step 7:

[0958] The server transmits the generated reward information to the user's terminal, which then displays it to the user. The displayed reward is visualized in a concrete form such as points or coupons.

[0959] Input: Bonus information

[0960] Output: Display rewards on user device

[0961] 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.

[0962] This invention is a system that automatically generates ethical scenarios using a generative AI model, evaluates and ranks user responses to those scenarios, and recognizes the user's emotions and reflects them in the evaluation and ranking. This system consists of four main components: a server, a terminal (user device), a user, and an emotion engine.

[0963] System Overview

[0964] Server Operation

[0965] The server first uses a generative AI model to generate ethical scenarios, which depict ethical issues and situations that users may face. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and prepared for transmission to user devices.

[0966] Device behavior

[0967] The user's device (computer, smartphone, tablet, etc.) displays an ethical scenario sent from the server. The user responds through the device's interface regarding their actions and opinions regarding the scenario. For example, in response to the question, "What would you do if you found a wallet on the street?", the user could select an option such as "Take it to the police station."

[0968] User Actions

[0969] The user selects their own ethical judgment and behavior for the scenario on the device and inputs their response. The response is sent to the server via the device. When the user responds, the emotion engine recognizes the user's emotions from their facial expression, tone of voice, input speed, etc.

[0970] Emotion Engine Operation

[0971] The emotion engine analyzes the emotions of users as they answer ethical scenarios. This engine analyzes the user's facial expressions, tone of voice, typing speed, etc. in real time to identify the user's emotional state. For example, it can recognize whether the user is nervous or calm when answering. The emotion engine sends this information to the server.

[0972] Server Ratings and Rankings

[0973] The server analyzes the user's answers received from the device and the user's emotional data received from the emotion engine. Using a generative AI model, the server evaluates whether the answers and emotional data are ethical, moral, and fair. For example, the choice "Take it to the police station" is highly evaluated and assigned a high score. Furthermore, if the server recognizes that the user responded calmly, the score may be even higher.

[0974] Based on the score, the server determines the user's rank. For example, a user with a score of 90 or more is assigned a "high rank." After the rank is assigned, the server generates reward and program information appropriate for the user.

[0975] Terminal display

[0976] The generated benefit information is sent to the terminal again and displayed to the user. For example, information such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" is displayed.

[0977] Specific examples

[0978] 1. Server-side scenario generation: The generative AI model generates the scenario: "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away.

[0979] 2. Displaying scenarios on the terminal: The above scenarios and options are displayed to the user.

[0980] 3. User response and emotion recognition: The user selects "Deliver to the police station." At the same time, the emotion engine recognizes the user's calm emotion from their facial expression and tone of voice.

[0981] 4. Server evaluation: The server analyzes the answer "Take it to the police station" and the emotional data that the user answered calmly, and gives a high evaluation score (for example, 90 points).

[0982] 5. Rank determination and reward generation: The server will set a "high rank" based on the score and generate rewards such as "tax reduction" or "priority support in the event of a disaster."

[0983] 6. Displaying rewards on the device: Display the generated reward information to the user.

[0984] This system makes it possible to accurately assess users' ethical behavior and emotional state and provide fair treatment based on that assessment.

[0985] The processing flow will be explained below.

[0986] Step 1:

[0987] The server runs the generative AI model to generate new ethical scenarios. The generated scenarios are stored in an internal database. For example, a scenario might be generated: "You found a wallet on the street. What would you do?"

[0988] Step 2:

[0989] The server prepares to send the generated scenario to the terminal. This scenario contains multiple options.

[0990] Step 3:

[0991] The terminal displays the scenario received from the server to the user. For example, a scenario such as "You found a wallet on the street. It has a lot of cash inside. What would you do?" is displayed.

[0992] Step 4:

[0993] The user reads the displayed scenario and selects an action from the options presented. For example, they select "deliver to the police station." At this time, the emotion engine recognizes the user's emotions in real time from their facial expressions, tone of voice, typing speed, etc.

[0994] Step 5:

[0995] The terminal transmits the user's selection together with the emotion data recognized by the emotion engine to the server.

[0996] Step 6:

[0997] The server analyzes the response data and sentiment data received from users and uses a generative AI model to evaluate whether the content and sentiment of the responses are ethical, moral, and fair.

[0998] Step 7:

[0999] The server calculates the user's score based on the evaluation results. For example, if the choice "deliver to the police station" is highly rated, the user will be given a high score. If the user answers calmly, the score may be even higher.

[1000] Step 8:

[1001] The server assigns a rank to the user based on the calculated score. For example, if the user has a score of 90 or above, the server assigns a "high rank."

[1002] Step 9:

[1003] The server generates reward information according to the user's rank. High-ranking users are given "tax reductions" and "priority support in the event of a disaster."

[1004] Step 10:

[1005] The server transmits the generated bonus information to the terminal.

[1006] Step 11:

[1007] The terminal displays the benefit information received from the server to the user, for example, "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster."

[1008] This series of steps creates a system in which users' ethical behavior and judgments are evaluated and appropriate treatment is provided based on that evaluation.

[1009] Example 2

[1010] 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."

[1011] Conventional systems lack fairness and accuracy because they do not consider emotional data when evaluating users' ethical judgments. Another issue is the lack of systems that not only simply score ethical judgments but also provide appropriate feedback and rewards based on those scores. Furthermore, if the difficulty of the generated scenarios is not appropriate for the user's ability, this could affect the reliability of the evaluation.

[1012] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1013] In this invention, the server includes means for automatically generating ethical situations using a generative AI model, means for presenting the generated ethical situations on a user interface, means for collecting responses and emotional data to users' ethical judgments, means for analyzing the responses and emotional data and evaluating and scoring the users' ethical judgments, means for setting the users' ranks based on the scores, means for generating reward information according to the users' ranks, and means for displaying the reward information on the user interface. This improves the accuracy and fairness of evaluations based on the users' ethical judgments, and makes it possible to provide users with feedback and rewards appropriate to them.

[1014] A "generative AI model" is a machine learning algorithm that performs human-like natural language generation based on input prompts.

[1015] An "ethical situation" is a situation or occasion that a user may encounter that requires a moral or ethical decision.

[1016] A "user interface" refers to the interaction that includes a screen and input devices that allow a user to interact with a device or system.

[1017] An "answer" is an option or opinion that a user selects and enters in response to a presented ethical situation.

[1018] "Emotional data" is information about the emotional state of the user, such as facial expressions, tone of voice, and input speed, that is collected when the user answers.

[1019] "Analysis" is the process of statistically or computationally analyzing collected data to extract meaningful information.

[1020] "Scoring" is the process of assigning a score according to specific evaluation criteria based on the user's answers and emotional data.

[1021] "Rank" refers to the evaluation level of a user that is determined based on the score.

[1022] "Reward information" is information about benefits and profits provided according to the user's rank.

[1023] This invention is a system that automatically generates ethical situations using a generative AI model, evaluates and scores users' responses to those situations, and generates and presents reward information. The system consists of four main components: a server, a terminal (user device), a user, and an emotion engine.

[1024] The server first generates ethical situations using a generative AI model. Examples of such models include ChatGPT. Situations are generated by inputting prompts into the model. For example, a prompt such as "Imagine a situation where a user finds a wallet on the street" can be used. Scenarios generated by this model are classified according to difficulty level (beginner, intermediate, or advanced) and saved on the server.

[1025] The server then sends the generated situation to the device, which then displays it on its user interface. For example, the device displays a situation such as "You found a wallet on the street. What would you do?" along with options such as "Take it to the police station," "Keep it," and "Ignore it and walk away."

[1026] The user selects and inputs their own response to a given situation on the device. At this time, the emotion engine analyzes the user's facial expression, tone of voice, input speed, etc. in real time to collect emotional data. This emotion engine uses, for example, emotion recognition software and sensor devices.

[1027] The device sends the user's selected answer and emotional data to the server. The server then statistically or computationally analyzes this data and uses a generative AI model to evaluate and score the user's ethical judgment. For example, the option "Take it to the police station" will receive a high score, and a calm emotional state will receive additional points.

[1028] The server determines the user's rank based on this score and generates reward information accordingly. For example, a user with a score of 90 or more will be assigned a "high rank" and be given special benefits such as "tax reductions" and "priority support in the event of a disaster." This reward information is then sent back to the terminal.

[1029] Finally, the terminal displays the generated reward information to the user on a user interface, such as a message saying, "Your rank is high. You will receive reduced taxes and priority assistance in the event of a disaster."

[1030] In this way, the server, terminal, user, and emotion engine work together to realize a system in which evaluations are made based on the user's ethical judgment and fair rewards are provided.

[1031] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1032] Step 1:

[1033] The server uses a generative AI model to generate ethical situations. The server inputs the prompt "Imagine a situation where a user finds a wallet on the street" into the generative AI model. Based on this input, the generative AI model outputs the situation "You found a wallet on the street. What would you do?". Furthermore, the server adds options to this output situation, such as "Take it to the police station," "Keep it for yourself," and "Ignore it and walk away."

[1034] Step 2:

[1035] The server sends the generated situation and options to the terminal. The server then sends the generated situation and options in packet format to a specific user ID within the system. This operation completes data transmission from the server to the terminal.

[1036] Step 3:

[1037] The device displays the received situation and options to the user. The device displays the situation "You found a wallet on the street. What would you do?" on the user interface along with options such as "Take it to the police station," "Keep it," and "Ignore it and walk away." This display allows the user to visually see the options for the situation.

[1038] Step 4:

[1039] The user inputs an answer to the situation. The user selects an option on the terminal, such as "deliver to the police station," and inputs that selection on the interface. This input records the user's selected answer on the terminal.

[1040] Step 5:

[1041] The device sends the user's answer and emotional data to the server. The device then sends the answer selected by the user and emotional data collected by the emotion engine, such as facial expressions, tone of voice, and input speed, in packet format to the server. This transmitted data includes information such as "User ID 12345: Option A (report to the police station), Emotion: Calm."

[1042] Step 6:

[1043] The server analyzes the user's responses and emotional data and assigns a score. The server analyzes the received data and evaluates the user's ethical judgment using a generative AI model. For example, the option "Take it to the police station" is given 50 points, and a calm emotional state is given 40 points, for a total score of 90 points.

[1044] Step 7:

[1045] The server determines the user's rank based on the score and generates reward information. The server assigns a "high rank" to users with a score of 90 or more, and generates reward information such as "tax reduction" or "priority support in the event of a disaster" as appropriate benefits. This reward information is stored in the user ID.

[1046] Step 8:

[1047] The server sends the generated reward information to the terminal. The server sends the reward information corresponding to the specific user ID to the terminal in packet format. This operation completes the data transmission from the server to the terminal.

[1048] Step 9:

[1049] The terminal displays the reward information to the user. The terminal displays reward information such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" on the user interface. This allows the user to check their own evaluation results and reward information.

[1050] (Application example 2)

[1051] 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."

[1052] Conventional ethical judgment evaluation systems calculate scores based solely on the user's responses, ignoring the user's emotional state and the psychological background of the situation. This makes it difficult to achieve truly fair and balanced evaluations. Furthermore, because reward information is determined solely based on the score, it cannot be said to be appropriate and reflects the user's actual behavior and emotions. In such cases, particularly in the field of security services, where immediate response and calmness on the spot are required, conventional systems are unable to provide sufficient evaluations.

[1053] The identification process by the identification 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 automatically generating an ethical scenario using a generative AI model, means for displaying the generated ethical scenario on a user device, means for collecting user responses to the ethical scenario, means for evaluating the responses and scoring the user's ethical judgment, means for analyzing the user's emotional state using an emotion recognition engine, means for setting the user's rank based on the score and emotional data, means for generating reward information according to the user's rank, and means for displaying the reward information on the user device. This makes it possible to comprehensively evaluate the user's ethical behavior and the emotional state at the time and provide more fair and appropriate reward information.

[1054] A "generative AI model" is a type of algorithm that automatically generates specific scenarios or sentences based on artificial intelligence.

[1055] An "ethical scenario" is a scenario that depicts a moral or ethical issue or situation that a user may face.

[1056] A "user device" is a terminal device operated by a user, and includes a computer, a smartphone, a tablet, smart glasses, a head-mounted display, etc.

[1057] A "means for collecting responses" is a mechanism for receiving user input via a user device and storing it in a database.

[1058] "Scoring means" is a system that evaluates the user's answers and assigns them points or evaluation values.

[1059] An "emotion recognition engine" is an algorithm that analyzes data such as a user's facial expression, tone of voice, and input speed to identify the user's emotional state.

[1060] The "means for setting a rank" is a mechanism for determining and classifying a user's evaluation rank based on the user's score and emotional data.

[1061] "Benefit information" is information about rewards and systems provided according to the user's rank.

[1062] This invention is a system that automatically generates ethical scenarios using a generative AI model. The system consists of a server, a terminal (user device), and an emotion recognition engine that recognizes the user's emotions. This system evaluates the user's ethical judgment and emotional state, and generates and displays reward information based on the results.

[1063] System Program

[1064] The server uses a generative AI model to generate ethical scenarios that depict moral or ethical issues or situations that users may encounter. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and then sent to the user's device.

[1065] The device (smartphone, smart glasses, head-mounted display, etc.) displays an ethical scenario sent from the server. The user responds to the scenario through the device's interface, expressing their actions and opinions. For example, in response to the question, "What would you do if you found a wallet on the street?", the user can select an option such as "Take it to the police station."

[1066] When a user answers a scenario, the emotion recognition engine analyzes the user's facial expressions, tone of voice, typing speed, etc. in real time to identify the user's emotional state. This information is sent to the server and evaluated along with the answer data. For example, if a user answers "deliver it to the police station" in a calm state, a high score will be assigned.

[1067] The server analyzes the user's answers received from the device and the emotional data received from the emotion engine. Using a generative AI model, it evaluates whether the answers and emotional data are ethical, moral, and fair. Based on the evaluation score, the server determines the user's rank. Users with high scores are given a "high rank" and are given rewards such as "tax reductions" and "priority support in the event of a disaster."

[1068] The generated reward information is retransmitted to the terminal and displayed to the user, thereby enabling the user's ethical behavior and emotional state to be accurately evaluated and reward information to be provided to the user based on the evaluation.

[1069] Hardware and software used

[1070] Hardware: Smartphone, smart glasses, head-mounted display, camera (for emotion recognition)

[1071] Software: OpenAI API (generative AI model), Emotion Recognition Library (emotion recognition), OpenCV (image processing)

[1072] Specific examples

[1073] 1. Example of a prompt for scenario generation

[1074] "Generate an ethical scenario of a situation a security guard might face. The scenario should include a situation in which a suspicious individual attempts to enter a staff area while on patrol."

[1075] 2. Example Scenario

[1076] "While on patrol, you notice an unknown individual attempting to enter a staff area. How would you respond?"

[1077] 3. Example user responses

[1078] "Stop them immediately and ask them to show their identification."

[1079] This system will help security guards make more ethical and fair decisions, improving their ability to respond immediately when dealing with problems.

[1080] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1081] Step 1:

[1082] The server uses a generative AI model to generate an ethical scenario. The server is given the prompt, "Generate an ethical scenario for a situation a security guard might face. Include a situation in which a suspicious person is attempting to enter a staff area while on patrol." The generated scenario might be text like, "While on patrol, you notice an unknown person attempting to enter a staff area. How would you respond?" The generated scenario is stored on the server and prepared for transmission to the terminal.

[1083] Step 2:

[1084] The device receives the ethical scenario sent from the server and displays it to the user. The user responds to the scenario through the device's interface, expressing their actions and opinions. The input response data is obtained in a format such as "Stop the person immediately and ask for identification" and is sent from the device to the server.

[1085] Step 3:

[1086] As the user answers the scenario, the emotion recognition engine uses a camera connected to the device to analyze data such as the user's facial expressions, tone of voice, and typing speed in real time. This allows the user's emotional state (e.g., calm or nervous) to be identified. This emotional data is then sent from the device to the server.

[1087] Step 4:

[1088] The server analyzes the user's response data received from the device and the emotion data from the emotion recognition engine. Utilizing a generative AI model, it evaluates whether the response is ethical and fair and assigns a score (e.g., 85 points for the content of the response, 90 points for the emotional state). Based on this, it calculates an overall evaluation score. This score data is used in subsequent processing.

[1089] Step 5:

[1090] The server determines the user's rank based on the evaluation score. For example, if the score is 80 or more, it sets the user as "high rank." This rank data is used to generate rewards.

[1091] Step 6:

[1092] The server generates special benefit information according to the user's rank. For example, special benefit information such as "Your rank is high. You will receive reduced taxes and priority assistance in the event of a disaster" is generated. This special benefit information is stored in the server and transmitted to the terminal.

[1093] Step 7:

[1094] The terminal receives the reward information sent from the server and displays it to the user, allowing the user to receive evaluations and rewards based on their own behavior and emotional state.

[1095] 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.

[1096] 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.

[1097] 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.

[1098] [Fourth embodiment]

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

[1100] 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.

[1101] 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).

[1102] 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.

[1103] 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.

[1104] 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).

[1105] 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.

[1106] 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.

[1107] 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.

[1108] 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.

[1109] 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.

[1110] 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.

[1111] 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."

[1112] This system automatically generates ethical scenarios using a generative AI model and evaluates and ranks user responses. This system consists of three main components: a server, a terminal, and a user.

[1113] System Overview

[1114] Server Operation

[1115] The server first uses a generative AI model to generate ethical scenarios, which depict ethical issues and situations that users may face. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and prepared for transmission to user devices.

[1116] Device behavior

[1117] The user's device (computer, smartphone, tablet, etc.) displays an ethical scenario sent from the server. The user responds through the device's interface regarding their actions and opinions regarding the scenario. For example, in response to the question, "What would you do if you found a wallet on the street?", the user could select an option such as "Take it to the police station."

[1118] User Actions

[1119] The user selects their own ethical judgments and actions for the scenarios on the terminal and inputs their answers, which are then sent to the server via the terminal.

[1120] Server Ratings and Rankings

[1121] The server receives the user's answers sent from the device and evaluates them using a generative AI model. Based on the evaluation results, the user's ethical judgments and actions are scored. For example, if the choice of "deliver to the police station" is highly evaluated, the user will be given a high score.

[1122] Based on the score, the server determines the user's rank. For example, a user with a score of 90 or more is assigned a "high rank." After the rank is assigned, the server generates reward and program information appropriate for the user.

[1123] Terminal display

[1124] The generated benefit information is sent to the terminal again and displayed to the user. For example, information such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" is displayed.

[1125] Specific examples

[1126] 1. Server-side scenario generation: The generative AI model generates the scenario: "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away.

[1127] 2. Displaying scenarios on the terminal: The above scenarios and options are displayed to the user.

[1128] 3. User response: The user selects "Deliver to the police station."

[1129] 4. Server evaluation: The server analyzes the answer "deliver to the police station" and gives it a high evaluation score (for example, 90 points).

[1130] 5. Rank determination and reward generation: The server will set a "high rank" based on the score and generate rewards such as "tax reduction" or "priority support in the event of a disaster."

[1131] 6. Displaying rewards on the device: Display the generated reward information to the user.

[1132] This system makes it possible to accurately evaluate users' ethical behavior and provide fair treatment based on that evaluation.

[1133] The processing flow will be explained below.

[1134] Step 1:

[1135] The server runs the generative AI model to generate new ethical scenarios. The generated scenarios are stored in an internal database. For example, a scenario might be generated: "You found a wallet on the street. What would you do?"

[1136] Step 2:

[1137] The server prepares to send the generated scenario to the terminal. This scenario contains multiple options.

[1138] Step 3:

[1139] The terminal displays the scenario received from the server to the user. For example, a scenario such as "You found a wallet on the street. It has a lot of cash inside. What would you do?" is displayed.

[1140] Step 4:

[1141] The user reads the displayed scenario and selects an action from the options presented. For example, they can select "deliver to the police station."

[1142] Step 5:

[1143] The terminal records the user's selection and transmits the information to the server.

[1144] Step 6:

[1145] The server analyzes the responses received from users and uses a generative AI model to evaluate whether the responses are ethical, moral, and fair.

[1146] Step 7:

[1147] The server calculates the user's score based on the evaluation results. For example, the option "Deliver to the police station" is highly rated and is set to a score of 90.

[1148] Step 8:

[1149] The server assigns a rank to the user based on the calculated score. For example, if the user has a score of 90 or above, the server assigns a "high rank."

[1150] Step 9:

[1151] The server generates reward information according to the user's rank. High-ranking users are given "tax reductions" and "priority support in the event of a disaster."

[1152] Step 10:

[1153] The server transmits the generated bonus information to the terminal.

[1154] Step 11:

[1155] The terminal displays the benefit information received from the server to the user, for example, "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster."

[1156] This series of steps creates a system in which users' ethical behavior and judgments are evaluated and appropriate treatment is provided based on that evaluation.

[1157] Example 1

[1158] 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."

[1159] In modern society, the ethical judgment of individuals is highly valued, and there is a growing demand for systems that evaluate such judgments and provide appropriate social responses and rewards. However, there is a lack of means to objectively and efficiently evaluate users' ethical judgments. There is also a need for a system that can appropriately rank users based on the evaluation results and quickly provide reward information according to their rank.

[1160] 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.

[1161] In this invention, the server includes means for automatically generating an ethical scenario using a generative AI model, means for displaying the generated ethical scenario on a user terminal, means for collecting users' answers to the ethical scenario, means for evaluating the answers and scoring the users' ethical judgments, means for setting the users' ranks based on the scores, means for generating reward information according to the users' ranks, and means for displaying the reward information on the user terminal. This makes it possible to objectively evaluate the users' ethical judgments and provide appropriate responses and rewards based on the evaluation.

[1162] A "generative AI model" is an artificial intelligence model that uses advanced algorithms to generate text and automatically create ethical scenarios that users may encounter.

[1163] An "ethical scenario" is a scenario that specifically depicts moral and ethical issues or situations that users may face.

[1164] "User terminal" refers to an electronic device, such as a computer, smartphone, or tablet, that a user operates to input and display information.

[1165] "Means for collecting responses" refers to a system that allows users to select their opinions and actions regarding ethical scenarios, and then electronically captures and records them.

[1166] "Scoring means" refers to a system that evaluates answers provided by users and assigns them a score or rating based on pre-defined criteria.

[1167] "Means for setting a rank" refers to a system for determining the ranking or rating of a user based on the scoring results and assigning a rank.

[1168] The "means for generating benefit information" refers to a system that determines the content of the benefits and systems that will be applied depending on the user's rank and creates that information.

[1169] The "means for displaying special benefit information" refers to a system that transmits the generated special benefit information to a user terminal and displays it in a form that can be understood by the user.

[1170] This invention describes a system consisting of three main elements: a server, a terminal, and a user, which automatically generates ethical scenarios using a generative AI model and evaluates and ranks the user's answers.

[1171] Server Operation

[1172] The server first uses a generative AI model (e.g., OpenAI's GPT-4) to generate ethical scenarios that users may encounter. These scenarios are presented to users in the form of depictions of moral and ethical issues and situations. The generated scenarios are classified according to difficulty level (beginner, intermediate, advanced, etc.) and stored in a database on the server.

[1173] Specifically, it is generated using the following prompt:

[1174] “You find a wallet on the street and it has a lot of cash inside, what do you do?

[1175] A. Take it to the police station

[1176] B. Make it your own

[1177] C. Leave it there and walk away.”

[1178] Device behavior

[1179] The user's device (computer, smartphone, tablet, etc.) receives and displays the ethical scenario sent from the server. The user selects actions and opinions regarding this scenario through the device's interface. For example, the user can choose an action such as "deliver to the police station" from the options.

[1180] User Actions

[1181] The user makes their own ethical judgments about the scenarios displayed on the device and selects the most appropriate option from the options. The answer is then sent to the server via the device.

[1182] Server Ratings and Rankings

[1183] The server receives the user's answers sent from the device and evaluates them using a generative AI model. Based on the evaluation results, the user's ethical judgments and actions are scored, and for example, the option "deliver to the police station" is given a high score (e.g., 90 points). Based on this score, the server determines the user's rank. Specifically, users with a score above a certain level are assigned a "high rank." The server also generates reward information (e.g., tax reductions, priority support in the event of a disaster) according to the user's rank.

[1184] Terminal display

[1185] The generated reward information is sent back to the user's terminal and displayed to the user. For example, a message such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" is displayed to the user.

[1186] Specific examples

[1187] 1. Server-side scenario generation: The server uses a generative AI model to generate a scenario: "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away.

[1188] 2. Displaying scenarios on the terminal: The above scenarios and options are displayed to the user.

[1189] 3. User response: The user selects "Deliver to the police station."

[1190] 4. Server evaluation: The server analyzes the answer "deliver to the police station" and gives it a high evaluation score (for example, 90 points).

[1191] 5. Rank determination and reward generation: The server will set a "high rank" based on the score and generate rewards such as "tax reduction" or "priority support in the event of a disaster."

[1192] 6. Displaying rewards on the device: Display the generated reward information to the user.

[1193] This system makes it possible to fairly and efficiently evaluate users' ethical behavior and provide appropriate treatment and benefits based on that evaluation.

[1194] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1195] Step 1:

[1196] Server-based scenario generation

[1197] Input: Prompt sentence: "You find a wallet on the street. It has a large amount of cash inside. What do you do?"

[1198] The server uses a generative AI model to automatically generate ethical scenarios that users may encounter, which include multiple choices and are categorized according to difficulty level: beginner, intermediate, or advanced.

[1199] Output: Generated ethical scenario (e.g., "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away)

[1200] Specific operation: The server inputs the prompt sentence into the generative AI model and saves the generated scenario in the database.

[1201] Step 2:

[1202] Sending scenarios from the server to the device

[1203] Input: Ethical scenario stored on the server

[1204] The server sends the generated scenario to the user's terminal.

[1205] Output: Scenario data sent to the user's device

[1206] Specific operation: The server retrieves the scenario from the database and sends it to the user's terminal.

[1207] Step 3:

[1208] Displaying scenarios on a device

[1209] Input: Scenario data sent from the server

[1210] The user's terminal displays the ethical scenario received from the server.

[1211] Output: The scenario displayed on the terminal screen

[1212] Specific operation: The terminal analyzes the scenario data received from the server and displays it on the screen.

[1213] Step 4:

[1214] User selection and response submission

[1215] Input: Scenario and options displayed on the terminal

[1216] The user makes their own ethical judgment about the scenario displayed on the terminal, selects an option, and submits their answer.

[1217] Output: The answer selected by the user

[1218] Specific operation: The user selects an option such as "deliver to the police station" and sends the answer to the server via the terminal.

[1219] Step 5:

[1220] Server-based answer evaluation and scoring

[1221] Input: User-selected answer data

[1222] The server evaluates the user's answers received from the device and scores them using a generative AI model.

[1223] Output: User's rating score for ethical judgment

[1224] Specific operation: The server inputs the user's answer into the generative AI model, analyzes it, and stores the evaluation score (e.g., "Take it to the police station" gets 90 points) in the database.

[1225] Step 6:

[1226] Server determines rank and generates rewards

[1227] Input: Evaluation score data

[1228] The server determines the rank of the user based on the evaluation score and generates bonus information according to the rank.

[1229] Output: User rank and benefits information (e.g., high rank, tax reduction, priority support in case of disaster)

[1230] Specific operation: The server analyzes the score, sets the user as "high rank", and generates reward information.

[1231] Step 7:

[1232] Sending bonus information from the server to the device

[1233] Input: Bonus information generated on the server

[1234] The server transmits the generated bonus information to the user's terminal.

[1235] Output: Reward information data sent to the user device

[1236] Specific operation: The server sends the reward information to the user's terminal.

[1237] Step 8:

[1238] Displaying bonus information on your device

[1239] Input: Bonus information data sent from the server

[1240] The user's terminal displays the bonus information received from the server.

[1241] Output: Reward information displayed on the device screen

[1242] Specific operation: The terminal analyzes the bonus information data received from the server and displays it on the screen.

[1243] (Application example 1)

[1244] 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."

[1245] Conventional ethical education and assessment systems lack effective and enjoyable methods for helping users learn ethical judgment. Furthermore, conventional systems only present simple scenarios and collect answers, and do not sufficiently motivate users to take actual action. In other words, innovative methods are needed to encourage users to behave ethically and to learn in an enjoyable way.

[1246] 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.

[1247] In this invention, the server includes means for automatically generating an ethical scenario using a generative AI model, means for displaying the generated ethical scenario on a user device, means for collecting user responses to the ethical scenario, means for evaluating the responses and scoring the user's ethical judgment, means for setting the user's rank based on the score, means for generating reward information according to the user's rank, means for displaying the reward information on the user device, and means for providing an interface in a virtual store where users can respond based on the ethical scenario and automatically awarding rewards such as points or coupons according to the responses. This allows users to learn about ethics while having fun, and the rewards increase their motivation, which is expected to lead to a high level of learning effectiveness.

[1248] A "generative AI model" is an artificial intelligence technology that automatically generates ethical problems and situations that users may face.

[1249] An "ethical scenario" is a hypothetical scenario that depicts an ethical problem or situation that a user is asked to solve.

[1250] A "user device" is a terminal such as a computer, smartphone, or tablet that a user uses to access the system.

[1251] The "answer collection means" is a function that allows users to input and record answers to ethical scenarios on the device.

[1252] The "evaluation means" is a function that analyzes the user's responses and scores the accuracy and appropriateness of the ethical judgment based on the results.

[1253] The "scoring means" is a function that evaluates the user's answers analyzed by the evaluation means as numerical values ​​or grades.

[1254] The "rank setting means" is a function that determines the rank of a user based on the score calculated by the scoring means.

[1255] The "privilege information generating means" is a function that generates privilege information such as points and coupons according to the rank of the user.

[1256] A "virtual store" is an environment where users can virtually shop in a digital space.

[1257] The "interface providing means" is a function that provides a screen and an operation method for the user to input answers to the scenario.

[1258] System Overview

[1259] Server Operation

[1260] The server first uses a generative AI model to automatically generate ethical scenarios. The generated scenarios depict ethical issues and situations that users may encounter in their daily lives. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and prepared for transmission to the user's device.

[1261] Device behavior

[1262] The user's device (such as a smartphone or head-mounted display) displays an ethical scenario sent from the server. The user responds to the scenario through the device's interface, expressing their actions and opinions. For example, in response to the scenario, "You found a wallet at the cash register. What would you do?", the user would select an option such as "Take it to the clerk."

[1263] User Actions

[1264] The user selects their own ethical judgment and actions for the scenario displayed on the terminal and transmits their response via the terminal.

[1265] Server Ratings and Rankings

[1266] The server receives the user's answers sent from the device and evaluates them using a generative AI model. Based on the evaluation results, the user's ethical judgments and actions are scored. For example, if the choice of "deliver to the store clerk" is highly evaluated, the user is given a high score. Based on the score, the server determines the user's rank. For example, a user with a score of 90 or above is assigned a "high rank."

[1267] Generate and view rewards

[1268] The server generates reward information according to the rank and sends it to the user device. The reward information is displayed on the user device. For example, a notification such as "Your rank is high. You have been awarded a reward of 100 points" is displayed.

[1269] Specific examples

[1270] 1. Server-side scenario generation:

[1271] The generative AI model generates the following scenario: "You find a wallet on the street. It has a large amount of cash inside. What would you do?"

[1272] Option A: Give it to the store clerk

[1273] Option B: Make it your own

[1274] Option C: Leave it and walk away

[1275] 2. Display scenario on device:

[1276] The above scenario and options are displayed on the user's terminal.

[1277] 3. User Answer:

[1278] The user selects "deliver to store clerk."

[1279] 4. Server Rating:

[1280] The server analyzes the answer "deliver to the store clerk" and gives a high evaluation score (for example, 90 points).

[1281] 5. Rank determination and reward generation:

[1282] The server will set a "high rank" based on the score and generate "100 points" as a reward.

[1283] 6. Displaying the benefits on your device:

[1284] The generated benefit information is displayed to the user.

[1285] Prompt Sentence Examples

[1286] You found a wallet on the street. What would you do?

[1287] 1. Report it to the police station

[1288] 2. Make it your own

[1289] 3. Leave it there and walk away

[1290] This system allows users to learn ethically while having fun, and the rewards it provides increase motivation, leading to the expectation of high learning outcomes.

[1291] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1292] Step 1:

[1293] The server automatically generates ethical scenarios using a generative AI model. The server inputs the prompt "Generate ethical issues that users may face" into the generative AI model to obtain ethical scenarios. The generated scenarios are classified into beginner, intermediate, and advanced levels of difficulty.

[1294] Input: prompt statement

[1295] Output: Ethical scenarios (with difficulty classification)

[1296] Step 2:

[1297] The server sends the generated ethical scenario to the user's device, where it is displayed on the user's connected smartphone or head-mounted display.

[1298] Input: Ethical scenario

[1299] Output: Scenario display on user terminal

[1300] Step 3:

[1301] The user inputs their response to the scenario through the interface on the device. As a specific action, the user selects the appropriate answer from the presented options. For example, in response to the question, "You found a wallet on the street. What would you do?", the user selects "Take it to the police station."

[1302] Input: Scenario choices

[1303] Output: User's answer

[1304] Step 4:

[1305] The device sends the user's answer to the server, which analyzes the received answer and evaluates it using a generative AI model. The evaluation process includes checking against a surrounding knowledge base and comparing it with similar answers from the past.

[1306] Input: User's answer

[1307] Output: Evaluation result (score)

[1308] Step 5:

[1309] The server calculates the user's score based on the evaluation results and assigns a rank according to that score. For example, if the score is 90 or above, it will be assigned a "high rank."

[1310] Input: Evaluation result (score)

[1311] Output: User's rank

[1312] Step 6:

[1313] The server generates reward information (points, coupons, etc.) according to the user's rank. The type and amount of reward information varies depending on the rank.

[1314] Input: User rank

[1315] Output: Bonus information

[1316] Step 7:

[1317] The server transmits the generated reward information to the user's terminal, which then displays it to the user. The displayed reward is visualized in a concrete form such as points or coupons.

[1318] Input: Bonus information

[1319] Output: Display rewards on user device

[1320] 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.

[1321] This invention is a system that automatically generates ethical scenarios using a generative AI model, evaluates and ranks user responses to those scenarios, and recognizes the user's emotions and reflects them in the evaluation and ranking. This system consists of four main components: a server, a terminal (user device), a user, and an emotion engine.

[1322] System Overview

[1323] Server Operation

[1324] The server first uses a generative AI model to generate ethical scenarios, which depict ethical issues and situations that users may face. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and prepared for transmission to user devices.

[1325] Device behavior

[1326] The user's device (computer, smartphone, tablet, etc.) displays an ethical scenario sent from the server. The user responds through the device's interface regarding their actions and opinions regarding the scenario. For example, in response to the question, "What would you do if you found a wallet on the street?", the user could select an option such as "Take it to the police station."

[1327] User Actions

[1328] The user selects their own ethical judgment and behavior for the scenario on the device and inputs their response. The response is sent to the server via the device. When the user responds, the emotion engine recognizes the user's emotions from their facial expression, tone of voice, input speed, etc.

[1329] Emotion Engine Operation

[1330] The emotion engine analyzes the emotions of users as they answer ethical scenarios. This engine analyzes the user's facial expressions, tone of voice, typing speed, etc. in real time to identify the user's emotional state. For example, it can recognize whether the user is nervous or calm when answering. The emotion engine sends this information to the server.

[1331] Server Ratings and Rankings

[1332] The server analyzes the user's answers received from the device and the user's emotional data received from the emotion engine. Using a generative AI model, the server evaluates whether the answers and emotional data are ethical, moral, and fair. For example, the choice "Take it to the police station" is highly evaluated and assigned a high score. Furthermore, if the server recognizes that the user responded calmly, the score may be even higher.

[1333] Based on the score, the server determines the user's rank. For example, a user with a score of 90 or more is assigned a "high rank." After the rank is assigned, the server generates reward and program information appropriate for the user.

[1334] Terminal display

[1335] The generated benefit information is sent to the terminal again and displayed to the user. For example, information such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" is displayed.

[1336] Specific examples

[1337] 1. Server-side scenario generation: The generative AI model generates the scenario: "You find a wallet on the street. It contains a large amount of cash. What will you do?" Option A: Take it to the police station, Option B: Keep it, Option C: Leave it and walk away.

[1338] 2. Displaying scenarios on the terminal: The above scenarios and options are displayed to the user.

[1339] 3. User response and emotion recognition: The user selects "Deliver to the police station." At the same time, the emotion engine recognizes the user's calm emotion from their facial expression and tone of voice.

[1340] 4. Server evaluation: The server analyzes the answer "Take it to the police station" and the emotional data that the user answered calmly, and gives a high evaluation score (for example, 90 points).

[1341] 5. Rank determination and reward generation: The server will set a "high rank" based on the score and generate rewards such as "tax reduction" or "priority support in the event of a disaster."

[1342] 6. Displaying rewards on the device: Display the generated reward information to the user.

[1343] This system makes it possible to accurately assess users' ethical behavior and emotional state and provide fair treatment based on that assessment.

[1344] The processing flow will be explained below.

[1345] Step 1:

[1346] The server runs the generative AI model to generate new ethical scenarios. The generated scenarios are stored in an internal database. For example, a scenario might be generated: "You found a wallet on the street. What would you do?"

[1347] Step 2:

[1348] The server prepares to send the generated scenario to the terminal. This scenario contains multiple options.

[1349] Step 3:

[1350] The terminal displays the scenario received from the server to the user. For example, a scenario such as "You found a wallet on the street. It has a lot of cash inside. What would you do?" is displayed.

[1351] Step 4:

[1352] The user reads the displayed scenario and selects an action from the options presented. For example, they select "deliver to the police station." At this time, the emotion engine recognizes the user's emotions in real time from their facial expressions, tone of voice, typing speed, etc.

[1353] Step 5:

[1354] The terminal transmits the user's selection together with the emotion data recognized by the emotion engine to the server.

[1355] Step 6:

[1356] The server analyzes the response data and sentiment data received from users and uses a generative AI model to evaluate whether the content and sentiment of the responses are ethical, moral, and fair.

[1357] Step 7:

[1358] The server calculates the user's score based on the evaluation results. For example, if the choice "deliver to the police station" is highly rated, the user will be given a high score. If the user answers calmly, the score may be even higher.

[1359] Step 8:

[1360] The server assigns a rank to the user based on the calculated score. For example, if the user has a score of 90 or above, the server assigns a "high rank."

[1361] Step 9:

[1362] The server generates reward information according to the user's rank. High-ranking users are given "tax reductions" and "priority support in the event of a disaster."

[1363] Step 10:

[1364] The server transmits the generated bonus information to the terminal.

[1365] Step 11:

[1366] The terminal displays the benefit information received from the server to the user, for example, "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster."

[1367] This series of steps creates a system in which users' ethical behavior and judgments are evaluated and appropriate treatment is provided based on that evaluation.

[1368] Example 2

[1369] 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."

[1370] Conventional systems lack fairness and accuracy because they do not consider emotional data when evaluating users' ethical judgments. Another issue is the lack of systems that not only simply score ethical judgments but also provide appropriate feedback and rewards based on those scores. Furthermore, if the difficulty of the generated scenarios is not appropriate for the user's ability, this could affect the reliability of the evaluation.

[1371] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1372] In this invention, the server includes means for automatically generating ethical situations using a generative AI model, means for presenting the generated ethical situations on a user interface, means for collecting responses and emotional data to users' ethical judgments, means for analyzing the responses and emotional data and evaluating and scoring the users' ethical judgments, means for setting the users' ranks based on the scores, means for generating reward information according to the users' ranks, and means for displaying the reward information on the user interface. This improves the accuracy and fairness of evaluations based on the users' ethical judgments, and makes it possible to provide users with feedback and rewards appropriate to them.

[1373] A "generative AI model" is a machine learning algorithm that performs human-like natural language generation based on input prompts.

[1374] An "ethical situation" is a situation or occasion that a user may encounter that requires a moral or ethical decision.

[1375] A "user interface" refers to the interaction that includes a screen and input devices that allow a user to interact with a device or system.

[1376] An "answer" is an option or opinion that a user selects and enters in response to a presented ethical situation.

[1377] "Emotional data" is information about the emotional state of the user, such as facial expressions, tone of voice, and input speed, that is collected when the user answers.

[1378] "Analysis" is the process of statistically or computationally analyzing collected data to extract meaningful information.

[1379] "Scoring" is the process of assigning a score according to specific evaluation criteria based on the user's answers and emotional data.

[1380] "Rank" refers to the evaluation level of a user that is determined based on the score.

[1381] "Reward information" is information about benefits and profits provided according to the user's rank.

[1382] This invention is a system that automatically generates ethical situations using a generative AI model, evaluates and scores users' responses to those situations, and generates and presents reward information. The system consists of four main components: a server, a terminal (user device), a user, and an emotion engine.

[1383] The server first generates ethical situations using a generative AI model. Examples of such models include ChatGPT. Situations are generated by inputting prompts into the model. For example, a prompt such as "Imagine a situation where a user finds a wallet on the street" can be used. Scenarios generated by this model are classified according to difficulty level (beginner, intermediate, or advanced) and saved on the server.

[1384] The server then sends the generated situation to the device, which then displays it on its user interface. For example, the device displays a situation such as "You found a wallet on the street. What would you do?" along with options such as "Take it to the police station," "Keep it," and "Ignore it and walk away."

[1385] The user selects and inputs their own response to a given situation on the device. At this time, the emotion engine analyzes the user's facial expression, tone of voice, input speed, etc. in real time to collect emotional data. This emotion engine uses, for example, emotion recognition software and sensor devices.

[1386] The device sends the user's selected answer and emotional data to the server. The server then statistically or computationally analyzes this data and uses a generative AI model to evaluate and score the user's ethical judgment. For example, the option "Take it to the police station" will receive a high score, and a calm emotional state will receive additional points.

[1387] The server determines the user's rank based on this score and generates reward information accordingly. For example, a user with a score of 90 or more will be assigned a "high rank" and be given special benefits such as "tax reductions" and "priority support in the event of a disaster." This reward information is then sent back to the terminal.

[1388] Finally, the terminal displays the generated reward information to the user on a user interface, such as a message saying, "Your rank is high. You will receive reduced taxes and priority assistance in the event of a disaster."

[1389] In this way, the server, terminal, user, and emotion engine work together to realize a system in which evaluations are made based on the user's ethical judgment and fair rewards are provided.

[1390] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1391] Step 1:

[1392] The server uses a generative AI model to generate ethical situations. The server inputs the prompt "Imagine a situation where a user finds a wallet on the street" into the generative AI model. Based on this input, the generative AI model outputs the situation "You found a wallet on the street. What would you do?". Furthermore, the server adds options to this output situation, such as "Take it to the police station," "Keep it for yourself," and "Ignore it and walk away."

[1393] Step 2:

[1394] The server sends the generated situation and options to the terminal. The server then sends the generated situation and options in packet format to a specific user ID within the system. This operation completes data transmission from the server to the terminal.

[1395] Step 3:

[1396] The device displays the received situation and options to the user. The device displays the situation "You found a wallet on the street. What would you do?" on the user interface along with options such as "Take it to the police station," "Keep it," and "Ignore it and walk away." This display allows the user to visually see the options for the situation.

[1397] Step 4:

[1398] The user inputs an answer to the situation. The user selects an option on the terminal, such as "deliver to the police station," and inputs that selection on the interface. This input records the user's selected answer on the terminal.

[1399] Step 5:

[1400] The device sends the user's answer and emotional data to the server. The device then sends the answer selected by the user and emotional data collected by the emotion engine, such as facial expressions, tone of voice, and input speed, in packet format to the server. This transmitted data includes information such as "User ID 12345: Option A (report to the police station), Emotion: Calm."

[1401] Step 6:

[1402] The server analyzes the user's responses and emotional data and assigns a score. The server analyzes the received data and evaluates the user's ethical judgment using a generative AI model. For example, the option "Take it to the police station" is given 50 points, and a calm emotional state is given 40 points, for a total score of 90 points.

[1403] Step 7:

[1404] The server determines the user's rank based on the score and generates reward information. The server assigns a "high rank" to users with a score of 90 or more, and generates reward information such as "tax reduction" or "priority support in the event of a disaster" as appropriate benefits. This reward information is stored in the user ID.

[1405] Step 8:

[1406] The server sends the generated reward information to the terminal. The server sends the reward information corresponding to the specific user ID to the terminal in packet format. This operation completes the data transmission from the server to the terminal.

[1407] Step 9:

[1408] The terminal displays the reward information to the user. The terminal displays reward information such as "Your rank is high. You will receive reduced taxes and priority support in the event of a disaster" on the user interface. This allows the user to check their own evaluation results and reward information.

[1409] (Application example 2)

[1410] 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."

[1411] Conventional ethical judgment evaluation systems calculate scores based solely on the user's responses, ignoring the user's emotional state and the psychological background of the situation. This makes it difficult to achieve truly fair and balanced evaluations. Furthermore, because reward information is determined solely based on the score, it cannot be said to be appropriate and reflects the user's actual behavior and emotions. In such cases, particularly in the field of security services, where immediate response and calmness on the spot are required, conventional systems are unable to provide sufficient evaluations.

[1412] The identification process by the identification 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 automatically generating an ethical scenario using a generative AI model, means for displaying the generated ethical scenario on a user device, means for collecting user responses to the ethical scenario, means for evaluating the responses and scoring the user's ethical judgment, means for analyzing the user's emotional state using an emotion recognition engine, means for setting the user's rank based on the score and emotional data, means for generating reward information according to the user's rank, and means for displaying the reward information on the user device. This makes it possible to comprehensively evaluate the user's ethical behavior and the emotional state at the time and provide more fair and appropriate reward information.

[1413] A "generative AI model" is a type of algorithm that automatically generates specific scenarios or sentences based on artificial intelligence.

[1414] An "ethical scenario" is a scenario that depicts a moral or ethical issue or situation that a user may face.

[1415] A "user device" is a terminal device operated by a user, and includes a computer, a smartphone, a tablet, smart glasses, a head-mounted display, etc.

[1416] A "means for collecting responses" is a mechanism for receiving user input via a user device and storing it in a database.

[1417] "Scoring means" is a system that evaluates the user's answers and assigns them points or evaluation values.

[1418] An "emotion recognition engine" is an algorithm that analyzes data such as a user's facial expression, tone of voice, and input speed to identify the user's emotional state.

[1419] The "means for setting a rank" is a mechanism for determining and classifying a user's evaluation rank based on the user's score and emotional data.

[1420] "Benefit information" is information about rewards and systems provided according to the user's rank.

[1421] This invention is a system that automatically generates ethical scenarios using a generative AI model. The system consists of a server, a terminal (user device), and an emotion recognition engine that recognizes the user's emotions. This system evaluates the user's ethical judgment and emotional state, and generates and displays reward information based on the results.

[1422] System Program

[1423] The server uses a generative AI model to generate ethical scenarios that depict moral or ethical issues or situations that users may encounter. The scenarios are categorized according to difficulty level: beginner, intermediate, or advanced. The generated scenarios are stored on the server and then sent to the user's device.

[1424] The device (smartphone, smart glasses, head-mounted display, etc.) displays an ethical scenario sent from the server. The user responds to the scenario through the device's interface, expressing their actions and opinions. For example, in response to the question, "What would you do if you found a wallet on the street?", the user can select an option such as "Take it to the police station."

[1425] When a user answers a scenario, the emotion recognition engine analyzes the user's facial expressions, tone of voice, typing speed, etc. in real time to identify the user's emotional state. This information is sent to the server and evaluated along with the answer data. For example, if a user answers "deliver it to the police station" in a calm state, a high score will be assigned.

[1426] The server analyzes the user's answers received from the device and the emotional data received from the emotion engine. Using a generative AI model, it evaluates whether the answers and emotional data are ethical, moral, and fair. Based on the evaluation score, the server determines the user's rank. Users with high scores are given a "high rank" and are given rewards such as "tax reductions" and "priority support in the event of a disaster."

[1427] The generated reward information is retransmitted to the terminal and displayed to the user, thereby enabling the user's ethical behavior and emotional state to be accurately evaluated and reward information to be provided to the user based on the evaluation.

[1428] Hardware and software used

[1429] Hardware: Smartphone, smart glasses, head-mounted display, camera (for emotion recognition)

[1430] Software: OpenAI API (generative AI model), Emotion Recognition Library (emotion recognition), OpenCV (image processing)

[1431] Specific examples

[1432] 1. Example of a prompt for scenario generation

[1433] "Generate an ethical scenario of a situation a security guard might face. The scenario should include a situation in which a suspicious individual attempts to enter a staff area while on patrol."

[1434] 2. Example Scenario

[1435] "While on patrol, you notice an unknown individual attempting to enter a staff area. How would you respond?"

[1436] 3. Example user responses

[1437] "Stop them immediately and ask them to show their identification."

[1438] This system will help security guards make more ethical and fair decisions, improving their ability to respond immediately when dealing with problems.

[1439] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1440] Step 1:

[1441] The server uses a generative AI model to generate an ethical scenario. The server is given the prompt, "Generate an ethical scenario for a situation a security guard might face. Include a situation in which a suspicious person is attempting to enter a staff area while on patrol." The generated scenario might be text like, "While on patrol, you notice an unknown person attempting to enter a staff area. How would you respond?" The generated scenario is stored on the server and prepared for transmission to the terminal.

[1442] Step 2:

[1443] The device receives the ethical scenario sent from the server and displays it to the user. The user responds to the scenario through the device's interface, expressing their actions and opinions. The input response data is obtained in a format such as "Stop the person immediately and ask for identification" and is sent from the device to the server.

[1444] Step 3:

[1445] As the user answers the scenario, the emotion recognition engine uses a camera connected to the device to analyze data such as the user's facial expressions, tone of voice, and typing speed in real time. This allows the user's emotional state (e.g., calm or nervous) to be identified. This emotional data is then sent from the device to the server.

[1446] Step 4:

[1447] The server analyzes the user's response data received from the device and the emotion data from the emotion recognition engine. Utilizing a generative AI model, it evaluates whether the response is ethical and fair and assigns a score (e.g., 85 points for the content of the response, 90 points for the emotional state). Based on this, it calculates an overall evaluation score. This score data is used in subsequent processing.

[1448] Step 5:

[1449] The server determines the user's rank based on the evaluation score. For example, if the score is 80 or more, it sets the user as "high rank." This rank data is used to generate rewards.

[1450] Step 6:

[1451] The server generates special benefit information according to the user's rank. For example, special benefit information such as "Your rank is high. You will receive reduced taxes and priority assistance in the event of a disaster" is generated. This special benefit information is stored in the server and transmitted to the terminal.

[1452] Step 7:

[1453] The terminal receives the reward information sent from the server and displays it to the user, allowing the user to receive evaluations and rewards based on their own behavior and emotional state.

[1454] 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.

[1455] 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.

[1456] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1457] 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.

[1458] 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.

[1459] 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.

[1460] 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).

[1461] 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.

[1462] 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."

[1463] 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.

[1464] 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).

[1465] 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.

[1466] 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.

[1467] 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.

[1468] 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.

[1469] 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.

[1470] 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.

[1471] 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.

[1472] 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.

[1473] 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.

[1474] 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.

[1475] The following is further disclosed regarding the above embodiment.

[1476] (Claim 1)

[1477] A means for automatically generating ethical scenarios using generative AI models; and

[1478] means for displaying the generated ethical scenario on a user device;

[1479] a means of collecting user responses to ethical scenarios;

[1480] a means for evaluating the responses and scoring the user's ethical judgment;

[1481] means for ranking users based on the scores;

[1482] means for generating benefit information according to the rank of the user;

[1483] and means for displaying the bonus information on a user device.

[1484] (Claim 2)

[1485] The system of claim 1, wherein the generated ethical scenarios are classified according to difficulty level.

[1486] (Claim 3)

[1487] 2. The system according to claim 1, wherein the system selects programs and benefits to be provided to the user based on the evaluation score.

[1488] "Example 1"

[1489] (Claim 1)

[1490] A means for automatically generating ethical scenarios using generative AI models; and

[1491] a means for displaying the generated ethical scenario on a user terminal;

[1492] a means of collecting user responses to ethical scenarios;

[1493] a means for evaluating the responses and scoring the user's ethical judgment;

[1494] means for ranking users based on the scores;

[1495] means for generating benefit information according to the rank of the user;

[1496] and means for displaying the benefit information on a user terminal.

[1497] (Claim 2)

[1498] The system of claim 1, wherein the generated ethical scenarios are classified according to difficulty level.

[1499] (Claim 3)

[1500] 10. The system of claim 1, wherein a reward to be provided to the user is selected based on the evaluation score.

[1501] "Application Example 1"

[1502] (Claim 1)

[1503] A means for automatically generating ethical scenarios using generative AI models; and

[1504] means for displaying the generated ethical scenario on a user device;

[1505] a means of collecting user responses to ethical scenarios;

[1506] a means for evaluating the responses and scoring the user's ethical judgment;

[1507] means for ranking users based on the scores;

[1508] means for generating benefit information according to the rank of the user;

[1509] means for displaying the bonus information on a user device;

[1510] The system includes a means for providing an interface in which a user can answer questions based on ethical scenarios within a virtual store, and automatically awarding rewards such as points and coupons depending on the answers.

[1511] (Claim 2)

[1512] The system of claim 1, wherein the generated ethical scenarios are classified according to difficulty level.

[1513] (Claim 3)

[1514] 2. The system according to claim 1, wherein rewards such as points or coupons to be provided to the user are selected based on the evaluation score.

[1515] "Example 2: Combining Emotion Engines"

[1516] (Claim 1)

[1517] A means to automatically generate ethical situations using generative AI models; and

[1518] means for presenting the generated ethical situation on a user interface;

[1519] A means for collecting responses and emotional data to the user's ethical judgments;

[1520] means for analyzing the responses and emotion data to evaluate and score the user's ethical judgment;

[1521] means for ranking users based on the scores;

[1522] means for generating reward information according to the rank of the user;

[1523] and means for displaying the reward information on a user interface.

[1524] (Claim 2)

[1525] 10. The system of claim 1, wherein the generated ethical situations are classified according to a plurality of levels of difficulty.

[1526] (Claim 3)

[1527] 10. The system of claim 1, wherein the reward information provided to the user is selected based on the evaluation score.

[1528] "Application example 2 when combining emotion engines"

[1529] (Claim 1)

[1530] A means for automatically generating ethical scenarios using generative AI models; and

[1531] means for displaying the generated ethical scenario on a user device;

[1532] a means of collecting user responses to ethical scenarios;

[1533] a means for evaluating the responses and scoring the user's ethical judgment;

[1534] means for analyzing a user's emotional state using an emotion recognition engine;

[1535] means for setting a rank for the user based on the score and emotion data;

[1536] means for generating benefit information according to the rank of the user;

[1537] and means for displaying the bonus information on a user device.

[1538] (Claim 2)

[1539] The system of claim 1, wherein the generated ethical scenarios are classified according to difficulty level.

[1540] (Claim 3)

[1541] 2. The system according to claim 1, wherein the system selects systems and benefits to be provided to the user based on the evaluation score and the emotion data. [Explanation of symbols]

[1542] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for automatically generating ethical scenarios using generative AI models; and means for displaying the generated ethical scenario on a user device; a means of collecting user responses to ethical scenarios; a means for evaluating the responses and scoring the user's ethical judgment; means for ranking users based on the scores; means for generating benefit information according to the rank of the user; and means for displaying the bonus information on a user device.

2. The system according to claim 1 , wherein the generated ethical scenarios are classified according to difficulty level.

3. The system according to claim 1, wherein the system selects systems and benefits to be provided to the user based on the evaluation score.

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